Antisense inhibition of human miRNAs and indications for an involvement of miRNA in cell growth and apoptosis

Efficient and accurate detection of GC-associated miR-96-5p using a competitive lateral flow method based on SERS

To facilitate rapid, efficient, and accurate detection of miR-96-5p associated with gastric cancer (GC), we developed a bioanalytical platform by integrating surface-enhanced Raman spectroscopy with lateral flow assay (SERS-LFA). With these SERS-LFA strips, miR-96-5p within the specimen competed with Au rhombic dodecahedron (AuRD) conjugated single-stranded DNA (ssDNA) to bond to the immobilized hairpin DNA (hpDNA) probe on the T line. Consequently, higher abundance of miR-96-5p led to reduced conjugation of AuRD on the T line, thereby resulting in diminished SERS intensity. The biosensor exhibited a detection time of approximately 30 min and demonstrated a low limit of detection (LOD) for miR-96-5p in PBS buffer solution, down to 3.7 fM. To validate its clinical utility for the early diagnosis of patients with different degrees of gastric lesions, we performed quantitative evaluations in cohorts that included healthy individuals, patients with mild intraepithelial neoplasia, patients with severe intraepithelial neoplasia, as well as patients diagnosed with GC. The results obtained from the SERS-LFA strips were in agreement with those obtained from the quantitative real-time polymerase chain reaction (qRT-PCR). Given the accomplishments, this biosensor has significant potential for the clinical diagnosis of GC, offering a promising avenue for timely detection and improved patient prognoses.

SPLHRNMTF: robust orthogonal non-negative matrix tri-factorization with self-paced learning and dual hypergraph regularization for predicting miRNA-disease associations

MicroRNAs (miRNAs) have been demonstrated to be closely related to human diseases. Studying the potential associations between miRNAs and diseases contributes to our understanding of disease pathogenic mechanisms. As traditional biological experiments are costly and time-consuming, computational models can be considered as effective complementary tools. In this study, we propose a novel model of robust orthogonal non-negative matrix tri-factorization (NMTF) with self-paced learning and dual hypergraph regularization, named SPLHRNMTF, to predict miRNA-disease associations. More specifically, SPLHRNMTF first uses a non-linear fusion method to obtain miRNA and disease comprehensive similarity. Subsequently, the improved miRNA-disease association matrix is reformulated based on weighted k-nearest neighbor profiles to correct false-negative associations. In addition, we utilize L 2 , 1 norm to replace Frobenius norm to calculate residual error, alleviating the impact of noise and outliers on prediction performance. Then, we integrate self-paced learning into NMTF to alleviate the model from falling into bad local optimal solutions by gradually including samples from easy to complex. Finally, hypergraph regularization is introduced to capture high-order complex relations from hypergraphs related to miRNAs and diseases. In 5-fold cross-validation five times experiments, SPLHRNMTF obtains higher average AUC values than other baseline models. Moreover, the case studies on breast neoplasms and lung neoplasms further demonstrate the accuracy of SPLHRNMTF. Meanwhile, the potential associations discovered are of biological significance.

Non-coding transcriptome profiles in clear-cell renal cell carcinoma

Clear-cell renal cell carcinoma (ccRCC) is a common urological malignancy with an increasing incidence. The development of molecular biomarkers that can predict the response to treatment and guide personalized therapy selection would substantially improve patient outcomes. Dysregulation of non-coding RNA (ncRNA) has been shown to have a role in the pathogenesis of ccRCC. Thus, an increasing number of studies are being carried out with a focus on the identification of ncRNA biomarkers in ccRCC tissue samples and the connection of these markers with patients' prognosis, pathological stage and grade (including metastatic potential), and therapy outcome. RNA sequencing analysis led to the identification of several ncRNA biomarkers that are dysregulated in ccRCC and might have a role in ccRCC development. These ncRNAs have the potential to be prognostic and predictive biomarkers for ccRCC, with prospective applications in personalized treatment selection. Research on ncRNA biomarkers in ccRCC is advancing, but clinical implementation remains preliminary owing to challenges in validation, standardization and reproducibility. Comprehensive studies and integration of ncRNAs into clinical trials are essential to accelerate the clinical use of these biomarkers.

DCSGMDA: A dual-channel convolutional model based on stacked deep learning collaborative gradient decomposition for predicting miRNA-disease associations

Numerous studies have shown that microRNAs (miRNAs) play a key role in human diseases as critical biomarkers. Its abnormal expression is often accompanied by the emergence of specific diseases. Therefore, studying the relationship between miRNAs and diseases can deepen the insights of their pathogenesis, grasp the process of disease onset and development, and promote drug research of specific diseases. However, many undiscovered relationships between miRNAs and diseases remain, significantly limiting research on miRNA-disease correlations. To explore more potential correlations, we propose a dual-channel convolutional model based on stacked deep learning collaborative gradient decomposition for predicting miRNA-disease associations (DCSGMDA). Firstly, we constructed similarity networks for miRNAs and diseases, as well as an association relationship network. Secondly, potential features were fully mined using stacked deep learning and gradient decomposition networks, along with dual-channel convolutional neural networks. Finally, correlations were scored by a multilayer perceptron. We performed 5-fold and 10-fold cross-validation experiments on DCSGMDA using two datasets based on the Human MicroRNA Disease Database (HMDD). Additionally, parametric, ablation, and comparative experiments, along with case studies, were conducted. The experimental results demonstrate that DCSGMDA performs well in predicting miRNA-disease associations.

PGCNMDA: Learning node representations along paths with graph convolutional network for predicting miRNA-disease associations

Identifying miRNA-disease associations (MDAs) is crucial for improving the diagnosis and treatment of various diseases. However, biological experiments can be time-consuming and expensive. To overcome these challenges, computational approaches have been developed, with Graph Convolutional Network (GCN) showing promising results in MDA prediction. The success of GCN-based methods relies on learning a meaningful spatial operator to extract effective node feature representations. To enhance the inference of MDAs, we propose a novel method called PGCNMDA, which employs graph convolutional networks with a learning graph spatial operator from paths. This approach enables the generation of meaningful spatial convolutions from paths in GCN, leading to improved prediction performance. On HMDD v2.0, PGCNMDA obtains a mean AUC of 0.9229 and an AUPRC of 0.9206 under 5-fold cross-validation (5-CV), and a mean AUC of 0.9235 and an AUPRC of 0.9212 under 10-fold cross-validation (10-CV), respectively. Additionally, the AUC of PGCNMDA also reaches 0.9238 under global leave-one-out cross-validation (GLOOCV). On HMDD v3.2, PGCNMDA obtains a mean AUC of 0.9413 and an AUPRC of 0.9417 under 5-CV, and a mean AUC of 0.9419 and an AUPRC of 0.9425 under 10-CV, respectively. Furthermore, the AUC of PGCNMDA also reaches 0.9415 under GLOOCV. The results show that PGCNMDA is superior to other compared methods. In addition, the case studies on pancreatic neoplasms, thyroid neoplasms and leukemia show that 50, 50 and 48 of the top 50 predicted miRNAs linked to these diseases are confirmed, respectively. It further validates the effectiveness and feasibility of PGCNMDA in practical applications.

Mono-(2-ethylhexyl) phthalate induces trophoblast hypoxia and mitochondrial dysfunction through HIF-1α-miR-210-3p axis in HTR-8/SVneo cell line

The exposure to the ubiquitous phthalate metabolite mono-(2-ethylhexyl) phthalate (MEHP) is connected to dysregulated trophoblast function and placenta health; however, the underlying mechanisms preluding this scenario remain to be elucidated. In this study, we explored the hypoxemic effects of MEHP on a human placental first-trimester trophoblast cell line (HTR-8/Svneo). MEHP-treated trophoblast cells displayed significantly increased levels of oxidative stress and hypoxia-inducible factor-1 alpha (HIF-1α) attributed by the induction of hypoxia. Further, HIF-1α exhibited higher DNA binding activity and upregulated gene expression of its downstream target vascular endothelial growth factor A (VEGFA). The hypoxia-induced microRNA miR-210-3p was also significantly increased upon MEHP treatment followed by disrupted mitochondrial ATP generation and membrane potential. This was identified to possibly be facilitated by lowered mitochondrial DNA copy number and inhibited expression of electron transport chain subunits, such as mitochondrial complex-IV. These results suggest potential adverse effects of MEHP exposure in a trophoblast cell line mediated by HIF-1α and the epigenetic modulator miR-210-3p. Chronic placental hypoxia and oxidative stress have long been implicated in the pathogenesis of pregnancy complications such as preeclampsia. As we've revealed genetic and epigenetic factors underscoring a potential mechanism induced by MEHP, this brings to light another significant implication of phthalate exposure on maternal and fetal health.

The Predictive Value of Blood-Derived Exosomal miRNAs as Biomarkers in Breast Cancer: A Systematic Review

Breast cancer (BC) remains a widespread disease worldwide, despite advances in its detection and treatment. microRNAs (miRNAs) play a significant role in cancer, and their presence within exosomes may confer several advantages in terms of tumor initiation, propagation, immune evasion, and drug resistance compared to freely circulating miRNAs in the blood. The objective of this study was to conduct a systematic review to analyze the role of exosomal miRNAs present in serum or plasma as biomarkers in BC. Bibliographic sources were collected from various databases with no starting date limit until March 2023. The search terms used were related to "breast cancer," "microRNAs," and "exosomes." Following the search, inclusion and exclusion criteria were applied, resulting in a total of 46 articles. Data were extracted from the selected studies and summarized to indicate the miRNAs, type of dysregulation, sample source, number of patients and controls, and clinical relevance of the miRNAs. We carried out an enrichment study of the microRNAs that appeared in at least 3 studies, those that were suitable for selection were miR-16, miR-21 and miR-155. Exosomal miRNAs isolated from blood samples of patients diagnosed with BC could be valuable in the clinical setting. They could provide information about early diagnosis, disease progression, recurrence, treatment response, and metastases. It is crucial to reach a consensus on the specific exosomal miRNAs to detect and the most appropriate type of sample for comprehensive utilization of miRNAs as biomarkers for BC.

Uracil-Selective Cross-Linking in RNA and Inhibition of miRNA Function by 2-Amino-6-vinyl-7-deazapurine Deoxynucleosides

MicroRNAs (miRNAs) regulate gene expression through RNA interference. Consequently, miRNA inhibitors, such as anti-miRNA oligonucleotides (AMOs), have attracted attention for treating miRNA overexpression. To achieve efficient inhibition, we developed 2-amino-6-vinylpurine (AVP) nucleosides that form covalent bonds with uridine counterparts in RNA. We demonstrated that mRNA cross-linked with AVP-conjugated antisense oligonucleotides with AVP were protected from gene silencing by exogenous miRNA. However, endogenous miRNA function could not be inhibited in cells, probably because of slow cross-linking kinetics. We recently developed ADpVP, an AVP derivative bearing a 7-propynyl group - which boasts faster reaction rate than the original AVP. Here, we synthesized dADpVP - a deoxy analog of ADpVP - through a simplified synthesis protocol. Evaluation of the cross-linking reaction revealed that the reaction kinetics of dADpVP were comparable to those of ADpVP. In addition, structural analysis of the cross-linked adduct discovered N3 linkage against uridine. Incorporating dADpVP into two types of miRNA inhibitors revealed a marginal impact on AMO efficacy yet improved the performance of target site blockers. These results indicate the potential of cross-linking nucleosides for indirect miRNA function inhibition.

CRISPR-Cas9 screens reveal common essential miRNAs in human cancer cell lines

BACKGROUND

Genome-wide functional screening using the CRISPR-Cas9 system is a powerful tool to uncover tumor-specific and common genetic dependencies across cancer cell lines. Current CRISPR-Cas9 knockout libraries, however, primarily target protein-coding genes. This limits functional genomics-based investigations of miRNA function.

METHODS

We designed a novel CRISPR-Cas9 knockout library (lentiG-miR) of 8107 distinct sgRNAs targeting a total of 1769 human miRNAs and benchmarked its single guide RNA (sgRNA) composition, predicted on- and off-target activity, and screening performance against previous libraries. Using a total of 45 human cancer cell lines, representing 16 different tumor entities, we performed negative selection screens to identify miRNA fitness genes. Fitness miRNAs in each cell line were scored using a combination of supervised and unsupervised essentiality classifiers. Common essential miRNAs across distinct cancer cell lines were determined using the 90th percentile method. For subsequent validation, we performed knockout experiments for selected common essential miRNAs in distinct cancer cell lines and gene expression profiling.

RESULTS

We found significantly lower off-target activity for protein-coding genes and a higher miRNA gene coverage for lentiG-miR as compared to previously described miRNA-targeting libraries, while preserving high on-target activity. A minor fraction of miRNAs displayed robust depletion of targeting sgRNAs, and we observed a high level of consistency between redundant sgRNAs targeting the same miRNA gene. Across 45 human cancer cell lines, only 217 (12%) of all targeted human miRNAs scored as a fitness gene in at least one model, and fitness effects for most miRNAs were confined to small subsets of cell lines. In contrast, we identified 49 common essential miRNAs with a homogenous fitness profile across the vast majority of all cell lines. Transcriptional profiling verified highly consistent gene expression changes in response to knockout of individual common essential miRNAs across a diverse set of cancer cell lines.

CONCLUSIONS

Our study presents a miRNA-targeting CRISPR-Cas9 knockout library with high gene coverage and optimized on- and off-target activities. Taking advantage of the lentiG-miR library, we define a catalogue of miRNA fitness genes in human cancer cell lines, providing the foundation for further investigation of miRNAs in human cancer.

AE-RW: Predicting miRNA-disease associations by using autoencoder and random walk on miRNA-gene-disease heterogeneous network

Since scientific investigations have demonstrated that aberrant expression of miRNAs brings about the incidence of numerous intricate diseases, precise determination of miRNA-disease relationships greatly contributes to the advancement of human medical progress. To tackle the issue of inefficient conventional experimental approaches, numerous computational methods have been proposed to predict miRNA-disease association with enhanced accuracy. However, constructing miRNA-gene-disease heterogeneous network by incorporating gene information has been relatively under-explored in existing computational techniques. Accordingly, this paper puts forward a technique to predict miRNA-disease association by applying autoencoder and implementing random walk on miRNA-gene-disease heterogeneous network(AE-RW). Firstly, we integrate association information and similarities between miRNAs, genes, and diseases to construct a miRNA-gene-disease heterogeneous network. Subsequently, we consolidate two network feature representations extracted independently via an autoencoder and a random walk procedure. Finally, deep neural network(DNN) are utilized to conduct association prediction. The experimental results demonstrate that the AE-RW model achieved an AUC of 0.9478 through 5-fold CV on the HMDD v3.2 dataset, outperforming the five most advanced existing models. Additionally, case studies were implemented for breast and lung cancer, further validated the superior predictive capabilities of our model.

How MicroRNAs Command the Battle against Cancer

MicroRNAs (miRNAs) are small RNA molecules that regulate more than 30% of genes in humans. Recent studies have revealed that miRNAs play a crucial role in tumorigenesis. Large sets of miRNAs in human tumors are under-expressed compared to normal tissues. Furthermore, experiments have shown that interference with miRNA processing enhances tumorigenesis. Multiple studies have documented the causal role of miRNAs in cancer, and miRNA-based anticancer therapies are currently being developed. This review primarily focuses on two key points: (1) miRNAs and their role in human cancer and (2) the regulation of tumor suppressors by miRNAs. The review discusses (a) the regulation of the tumor suppressor p53 by miRNA, (b) the critical role of the miR-144/451 cluster in regulating the Itch-p63-Ago2 pathway, and (c) the regulation of PTEN by miRNAs. Future research and the perspectives of miRNA in cancer are also discussed. Understanding these pathways will open avenues for therapeutic interventions targeting miRNA regulation.

SCPLPA: An miRNA-disease association prediction model based on spatial consistency projection and label propagation algorithm

Identifying the association between miRNA and diseases is helpful for disease prevention, diagnosis and treatment. It is of great significance to use computational methods to predict potential human miRNA disease associations. Considering the shortcomings of existing computational methods, such as low prediction accuracy and weak generalization, we propose a new method called SCPLPA to predict miRNA-disease associations. First, a heterogeneous disease similarity network was constructed using the disease semantic similarity network and the disease Gaussian interaction spectrum kernel similarity network, while a heterogeneous miRNA similarity network was constructed using the miRNA functional similarity network and the miRNA Gaussian interaction spectrum kernel similarity network. Then, the estimated miRNA-disease association scores were evaluated by integrating the outcomes obtained by implementing label propagation algorithms in the heterogeneous disease similarity network and the heterogeneous miRNA similarity network. Finally, the spatial consistency projection algorithm of the network was used to extract miRNA disease association features to predict unverified associations between miRNA and diseases. SCPLPA was compared with four classical methods (MDHGI, NSEMDA, RFMDA and SNMFMDA), and the results of multiple evaluation metrics showed that SCPLPA exhibited the most outstanding predictive performance. Case studies have shown that SCPLPA can effectively identify miRNAs associated with colon neoplasms and kidney neoplasms. In summary, our proposed SCPLPA algorithm is easy to implement and can effectively predict miRNA disease associations, making it a reliable auxiliary tool for biomedical research.

Recent Advances in Electrochemiluminescence Biosensors for MicroRNA Detection

Electrochemiluminescence (ECL) as an analytical technology with a perfect combination of electrochemistry and spectroscopy has received considerable attention in bioanalysis due to its high sensitivity and broad dynamic range. Given the selectivity of bio-recognition elements and the high sensitivity of the ECL analysis technique, ECL biosensors are powerful platforms for the sensitive detection of biomarkers, achieving the accurate prognosis and diagnosis of diseases. MicroRNAs (miRNAs) are crucial biomarkers involved in a variety of physiological and pathological processes, whose aberrant expression is often related to serious diseases, especially cancers. ECL biosensors can fulfill the highly sensitive and selective requirements for accurate miRNA detection, prompting this review. The ECL mechanisms are initially introduced and subsequently categorize the ECL biosensors for miRNA detection in terms of the quenching agents. Furthermore, the work highlights the signal amplification strategies for enhancing ECL signal to improve the sensitivity of miRNA detection and finally concludes by looking at the challenges and opportunities in ECL biosensors for miRNA detection.

miR-200b-3p accelerates progression of pituitary adenomas by negatively regulating expression of RECK

MicroRNA (miR)-200b-3p has been associated with many tumors, but its involvement in pituitary adenoma is unclear. This study investigated the molecular mechanism underlying miR-200b-3p regulation in pituitary adenomas to provide a theoretical basis for treatment. Bioinformatics was used to analyze pituitary adenoma-related genes and screen new targets related to RECK and miRNA. As well, the relationship between miR-200b-3p and RECK protein was verified using a double-luciferase reporter gene assay. The expression of miR-200b-3p in clinical samples was analyzed by in situ hybridization. Transfection of the miR-200b-3p inhibitor and small interfering-RECK (si-RECK) was verified by qPCR. GH3 cell viability and proliferation were detected using CCK8 and EdU assays. Apoptosis was detected by flow cytometry and western blotting. Wound healing and Transwell assays were used to detect cell migration and invasion. The effects of miR-200b-3p and RECK on GH3 cells were verified using salvage experiments. miR-200b-3p was highly expressed in pituitary tumor tissue. Inhibitors of miR-200b-3p inhibited cell proliferation promoted cell apoptosis, inhibited invasion and migration, and inhibited the expression of matrix metalloproteinases. Interestingly, miR-200b-3p negatively regulated RECK. The expression of RECK in pituitary adenoma tissues was lower than that in neighboring tissues. Si-RECK rescued the function of miR-200b-3p inhibitors in the above cellular behaviors, and miR-200b-3p accelerated the development of pituitary adenoma by negatively regulating RECK expression. In summary, this study investigated the molecular mechanism by which miR-200b-3p regulates the progression of pituitary adenoma through the negative regulation of RECK. The findings provide a new target for the treatment of pituitary adenoma.

Analysis of microRNA Expression Profiles in Broiler Muscle Tissues by Feeding Different Levels of Guanidinoacetic Acid

The aim of this study was to explore the molecular mechanisms through which different levels of GAA affect chicken muscle development by influencing miRNA expression, to lay a theoretical foundation for the identification of key functional small RNAs related to poultry muscle development, and to provide new insights into the regulatory mechanisms of GAA on muscle development and meat quality in broilers. It provides a new theoretical basis for using GAA as a feed additive to improve feed performance. Small RNA sequencing technology was utilized to obtain the expression profiles of miRNA in the broiler pectoral muscle fed with different levels of GAA (0 g/kg, 1.2 g/kg and 3.6 g/kg). An analysis of differentially expressed miRNAs revealed 90 such miRNAs in the three combination comparisons, with gga-miR-130b-5p exhibiting significant differences across all three combinations. Furthermore, three of the differentially expressed miRNAs were performed by RT-qPCR verification, yielding results consistent with those obtained from small RNA sequencing. Target gene prediction, as well as the GO and KEGG enrichment analysis of differentially expressed miRNAs, indicated their involvement in muscle cell differentiation and other processes, particularly those associated with the MAPK signaling pathway. This study has, thus, provided valuable insights and resources for the further exploration of the miRNA molecular mechanism underlying the influence of guanidine acetic acid on broiler muscle development. Combined with previous studies and small RNA sequencing, adding 1.2 g/kg GAA to the diet can better promote the muscle development of broilers.

Ocu-miR-10a-5p promotes the chondrogenic differentiation of rabbit BMSCs by targeting BTRC-mediated Wnt/β-catenin signaling pathway

MicroRNAs (miRNAs) play an important role in articular cartilage damage in osteoarthritis (OA). However, the biological role of miRNAs in the chondrogenic differentiation of bone marrow mesenchymal stem cell (BMSC) remains largely unclear. Rabbit bone marrow mesenchymal stem cells (rBMSCs) were isolated, cultured, and identified. Afterwards, rBMSCs were induced to chondrogenic differentiation, examined by Alcian Blue staining. Differentially expressed miRNAs were identified in rBMSCs between induced and non-induced groups by miRNA sequencing analysis, part of which was validated via PCR assay. Cell viability and apoptosis were assessed by CCK-8 assay and Hoechst staining. Saffron O staining was utilized to assess chondrocyte hyperplasia. The expression of specific chondrogenic markers, including COL2A1, SOX9, Runx2, MMP-13, Aggrecan, and BMP-2, were measured at mRNA and protein levels. The association between beta-transducin repeat containing E3 ubiquitin protein ligase (BTRC) and miR-10a-5p in the miRNA family from rabbit (ocu-miR-10a-5p) was determined by luciferase reporter assay. A total of 76 differentially expressed miRNAs, including 52 downregulated and 24 upregulated miRNAs, were identified in rBMSCs from the induced group. Inhibition of ocu-miR-10a-5p suppressed rBMSC viability and chondrogenic differentiation, as well as downregulated the expression of β-catenin, SOX9, COL2A1, MMP-13, and Runx2. BTRC was predicted and confirmed as a target of ocu-miR-10a-5p. Overexpression of BTRC rescued the promoting impacts of overexpressed ocu-miR-10a-5p on chondrogenic differentiation of rBMSCs and β-catenin expression. Taken together, our data suggested that ocu-miR-10a-5p facilitated rabbit BMSC survival and chondrogenic differentiation by activating Wnt/β-catenin signaling through BTRC.

GeneAI 3.0: powerful, novel, generalized hybrid and ensemble deep learning frameworks for miRNA species classification of stationary patterns from nucleotides

Due to the intricate relationship between the small non-coding ribonucleic acid (miRNA) sequences, the classification of miRNA species, namely Human, Gorilla, Rat, and Mouse is challenging. Previous methods are not robust and accurate. In this study, we present AtheroPoint's GeneAI 3.0, a powerful, novel, and generalized method for extracting features from the fixed patterns of purines and pyrimidines in each miRNA sequence in ensemble paradigms in machine learning (EML) and convolutional neural network (CNN)-based deep learning (EDL) frameworks. GeneAI 3.0 utilized five conventional (Entropy, Dissimilarity, Energy, Homogeneity, and Contrast), and three contemporary (Shannon entropy, Hurst exponent, Fractal dimension) features, to generate a composite feature set from given miRNA sequences which were then passed into our ML and DL classification framework. A set of 11 new classifiers was designed consisting of 5 EML and 6 EDL for binary/multiclass classification. It was benchmarked against 9 solo ML (SML), 6 solo DL (SDL), 12 hybrid DL (HDL) models, resulting in a total of 11 + 27 = 38 models were designed. Four hypotheses were formulated and validated using explainable AI (XAI) as well as reliability/statistical tests. The order of the mean performance using accuracy (ACC)/area-under-the-curve (AUC) of the 24 DL classifiers was: EDL > HDL > SDL. The mean performance of EDL models with CNN layers was superior to that without CNN layers by 0.73%/0.92%. Mean performance of EML models was superior to SML models with improvements of ACC/AUC by 6.24%/6.46%. EDL models performed significantly better than EML models, with a mean increase in ACC/AUC of 7.09%/6.96%. The GeneAI 3.0 tool produced expected XAI feature plots, and the statistical tests showed significant p-values. Ensemble models with composite features are highly effective and generalized models for effectively classifying miRNA sequences.

MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion

Introduction: MicroRNAs (miRNAs) are a class of non-coding RNA molecules that play a crucial role in the regulation of diverse biological processes across various organisms. Despite not encoding proteins, miRNAs have been found to have significant implications in the onset and progression of complex human diseases. Methods: Conventional methods for miRNA functional enrichment analysis have certain limitations, and we proposed a novel method called MiRNA Set Enrichment Analysis based on Multi-source Heterogeneous Information Fusion (MHIF-MSEA). Three miRNA similarity networks (miRSN-DA, miRSN-GOA, and miRSN-PPI) were constructed in MHIF-MSEA. These networks were built based on miRNA-disease association, gene ontology (GO) annotation of target genes, and protein-protein interaction of target genes, respectively. These miRNA similarity networks were fused into a single similarity network with the averaging method. This fused network served as the input for the random walk with restart algorithm, which expanded the original miRNA list. Finally, MHIF-MSEA performed enrichment analysis on the expanded list. Results and Discussion: To determine the optimal network fusion approach, three case studies were introduced: colon cancer, breast cancer, and hepatocellular carcinoma. The experimental results revealed that the miRNA-miRNA association network constructed using miRSN-DA and miRSN-GOA exhibited superior performance as the input network. Furthermore, the MHIF-MSEA model performed enrichment analysis on differentially expressed miRNAs in breast cancer and hepatocellular carcinoma. The achieved p-values were 2.17e(-75) and 1.50e(-77), and the hit rates improved by 39.01% and 44.68% compared to traditional enrichment analysis methods, respectively. These results confirm that the MHIF-MSEA method enhances the identification of enriched miRNA sets by leveraging multiple sources of heterogeneous information, leading to improved insights into the functional implications of miRNAs in complex diseases.

Prediction of MicroRNA-Disease Potential Association Based on Sparse Learning and Multilayer Random Walks

More and more studies have shown that microRNAs (miRNAs) play an indispensable role in the study of complex diseases in humans. Traditional biological experiments to detect miRNA-disease associations are expensive and time-consuming. Therefore, it is necessary to propose efficient and meaningful computational models to predict miRNA-disease associations. In this study, we aim to propose a miRNA-disease association prediction model based on sparse learning and multilayer random walks (SLMRWMDA). The miRNA-disease association matrix is decomposed and reconstructed by the sparse learning method to obtain richer association information, and at the same time, the initial probability matrix for the random walk with restart algorithm is obtained. The disease similarity network, miRNA similarity network, and miRNA-disease association network are used to construct heterogeneous networks, and the stable probability is obtained based on the topological structure features of diseases and miRNAs through a multilayer random walk algorithm to predict miRNA-disease potential association. The experimental results show that the prediction accuracy of this model is significantly improved compared with the previous related models. We evaluated the model using global leave-one-out cross-validation (global LOOCV) and fivefold cross-validation (5-fold CV). The area under the curve (AUC) value for the LOOCV is 0.9368. The mean AUC value for 5-fold CV is 0.9335 and the variance is 0.0004. In the case study, the results show that SLMRWMDA is effective in inferring the potential association of miRNA-disease.

The cross-talk between miRNAs and JAK/STAT pathway in cutaneous T cell lymphoma: Emphasis on therapeutic opportunities

Mycosis Fungoides (MF) and Sézary Syndrome (SS) belong to a wide spectrum of T cell lymphoproliferative disorders collectively termed cutaneous T cell lymphomas (CTCL). CTCLs represent an archetype of heterogeneous and dynamically variable lymphoproliferative neoplasms typified by distinct clinical, histological, immunophenotypic, and genetic features. Owing to its complex dynamics, the pathogenesis of CTCL remains elusive. However, in recent years, progress in CTCL classification combined with next-generation sequencing analyses has broadened the genetic and epigenetic spectrum of clearly defined CTCL entities such as MF and SS. Several large-scale genome studies have identified the polygenic nature of CTCL and unveiled an idiosyncratic mutational landscape involving genetic aberrations, epigenetic alterations, cell cycle dysregulation, apoptosis, and the constitutive activation of T cell/NF-κB/JAK-STAT signaling pathways. In this review, we summarize the evolving insights on how the intrinsic epigenetic events driven by dysregulated miRNAs, including the oncogenic and tumor-suppressive miRNAs, influence the pathogenesis of MF and SS. We also focus on the interplay between the JAK/STAT pathway and miRNAs in CTCL as well as the significance of the miRNA/STAT axis as a relevant pathogenetic mechanism underlying CTCL initiation and progression. Based on these biologic insights, the current status and recent progress on novel therapies with a strong biological rationale, including miRNA-targeted molecules and JAK/STAT-targeted therapy for CTCL management, are discussed.

Multiple Sclerosis: Roles of miRNA, lcnRNA, and circRNA and Their Implications in Cellular Pathways

Multiple sclerosis (MS) is a degenerative condition characterized by axonal damage and demyelination induced by autoreactive immune cells that occur in the Central Nervous System (CNS). The interaction between epigenetic changes and genetic factors can be widely involved in the onset, development, and progression of the disease. Although numerous efforts were made to discover new therapies able to prevent and improve the course of MS, definitive curative treatments have not been found yet. However, in recent years, it has been reported that non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long ncRNAs (lncRNAs), and circular RNAs (circRNAs), acting as gene expression regulators, could be used as potential therapeutic targets or biomarkers to diagnose and fight MS. In this review, we discussed the role of miRNAs, lncRNAs, and circRNAs, as well as their expression level changes and signaling pathways that are related to preclinical and human MS studies. Hence, the investigation of ncRNAs could be important to provide additional information regarding MS pathogenesis as well as promote the discovery of new therapeutic strategies or biomarkers.

Action mechanisms and characteristics of miRNAs to regulate virus replication

MicroRNAs (miRNAs) have the potential to be explored as antiviral products. It is known that miRNAs have different kinds of target mRNAs and different target sites in mRNAs, and that the action-modes of miRNAs at different target sites may be different. But there is no evidence demonstrating the significance of the differences for the regulation of viruses by miRNAs, which might be crucial for the exploration of miRNA-based antiviral products. Here the experimental studies about the antiviral effects of miRNAs, with validated target mRNAs and target sites in the mRNAs, were systematically collected, based on which the mechanisms whereby miRNAs regulated virus replication were systematically reviewed. And miRNAs' down-regulation rates on target mRNAs and antiviral rates were compared among the miRNAs with different target sites, to analyze the characteristics of action-modes of miRNAs at different target sites during virus replication.

LncRNA pol-lnc78 as a ceRNA regulates antibacterial responses via suppression of pol-miR-n199-3p-mediated SARM down-regulation in Paralichthys olivaceus

Long non-coding RNAs (lncRNAs) function as key modulators in mammalian immunity, particularly due to their involvement in lncRNA-mediated competitive endogenous RNA (ceRNA) crosstalk. Despite their recognized significance in mammals, research on lncRNAs in lower vertebrates remains limited. In the present study, we characterized the first immune-related lncRNA (pol-lnc78) in the teleost Japanese flounder ( Paralichthys olivaceus). Results indicated that pol-lnc78 acted as a ceRNA for pol-miR-n199-3p to target the sterile alpha and armadillo motif-containing protein (SARM), the fifth discovered member of the Toll/interleukin 1 (IL-1) receptor (TIR) adaptor family. This ceRNA network regulated the antibacterial responses of flounder via the Toll-like receptor (TLR) signaling pathway. Specifically, SARM acted as a negative regulator and exacerbated bacterial infection by inhibiting the expression of inflammatory cytokines IL-1β and tumor necrosis factor-α (TNF-α). Pol-miR-n199-3p reduced SARM expression by specifically interacting with the 3' untranslated region (UTR), thereby promoting SARM-dependent inflammatory cytokine expression and protecting the host against bacterial dissemination. Furthermore, pol-lnc78 sponged pol-miR-n199-3p to ameliorate the inhibition of SARM expression. During infection, the negative regulators pol-lnc78 and SARM were significantly down-regulated, while pol-miR-n199-3p was significantly up-regulated, thus favoring host antibacterial defense. These findings provide novel insights into the mechanisms underlying fish immunity and open new horizons to better understand ceRNA crosstalk in lower vertebrates.

MicroRNAs in infectious diseases: potential diagnostic biomarkers and therapeutic targets

MicroRNAs (miRNAs) are conserved, short, non-coding RNAs that play a crucial role in the post-transcriptional regulation of gene expression. They have been implicated in the pathogenesis of cancer and neurological, cardiovascular, and autoimmune diseases. Several recent studies have suggested that miRNAs are key players in regulating the differentiation, maturation, and activation of immune cells, thereby influencing the host immune response to infection. The resultant upregulation or downregulation of miRNAs from infection influences the protein expression of genes responsible for the immune response and can determine the risk of disease progression. Recently, miRNAs have been explored as diagnostic biomarkers and therapeutic targets in various infectious diseases. This review summarizes our current understanding of the role of miRNAs during viral, fungal, bacterial, and parasitic infections from a clinical perspective, including critical functional mechanisms and implications for their potential use as biomarkers and therapeutic targets.

The relationship between microRNAs and bladder cancer: are microRNAs useful to predict bladder cancer in suspicious patients?

PURPOSE

Recent studies indicate that circulating micro RNAs (miRNAs) are novel class of non-invasive biomarkers with diagnostic and prognostic information. We evaluated the miRNA expressions in bladder cancer (BC) and their associations with disease diagnosis.

METHODS

We profiled the expressions of 379 miRNAs in the plasma samples from patients with non-muscle invasive bladder cancer (NMIBC) (n = 34) and non-malignant urological diseases as a control group (n = 32). Patients were evaluated regarding with age, miRNA expressions, by using descriptive statistics. miRNA expression in extracted RNA was quantified using the NanoString nCounter Digital Analyzer.

RESULTS

The analysis of plasma miRNA levels in the marker identification cohort indicated that plasma (miR-1260a, let-7a-3p miR-196b-5p, miR-196a-5p, miR-99a-5p, miR-615-5p, miR-4301, miR-28-3p, miR-4538, miR-1233-3p, miR-4732-5p, miR-1913, miR-1280) levels were increased in NMIBC patients compared to control subjects. There were no significant differences other parameters studied between groups.

CONCLUSIONS

The analysis of serum plasma miRNA (miR-1260a, let-7a-3p miR-196b-5p, miR-196a-5p, miR-99a-5p, miR-615-5p, miR-4301, miR-28-3p, miR-4538, miR-1233-3p, miR-4732-5p, miR-1913, miR-1280) levels could be useful plasma biomarkers for BC.

A thorough and current study of miR-214-related targets in cancer

Cancer is a complex genetic anomaly involving coding and non-coding transcript structural and expressive irregularities. A class of tiny non-coding RNAs known as microRNAs (miRNAs) regulates gene expression at the post-transcriptional level by binding only to messenger RNAs (mRNAs). Due to their capacity to target numerous genes, miRNAs have the potential to play a significant role in the development of tumors by controlling several biological processes, including angiogenesis, drug resistance, metastasis, apoptosis, proliferation, and drug resistance. According to several recent studies, miRNA-214 has been linked to the emergence and spread of tumors. The human genome's q24.3 arm contains the DNM3 gene, which is about 6 kb away and includes the microRNA-214. Its primary purpose was the induction of apoptosis in cancerous cells. The multifaceted and complex functions of miR-214 as a modulator in neoplastic conditions have been outlined in the current review.

Exploring the Feasibility of Circulating miRNAs as Diagnostic and Prognostic Biomarkers in Osteoarthritis: Challenges and Opportunities

Osteoarthritis (OA) is a prevalent degenerative joint disease characterized by progressive cartilage degradation and joint inflammation. As the most common aging-related joint disease, OA is marked by inadequate extracellular matrix synthesis and the breakdown of articular cartilage. However, traditional diagnostic methods for OA, relying on clinical assessments and radiographic imaging, often need to catch up in detecting early-stage disease or i accurately predicting its progression. Consequently, there is a growing interest in identifying reliable biomarkers that can facilitate early diagnosis and prognosis of OA. MicroRNAs (miRNAs) have emerged as potential candidates due to their involvement in various cellular processes, including cartilage homeostasis and inflammation. This review explores the feasibility of circulating miRNAs as diagnostic and prognostic biomarkers in OA, focusing on knee OA while shedding light on the challenges and opportunities associated with their implementation in clinical practice.

Identification of Disease-Associated MicroRNAs Via Locality-Constrained Linear Coding-Based Ensemble Learning

Clinical trials indicate that the dysregulation of microRNAs (miRNAs) is closely associated with the development of diseases. Therefore, predicting miRNA-disease associations is significant for studying the pathogenesis of diseases. Since traditional wet-lab methods are resource-intensive, cost-saving computational models can be an effective complementary tool in biological experiments. In this work, a locality-constrained linear coding is proposed to predict associations (ILLCEL). Among them, ILLCEL adopts miRNA sequence similarity, miRNA functional similarity, disease semantic similarity, and interaction profile similarity obtained by locality-constrained linear coding (LLC) as the priori information. Next, features and similarities extracted from multiperspectives are input to the ensemble learning framework to improve the comprehensiveness of the prediction. Significantly, the introduction of hypergraph-regular terms improves the accuracy of prediction by describing complex associations between samples. The results under fivefold cross validation indicate that ILLCEL achieves superior prediction performance. In case studies, known associations are accurately predicted and novel associations are verified in HMDD v3.2, miRCancer, and existing literature. It is concluded that ILLCEL can be served as a powerful tool for inferring potential associations.

Discovery of novel microRNAs and their pathogenic responsive target genes in mild traumatic brain injury

MicroRNAs (miRNAs) are non-coding RNA molecules that function in RNA silencing and post-transcriptional regulation of gene expression. They are profound mediators of molecular and cellular changes in several pathophysiological conditions. Since miRNAs play major roles in regulating gene expression after traumatic brain injury (TBI), their possible role in diagnosis, prognosis, and therapy is not much explored. In this study, we aimed to identify specific miRNAs that are involved in the pathophysiological conditions in the first 24 h after mild TBI (mTBI). The genome-wide expression of miRNAs was evaluated by applying RNA sequence in the injury area of the cerebral cortex 24 after inflicting the injury using a mouse model of mild fluid percussion injury (FPI; 10 psi). Here, we identified different annotated, conserved, and novel miRNAs. A total of 978 miRNAs after 24 h of TBI were identified, and among these, 906 miRNAs were differentially expressed between control and mTBI groups. In this study, 146 miRNAs were identified as novel to mTBI and among them, 21 miRNAs were significant (p < 0.05). Using q-RT-PCR, we validated 10 differentially and significantly expressed novel miRNAs. Further, we filtered the differentially expressed miRNAs that were linked with proinflammatory cytokines, apoptosis, matrix metalloproteinases (MMPs), and tight junction and junctional adhesion molecule genes. Overall, this work shows that mTBI induces widespread changes in the expression of miRNAs that may underlie the progression of the TBI pathophysiology. The detection of several novel TBI-responsive miRNAs and their solid link with pathophysiological genes may help in identifying novel therapeutic targets.

Bifunctional Y-shaped probe combined with dual amplification for colorimetric sensing and molecular logic operation of two miRNAs

The abnormal expression of miRNA is closely related to various human diseases. In particular, the sensitivity detection of miRNA expression level is of great significance for the early diagnosis and prognosis of cancer. In this paper, we designed a Y-shaped DNA probe, using miRNA-21 and miRNA-141 as the dual input signals of AND logic gate. By combining with EXO III assisted target recycle and DNA hybridization chain reaction (HCR), we have realized dual signal amplification for detection of two miRNAs. In short, the Y-shaped DNA probe consists of two parts: the miRNA target binding region and the HCR initiator. When the two miRNAs are present at the same time, the target binding region specifically recognizes the target to generate two circulators, and then the HCR initiator is released. The EXO III specific cleavage two circulator, and release the target again which achieves the first step of signal amplification. After that, HCR was started by the split initiator generated in the first stage of continuous cycle, and the second step of signal amplification was realized. Thanks to the sensitive color change of gold nanoparticles in response to salt, we achieved ultra-high sensitivity for visual detection of miRNA-21 and miRNA-141. Under optimal conditions, the detection limit of both miRNA is 3 pM and the linear range is 10 pM to 0.4 nM. The method we designed could be applied in early detection and diagnosis of cancer.

Hypoxia stress induces complicated miRNA responses in the gill of Chinese mitten crab (Eriocheir sinensis)

Hypoxia caused by global climate change and human activities has become a growing concern eliciting serious damages to aquatic animals. microRNAs (miRNAs) as non-coding regulatory RNAs exert vital effects on hypoxia responses. Chinese mitten crab (Eriocheir sinensis) with the habitat on the sediment surface or the pond bottom is susceptible to oxygen deficiency. However, whether miRNAs are involved in the response of the crabs to hypoxia stress remains enigmas. In this study, we conducted the whole transcriptome-based miRNA-mRNA integrated analysis of Chinese mitten crab gill under hypoxic condition for 3 h and 24 h We found that the acute hypoxia induces complex miRNA responses with the extensive influences on their target genes that engaged in various bio-processes, especially those associated with immunity, metabolism and endocrine. The impact of hypoxia on crab miRNAs is severer, as the exposure lasts longer. In response to the dissolved oxygen fluctuation, the HIF-1 signaling is activated by miRNAs to cope with the hypoxia stress through strategies including balancing inflammatory and autophagy involved in immunity, changing metabolism to reducing energy consumption, and enhancing oxygen-carrying and delivering capacities. The miRNAs and their corresponding target genes engaged in hypoxia response were intertwined into an intricate network. Moreover, the top hub molecular, miR-998-y and miR-275-z, discovered from the network might serve as biomarkers for hypoxia response in crabs. Our study provides the first systemic miRNA profile of Chinese mitten crab induced by hypoxia stress, and the identified miRNAs and the interactive network add new insights into the mechanism of hypoxia response in crabs.

Abnormal expression and role of MicroRNA-214-3p/SLC8A1 in neonatal Hypoxic-Ischaemic encephalopathy

Neonatal hypoxic-ischaemic encephalopathy (HIE) refers to brain damage caused by intra-uterine distress and asphyxia/hypoxia during the perinatal and neonatal periods. MicroRNA (MiR)-214-3p plays a critical role in cell growth and apoptosis. The aim of this study was to investigate the expression and role of miR-214-3p in neonatal HIE development, and to explore the underlying mechanisms. The expression of miR-214-3p was significantly down-regulated, while that of Slc8a1, a direct target of miR-214-3p, was significantly up-regulated, in the brain tissue of neonatal HIE rats. The over-expression of miR-214-3p promoted the proliferation and inhibited the apoptosis of neurones, while its down-regulation had the opposite effect. Our results indicate that miR-214-3p expression was down-regulated in neonatal HIE rats, and the up-regulation of miR-214-3p expression protected against HIE development by inhibiting neuronal apoptosis.

Insight into the mechanism of DNA methylation and miRNA-mRNA regulatory network in ischemic stroke

BACKGROUND

Epigenetic changes, such as DNA methylation and miRNA-target gene mechanisms, have recently emerged as key provokers in Ischemic stroke (IS) onset. However, cellular and molecular events harboring these epigenetic alterations are poorly understood. Therefore, the present study aimed to explore the potential biomarkers and therapeutic targets for IS.

METHODS

miRNAs, mRNAs and DNA methylation datasets of IS were derived from the GEO database and normalized by PCA sample analysis. Differentially expressed genes (DEGs) were identified, and GO and KEGG enrichment analyses were performed. The overlapped genes were utilized to construct a protein-protein interaction network (PPI). Meanwhile, differentially expressed mRNAs and miRNAs interaction pairs were obtained from the miRDB, TargetScan, miRanda, miRMap and miTarBase databases. We constructed differential miRNA-target gene regulatory networks based on mRNA-miRNA interactions.

RESULTS

A total of 27 up-regulated and 15 down-regulated differential miRNAs were identified. Dataset analysis identified 1053 and 132 up-regulated and 1294 and 9068 down-regulated differentially expressed genes in the GSE16561 and GSE140275 datasets, respectively. Moreover, 9301 hypermethylated and 3356 hypomethylated differentially methylated sites were also identified. Moreover, DEGs were enriched in terms related to translation, peptide biosynthesis, gene expression, autophagy, Th1 and Th2 cell differentiation, primary immunodeficiency, oxidative phosphorylation and T cell receptor signaling pathway. MRPS9, MRPL22, MRPL32 and RPS15 were identified as hub genes. Finally, a differential miRNA-target gene regulatory network was constructed.

CONCLUSIONS

RPS15, along with hsa-miR-363-3p and hsa-miR-320e have been identified in the differential DNA methylation protein interaction network and miRNA-target gene regulatory network, respectively. These findings strongly posit the differentially expressed miRNAs as potential biomarkers to improve ischemic stroke diagnosis and prognosis.

MicroRNA-483-5p Inhibits Hepatocellular Carcinoma Cell Proliferation, Cell Steatosis, and Fibrosis by Targeting PPARα and TIMP2

MicroRNAs (miRNAs) are small non-coding RNA molecules that bind with the 3′ untranslated regions (UTRs) of genes to regulate expression. Downregulation of miR-483-5p (miR-483) is associated with the progression of hepatocellular carcinoma (HCC). However, the significant roles of miR-483 in nonalcoholic fatty liver disease (NAFLD), alcoholic fatty liver diseases (AFLD), and HCC remain elusive. In the current study, we investigated the biological significance of miR-483 in NAFLD, AFLD, and HCC in vitro and in vivo. The downregulation of miR-483 expression in HCC patients’ tumor samples was associated with Notch 3 upregulation. Overexpression of miR-483 in a human bipotent progenitor liver cell line HepaRG and HCC cells dysregulated Notch signaling, inhibited cell proliferation/migration, induced apoptosis, and increased sensitivity towards antineoplastic agents sorafenib/regorafenib. Interestingly, the inactivation of miR-483 upregulated cell steatosis and fibrosis signaling by modulation of lipogenic and fibrosis gene expression. Mechanistically, miR-483 targets PPARα and TIMP2 gene expression, which leads to the suppression of cell steatosis and fibrosis. The downregulation of miR-483 was observed in mice liver fed with a high-fat diet (HFD) or a standard Lieber-Decarli liquid diet containing 5% alcohol, leading to increased hepatic steatosis/fibrosis. Our data suggest that miR-483 inhibits cell steatosis and fibrogenic signaling and functions as a tumor suppressor in HCC. Therefore, miR-483 may be a novel therapeutic target for NAFLD/AFLD/HCC management in patients with fatty liver diseases and HCC.

NCMD: Node2vec-Based Neural Collaborative Filtering for Predicting MiRNA-Disease Association

Numerous studies have reported that micro RNAs (miRNAs) play pivotal roles in disease pathogenesis based on the deregulation of the expressions of target messenger RNAs. Therefore, the identification of disease-related miRNAs is of great significance in understanding human complex diseases, which can also provide insight into the design of novel prognostic markers and disease therapies. Considering the time and cost involved in wet experiments, most recent works have focused on the effective and feasible modeling of computational frameworks to uncover miRNA-disease associations. In this study, we propose a novel framework called node2vec-based neural collaborative filtering for predicting miRNA-disease association (NCMD) based on deep neural networks. Initially, NCMD exploits Node2vec to learn low-dimensional vector representations of miRNAs and diseases. Next, it utilizes a deep learning framework that combines the linear ability of generalized matrix factorization and nonlinear ability of a multilayer perceptron. Experimental results clearly demonstrate the comparable performance of NCMD relative to the state-of-the-art methods according to statistical measures. In addition, case studies on breast cancer, lung cancer and pancreatic cancer validate the effectiveness of NCMD. Extensive experiments demonstrate the benefits of modeling a neural collaborative-filtering-based approach for discovering novel miRNA-disease associations.

Quantitative analysis of miRNAs using SplintR ligase-mediated ligation of complementary-pairing probes enhanced by RNase H (SPLICER)-qPCR

Here, a method using SplintR ligase-mediated ligation of complementary-pairing probes enhanced by RNase H (SPLICER) for miRNAs quantification was established. The strategy has two steps: (1) ligation of two DNA probes specifically hybridize to target miRNA and (2) qPCR amplifying the ligated probe. The miRNA-binding regions of the probes are stem-looped, a motif significantly reduces nonspecific ligation at high ligation temperature (65°C). The ends of the probes are designed complementary to form a paired probe, facilitating the recognition of target miRNAs with low concentrations. RNase H proved to be able to stabilize the heteroduplex formed by the probe and target miRNA, contributing to enhanced sensitivity (limit of detection = 60 copies). High specificity (discriminating homology miRNAs differing only one nucleotide), wide dynamic range (seven orders of magnitude) and ability to accurately detect plant miRNAs (immune to hindrance of 2'-O-methyl moiety) enable SPLICER comparable with the commercially available TaqMan and miRCURY assays. SYBR green I, rather than expensive hydrolysis or locked nucleic acid probes indispensable to TaqMan and miRCURY assays, is adequate for SPLICER. The method was efficient (<1 h), economical ($7 per sample), and robust (able to detect xeno-miRNAs in mammalian bodies), making it a powerful tool for molecular diagnosis and corresponding therapy.

NcPath: a novel platform for visualization and enrichment analysis of human non-coding RNA and KEGG signaling pathways

SUMMARY

Non-coding RNAs play important roles in transcriptional processes and participate in the regulation of various biological functions, in particular miRNAs and lncRNAs. Despite their importance for several biological functions, the existing signaling pathway databases do not include information on miRNA and lncRNA. Here, we redesigned a novel pathway database named NcPath by integrating and visualizing a total of 178 308 human experimentally validated miRNA-target interactions (MTIs), 32 282 experimentally verified lncRNA-target interactions (LTIs) and 4837 experimentally validated human ceRNA networks across 222 KEGG pathways (including 27 sub-categories). To expand the application potential of the redesigned NcPath database, we identified 556 798 reliable lncRNA-protein-coding genes (PCG) interaction pairs by integrating co-expression relations, ceRNA relations, co-TF-binding interactions, co-histone-modification interactions, cis-regulation relations and lncPro Tool predictions between lncRNAs and PCG. In addition, to determine the pathways in which miRNA/lncRNA targets are involved, we performed a KEGG enrichment analysis using a hypergeometric test. The NcPath database also provides information on MTIs/LTIs/ceRNA networks, PubMed IDs, gene annotations and the experimental verification method used. In summary, the NcPath database will serve as an important and continually updated platform that provides annotation and visualization of the pathways on which non-coding RNAs (miRNA and lncRNA) are involved, and provide support to multimodal non-coding RNAs enrichment analysis. The NcPath database is freely accessible at http://ncpath.pianlab.cn/.

AVAILABILITY AND IMPLEMENTATION

NcPath database is freely available at http://ncpath.pianlab.cn/. The code and manual to use NcPath can be found at https://github.com/Marscolono/NcPath/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Bone Marrow Mesenchymal Stem Cells-Derived miR-21-5p Protects Grafted Islets Against Apoptosis by Targeting PDCD4

The apoptosis of grafted islets is an urgent problem due to the high rate of islet loss soon after transplantation. MicroRNA-21-5p (miR-21-5p) is an essential mediator of bone marrow mesenchymal stem cells-derived exosomes (BMSCs-Exo) during anti-apoptosis, but its effect and the underlying molecular mechanism in islet transplantation remain partially understood. Here, we found that miR-21-5p could be delivered to islet cells via BMSCs-Exo. Subsequently, we demonstrated that miR-21-5p overexpression reduced apoptosis in islets and INS-1 cells, whereas miR-21-5p inhibition enhanced apoptosis. A mechanistic analysis involving RNA sequencing and bioinformatic analysis was performed to determine the interaction between miR-21-5p and its target gene programmed cell death 4 (PDCD4), which was further verified by a dual luciferase assay. In vivo, the grafted islets overexpressing miR-21-5p showed a higher survival rate, better insulin secretion function, and a lower apoptosis rate. In conclusion, these results demonstrated that miR‑21‑5p from BMSCs-Exo protects against the apoptosis of grafted islets by inhibiting PDCD4 expression. Hence, miR-21-5p can be used as a cell-free therapeutic agent to minimize β-cell apoptosis at the early stage of islet transplantation.

Prediction of potential small molecule-miRNA associations based on heterogeneous network representation learning

MicroRNAs (miRNAs) are closely associated with the occurrences and developments of many complex human diseases. Increasing studies have shown that miRNAs emerge as new therapeutic targets of small molecule (SM) drugs. Since traditional experiment methods are expensive and time consuming, it is particularly crucial to find efficient computational approaches to predict potential small molecule-miRNA (SM-miRNA) associations. Considering that integrating multi-source heterogeneous information related with SM-miRNA association prediction would provide a comprehensive insight into the features of both SMs and miRNAs, we proposed a novel model of Small Molecule-MiRNA Association prediction based on Heterogeneous Network Representation Learning (SMMA-HNRL) for more precisely predicting the potential SM-miRNA associations. In SMMA-HNRL, a novel heterogeneous information network was constructed with SM nodes, miRNA nodes and disease nodes. To access and utilize of the topological information of the heterogeneous information network, feature vectors of SM and miRNA nodes were obtained by two different heterogeneous network representation learning algorithms (HeGAN and HIN2Vec) respectively and merged with connect operation. Finally, LightGBM was chosen as the classifier of SMMA-HNRL for predicting potential SM-miRNA associations. The 10-fold cross validations were conducted to evaluate the prediction performance of SMMA-HNRL, it achieved an area under of ROC curve of 0.9875, which was superior to other three state-of-the-art models. With two independent validation datasets, the test experiment results revealed the robustness of our model. Moreover, three case studies were performed. As a result, 35, 37, and 22 miRNAs among the top 50 predicting miRNAs associated with 5-FU, cisplatin, and imatinib were validated by experimental literature works respectively, which confirmed the effectiveness of SMMA-HNRL. The source code and experimental data of SMMA-HNRL are available at https://github.com/SMMA-HNRL/SMMA-HNRL.

MicroRNA-205-5p: A potential therapeutic target for influenza A

We are committed to finding host targets for influenza A therapeutics. The nucleoprotein (NP) plays an important role in influenza A virus replication and is an indispensable part of viral transcription and replication. Exploring endogenous substances that can modulate NP is critical for finding host targets. MicroRNAs (miRNAs, miR) are a novel class of powerful, endogenous gene expression regulators. Herein, we used miRanda to analyse the base complementarity between the NP gene and the 14 host miRNAs reported previously by us. MiRanda predicted that miR-431-5p, miR-744-3p and miR-205-5p could complement the NP gene. To understand the effect of these miRNAs on NP expression, we co-transfected 293 T cells with NP gene sequence containing above miRNAs binding site or full sequence of NP gene (transfected into pmirGlo or pcDNA3.1 vectors, respectively), and mimics of miR-205-5p, miR-431-5p and miR-744-3p. Dual luciferase reporter gene or Western blotting assays confirmed that miR-205-5p and miR-431-5p inhibit NP expression by binding with the miRNA binding site of NP gene. Further, we infected Mouse Lung Epithelial (MLE-12) cells overexpressing miR-205-5p and miR-431-5p with influenza A virus and performed Western blotting to examine NP expression. We found that NP expression was significantly reduced in MLE-12 cells overexpressing miR-205-5p during influenza A infection. The miR-205-5p overexpression-induced inhibition of influenza A replication could be attributed to the inhibition of NP expression. Further, we administered oseltamivir and Jinchai Antiviral Capsules (JC, an anti-influenza Chinese medicine) to influenza A virus-infected MLE-12 cells and mice. We found that miR-205-5p was significantly decreased increased in infected cells and lung tissues, and oseltamivir and JC could up-regulate miR-205-5p. In conclusion, we provide new evidence that miR-205-5p plays a role in regulating viral NP protein expression in combating influenza A and may be a potential target for influenza A therapy.

PMMS: Predicting essential miRNAs based on multi-head self-attention mechanism and sequences

Increasing evidence has proved that miRNA plays a significant role in biological progress. In order to understand the etiology and mechanisms of various diseases, it is necessary to identify the essential miRNAs. However, it is time-consuming and expensive to identify essential miRNAs by using traditional biological experiments. It is critical to develop computational methods to predict potential essential miRNAs. In this study, we provided a new computational method (called PMMS) to identify essential miRNAs by using multi-head self-attention and sequences. First, PMMS computes the statistic and structure features and extracts the static feature by concatenating them. Second, PMMS extracts the deep learning original feature (BiLSTM-based feature) by using bi-directional long short-term memory (BiLSTM) and pre-miRNA sequences. In addition, we further obtained the multi-head self-attention feature (MS-based feature) based on BiLSTM-based feature and multi-head self-attention mechanism. By considering the importance of the subsequence of pre-miRNA to the static feature of miRNA, we obtained the deep learning final feature (WA-based feature) based on the weighted attention mechanism. Finally, we concatenated WA-based feature and static feature as an input to the multilayer perceptron) model to predict essential miRNAs. We conducted five-fold cross-validation to evaluate the prediction performance of PMMS. The areas under the ROC curves (AUC), the F1-score, and accuracy (ACC) are used as performance metrics. From the experimental results, PMMS obtained best prediction performances (AUC: 0.9556, F1-score: 0.9030, and ACC: 0.9097). It also outperformed other compared methods. The experimental results also illustrated that PMMS is an effective method to identify essential miRNA.

Predicting miRNA-Disease Association Based on Improved Graph Regression

Recently, as a growing number of associations between microRNAs (miRNAs) and diseases are discovered, researchers gradually realize that miRNAs are closely related to several complicated biological processes and human diseases. Hence, it is especially important to construct availably models to infer associations between miRNAs and diseases. In this study, we presented Improved Graph Regression for miRNA-Disease Association Prediction (IGRMDA) to observe potential relationship between miRNAs and diseases. In order to reduce the inherent noise existing in the acquired biological datasets, we utilized matrix decomposition algorithm to process miRNA functional similarity and disease semantic similarity and then combining them with existing similarity information to obtain final miRNA similarity data and disease similarity data. Then, we applied miRNA-disease association data, miRNA similarity data and disease similarity data to form corresponding latent spaces. Furthermore, we performed improved graph regression algorithm in latent spaces, which included miRNA-disease association space, miRNA similarity space and disease similarity space. Non-negative matrix factorization and partial least squares were used in the graph regression process to obtain important related attributes. The cross validation experiments and case studies were also implemented to prove the effectiveness of IGRMDA, which showed that IGRMDA could predict potential associations between miRNAs and diseases.

Predicting miRNA-disease associations based on multi-view information fusion

MicroRNAs (miRNAs) play an important role in various biological processes and their abnormal expression could lead to the occurrence of diseases. Exploring the potential relationships between miRNAs and diseases can contribute to the diagnosis and treatment of complex diseases. The increasing databases storing miRNA and disease information provide opportunities to develop computational methods for discovering unobserved disease-related miRNAs, but there are still some challenges in how to effectively learn and fuse information from multi-source data. In this study, we propose a multi-view information fusion based method for miRNA-disease association (MDA)prediction, named MVIFMDA. Firstly, multiple heterogeneous networks are constructed by combining the known MDAs and different similarities of miRNAs and diseases based on multi-source information. Secondly, the topology features of miRNAs and diseases are obtained by using the graph convolutional network to each heterogeneous network view, respectively. Moreover, we design the attention strategy at the topology representation level to adaptively fuse representations including different structural information. Meanwhile, we learn the attribute representations of miRNAs and diseases from their similarity attribute views with convolutional neural networks, respectively. Finally, the complicated associations between miRNAs and diseases are reconstructed by applying a bilinear decoder to the combined features, which combine topology and attribute representations. Experimental results on the public dataset demonstrate that our proposed model consistently outperforms baseline methods. The case studies further show the ability of the MVIFMDA model for inferring underlying associations between miRNAs and diseases.

miR-1306 induces cell apoptosis by targeting BMPR1B gene in the ovine granulosa cells

Bone morphogenetic protein receptor type-1B (BMPR1B) is one of the major gene for sheep prolificacy. However, few studies investigated its regulatory region. In this study, we reported that miR-1306 is a direct inhibitor of BMPR1B gene in the ovine granulosa cells (ovine GCs). We detected a miRNA response element of miR-1306 in the 3’ untranslated region of the ovine BMPR1B gene. Luciferase assay showed that the ovine BMPR1B gene is a direct target of miR-1306. qPCR and western blotting revealed that miR-1306 reduces the expression of BMPR1B mRNA and protein in the ovine granulosa cells. Furthermore, miR-1306 promoted cell apoptosis by suppressing BMPR1B expression in the ovine granulosa cells. Overall, our results suggest that miR-1306 is an epigenetic regulator of BMPR1B, and may serve as a potential target to improve the fecundity of sheep.

Exploratory Circular RNA Profiling in Adrenocortical Tumors

Simple Summary

The histological differential diagnosis of adrenocortical adenoma and carcinoma is difficult and requires great expertise. Measures taken towards the distinction of adrenal tumors are of paramount importance. The non-coding circular RNAs (circRNAs) were shown to be expressed in a tissue and tumor specific manner. CircRNAs are investigated as a useful adjunct to the differential diagnosis of benign and malignant tumors of several organs, but they have not been investigated in adrenocortical tumors yet. Here, we have performed circRNA profiling in adrenocortical tumors by next-generation sequencing to detect already known and de novo circRNAs. Out of the five most differentially expressed circRNAs, circPHC3 could be confirmed by TaqMan RT-qPCR to be overexpressed in carcinoma and adenoma vs. healthy tissues in an independent validation cohort.

Abstract

Differentiation of adrenocortical adenoma (ACA) and carcinoma (ACC) is often challenging even in the histological analysis. Circular RNAs (circRNAs) belonging to the group of non-coding RNAs have been implicated as relevant factors in tumorigenesis. Our aim was to explore circRNA expression profiles in adrenocortical tumors by next-generation sequencing followed by RT-qPCR validation. Archived FFPE (formalin-fixed, paraffin embedded) including 8 ACC, 8 ACA and 8 normal adrenal cortices (NAC) were used in the discovery cohort. For de novo and known circRNA expression profiling, a next-generation sequencing platform was used. CIRI2, CircExplorer2, AutoCirc bioinformatics tools were used for the discovery of circRNAs. The top five most differentially circRNAs were measured by RT-qPCR in an independent validation cohort (10 ACC, 8 ACA, 8 NAC). In silico predicted, interacting microRNAs potentially sponged by differentially expressed circRNAs were studied by individual RT-qPCR assays. We focused on overexpressed circRNAs here. Significantly differentially expressed circRNAs have been revealed between the cohorts by NGS. Only circPHC3 could be confirmed to be significantly overexpressed in ACC, ACA vs. NAC samples by RT-qPCR. We could not observe microRNA expression changes fully corresponding to our sponging hypothesis. To the best of our knowledge, our study is the first to investigate circRNAs in adrenocortical tumors. Further studies are warranted to explore their biological and diagnostic relevance.

Extracellular vesicle expansion of PMIS-miR-210 expression inhibits colorectal tumour growth via apoptosis and an XIST/NME1 regulatory mechanism

BACKGROUND

Colorectal cancer (CRC) has a high mortality rate, and therapeutic approaches to treat these cancers are varied and depend on the metabolic state of the tumour. Profiles of CRC tumours have identified several biomarkers, including microRNAs. microRNA-210 (miR-210) levels are directly correlated with CRC survival. miR-210 expression is higher in metastatic colon cancer cells versus non-metastatic and normal colon epithelium. Therefore, efficient methods to inhibit miR-210 expression in CRC may provide new advances in treatments.

METHODS

Expression of miRs was determined in several metastatic and non-metastatic cell lines. miR-210 expression was inhibited using PMIS-miR-210 in transduced cells, which were transplanted into xenograft mice. In separate experiments, CRC tumours were allowed to grow in xenograft mice and treated with therapeutic injections of PMIS-miR-210. Molecular and biochemical experiments identified several new pathways targeted by miR-210 inhibition.

RESULTS

miR-210 inhibition can significantly reduce tumour growth of implanted colon cancer cells in xenograft mouse models. The direct administration of PMIS-miR-210 to existing tumours can inhibit tumour growth in both NSG and Foxn1nu/j mouse models and is more efficacious than capecitabine treatments. Tumour cells further transfer the PMIS-miR-210 inhibitor to neighbouring cells by extracellular vesicles to inhibit miR-210 throughout the tumour. miR-210 inhibition activates the cleaved caspase 3 apoptotic pathway to reduce tumour formation. We demonstrate that the long non-coding transcript XIST is regulated by miR-210 correlating with decreased XIST expression in CRC tumours. XIST acts as a competing endogenous RNA for miR-210, which reduces XIST levels and miR-210 inhibition increases XIST transcripts in the nucleus and cytoplasm. The increased expression of NME1 is associated with H3K4me3 and H3K27ac modifications in the NME1 proximal promoter by XIST.

CONCLUSION

Direct application of the PMIS-miR-210 inhibitor to growing tumours may be an effective colorectal cancer therapeutic.