microRNAs and genetic diseases

Sex-Biased Expression and Response of microRNAs in Neurological Diseases and Neurotrauma

Neurological diseases and neurotrauma manifest significant sex differences in prevalence, progression, outcome, and therapeutic responses. Genetic predisposition, sex hormones, inflammation, and environmental exposures are among many physiological and pathological factors that impact the sex disparity in neurological diseases. MicroRNAs (miRNAs) are a powerful class of gene expression regulator that are extensively involved in mediating biological pathways. Emerging evidence demonstrates that miRNAs play a crucial role in the sex dimorphism observed in various human diseases, including neurological diseases. Understanding the sex differences in miRNA expression and response is believed to have important implications for assessing the risk of neurological disease, defining therapeutic intervention strategies, and advancing both basic research and clinical investigations. However, there is limited research exploring the extent to which miRNAs contribute to the sex disparities observed in various neurological diseases. Here, we review the current state of knowledge related to the sexual dimorphism in miRNAs in neurological diseases and neurotrauma research. We also discuss how sex chromosomes may contribute to the miRNA sexual dimorphism phenomenon. We attempt to emphasize the significance of sexual dimorphism in miRNA biology in human diseases and to advocate a gender/sex-balanced science.

Targeted genome editing restores auditory function in adult mice with progressive hearing loss caused by a human microRNA mutation

Mutations in microRNA-96 ( MIR96 ) cause dominant delayed onset hearing loss DFNA50 without treatment. Genome editing has shown efficacy in hearing recovery by intervention in neonatal mice, yet editing in the adult inner ear is necessary for clinical applications. Here, we developed an editing therapy for a C>A point mutation in the seed region of the Mir96 gene, Mir96 14C>A associated with hearing loss by screening gRNAs for genome editors and optimizing Cas9 and sgRNA scaffold for efficient and specific mutation editing in vitro. By AAV delivery in pre-symptomatic (3-week-old) and symptomatic (6-week-old) adult Mir96 14C>A mutant mice, hair cell on-target editing significantly improved hearing long-term, with an efficacy inversely correlated with injection age. We achieved transient Cas9 expression without the evidence of AAV genomic integration to significantly reduce the safety concerns associated with editing. We developed an AAV-sgmiR96-master system capable of targeting all known human MIR96 mutations. As mouse and human MIR96 sequences share 100% homology, our approach and sgRNA selection for efficient and specific hair cell editing for long-term hearing recovery lays the foundation for future treatment of DFNA50 caused by MIR96 mutations.

miR-136-5p: A key player in human cancers with diagnostic, prognostic and therapeutic implications

MiRNAs have emerged as crucial modulators of the expression of their target genes, attracting significant attention due to their engagement in various cellular processes, like cancer onset and development. Amidst the extensive repertoire of miRNAs implicated in cancer, miR-136-5p has emerged as an emerging miRNA with diverse roles. Dysregulation of miR-136-5p has been proved in human cancers. Accumulating evidence suggests that miR-136-5p mainly functions as a tumor suppressor. These data proposed that miR-136-5p is engaged in the regulation of various cellular processes, like cell proliferation, migration, invasion, EMT, and apoptosis. In addition, miR-136-5p has demonstrated substantial potential as a prognostic and diagnostic marker in human cancers as well as an effective mediator in cancer chemotherapy. Furthermore, miR-136-5p was shown to be correlated with clinicopathological features of affected patients, proposing that it could be used for cancer staging and patient survival. Therefore, a comprehensive comprehension of the precise molecular basis governing miR-136-5p dysregulation in different cancers is vital for unraveling its therapeutic importance. Here, we have discussed the molecular basis of miR-136-5p as a potential tumor suppressor as well as its importance in cancer diagnosis, prognosis, and chemotherapy. Finally, we have discussed the challenge of using miRNAs as a therapeutic target as well as the prospect regarding the importance of miR-136-5p in human cancers.

Circ_0000285 regulates nasopharyngeal carcinoma progression through miR-1278/FNDC3B axis

BACKGROUND

Nasopharyngeal carcinoma (NPC) is cancer with high mortality and poor prognosis. Circular RNAs (circRNAs) have been identified in a wide variety of cancers. But the functional mechanism of circ_000285 in NPC remains unclear.

PURPOSE

To decipher the biological function and molecular mechanism of circ_000285 in NPC.

METHODS

Quantitative PCR (RT-qPCR) was applied for detecting the expression of circ_0000285, miR-1278, and FNDC3B. Western blot was used to measure the protein levels of Fibronectin type III domain containing 3B (FNDC3B), Bcl2 associated X (Bax), and B cell leukemia/lymphoma 2 (Bcl2). Cell proliferation, migration, and invasion were analyzed by colony formation, 5-ethynyl-2'-deoxyuridine (EdU), and transwell assays. Cell apoptosis was detected by flow cytometry assays. ELISA assay was used to analyze Caspase-3 activity. Bioinformatics was used to predict, and the target relationship between miR-1278 and circ_0000285 or FNDC3B was verified by luciferase reporter assay. Tumor xenograft models were established to examine how circ_0000285 functions during the mediation of NPC tumor growth in vivo.

RESULTS

Increased circ_0000285 and FNDC3B expressions, and a decreased miR-1278 expression were observed in NPC tissues and cell lines. Knockdown of circ_0000285 inhibited NPC cell proliferation, migration, invasion, and while promoting NPC cell apoptosis in vitro. Circ_0000285 knockdown-mediated anti-tumor effects in NPC cells could be largely reversed by silencing of miR-1278 or overexpression of FNDC3B. Circ_0000285 could up-regulate FNDC3B expression by sponging miR-1278 in NPC cells. Knockdown of circ_0000285 could inhibit tumor growth in vivo.

CONCLUSION

Circ_0000285 upregulates FNDC3B expression by adsorbing miR-1278 to promote NPC development.

MicroRNAs as Biomarkers for Coronary Artery Disease Related to Type 2 Diabetes Mellitus-From Pathogenesis to Potential Clinical Application

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease with still growing incidence among adults and young people worldwide. Patients with T2DM are more susceptible to developing coronary artery disease (CAD) than non-diabetic individuals. The currently used diagnostic methods do not ensure the detection of CAD at an early stage. Thus, extensive research on non-invasive, blood-based biomarkers is necessary to avoid life-threatening events. MicroRNAs (miRNAs) are small, endogenous, non-coding RNAs that are stable in human body fluids and easily detectable. A number of reports have highlighted that the aberrant expression of miRNAs may impair the diversity of signaling pathways underlying the pathophysiology of atherosclerosis, which is a key player linking T2DM with CAD. The preclinical evidence suggests the atheroprotective and atherogenic influence of miRNAs on every step of T2DM-induced atherogenesis, including endothelial dysfunction, endothelial to mesenchymal transition, macrophage activation, vascular smooth muscle cells proliferation/migration, platelet hyperactivity, and calcification. Among the 122 analyzed miRNAs, 14 top miRNAs appear to be the most consistently dysregulated in T2DM and CAD, whereas 10 miRNAs are altered in T2DM, CAD, and T2DM-CAD patients. This up-to-date overview aims to discuss the role of miRNAs in the development of diabetic CAD, emphasizing their potential clinical usefulness as novel, non-invasive biomarkers and therapeutic targets for T2DM individuals with a predisposition to undergo CAD.

The microRNA processor DROSHA is a candidate gene for a severe progressive neurological disorder

DROSHA encodes a ribonuclease that is a subunit of the Microprocessor complex and is involved in the first step of microRNA (miRNA) biogenesis. To date, DROSHA has not yet been associated with a Mendelian disease. Here, we describe two individuals with profound intellectual disability, epilepsy, white matter atrophy, microcephaly and dysmorphic features, who carry damaging de novo heterozygous variants in DROSHA. DROSHA is constrained for missense variants and moderately intolerant to loss-of-function (o/e = 0.24). The loss of the fruit fly ortholog drosha causes developmental arrest and death in third instar larvae, a severe reduction in brain size and loss of imaginal discs in the larva. Loss of drosha in eye clones causes small and rough eyes in adult flies. One of the identified DROSHA variants (p.Asp1219Gly) behaves as a strong loss-of-function allele in flies, while another variant (p.Arg1342Trp) is less damaging in our assays. In worms, a knock-in that mimics the p.Asp1219Gly variant at a worm equivalent residue causes loss of miRNA expression and heterochronicity, a phenotype characteristic of the loss of miRNA. Together, our data show that the DROSHA variants found in the individuals presented here are damaging based on functional studies in model organisms and likely underlie the severe phenotype involving the nervous system.

MDSCMF: Matrix Decomposition and Similarity-Constrained Matrix Factorization for miRNA–Disease Association Prediction

MicroRNAs (miRNAs) are small non-coding RNAs that are related to a number of complicated biological processes, and numerous studies have demonstrated that miRNAs are closely associated with many human diseases. In this study, we present a matrix decomposition and similarity-constrained matrix factorization (MDSCMF) to predict potential miRNA–disease associations. First of all, we utilized a matrix decomposition (MD) algorithm to get rid of outliers from the miRNA–disease association matrix. Then, miRNA similarity was determined by utilizing similarity kernel fusion (SKF) to integrate miRNA function similarity and Gaussian interaction profile (GIP) kernel similarity, and disease similarity was determined by utilizing SKF to integrate disease semantic similarity and GIP kernel similarity. Furthermore, we added L₂ regularization terms and similarity constraint terms to non-negative matrix factorization to form a similarity-constrained matrix factorization (SCMF) algorithm, which was applied to make prediction. MDSCMF achieved AUC values of 0.9488, 0.9540, and 0.8672 based on fivefold cross-validation (5-CV), global leave-one-out cross-validation (global LOOCV), and local leave-one-out cross-validation (local LOOCV), respectively. Case studies on three common human diseases were also implemented to demonstrate the prediction ability of MDSCMF. All experimental results confirmed that MDSCMF was effective in predicting underlying associations between miRNAs and diseases.

A New Method Based on Matrix Completion and Non-Negative Matrix Factorization for Predicting Disease-Associated miRNAs

Numerous studies have shown that microRNAs are associated with the occurrence and development of human diseases. Thus, studying disease-associated miRNAs is significantly valuable to the prevention, diagnosis and treatment of diseases. In this paper, we proposed a novel method based on matrix completion and non-negative matrix factorization (MCNMF)for predicting disease-associated miRNAs. Due to the information inadequacy on miRNA similarities and disease similarities, we calculated the latter via two models, and introduced the Gaussian interaction profile kernel similarity. In addition, the matrix completion (MC)was employed to further replenish the miRNA and disease similarities to improve the prediction performance. And to reduce the sparsity of miRNA-disease association matrix, the method of weighted K nearest neighbor (WKNKN)was used, which is a pre-processing step. We also utilized non-negative matrix factorization (NMF)using dual L_2,1-norm, graph Laplacian regularization, and Tikhonov regularization to effectively avoid the overfitting during the prediction. Finally, several experiments and a case study were implemented to evaluate the effectiveness and performance of the proposed MCNMF model. The results indicated that our method could reliably and effectively predict disease-associated miRNAs.

LE-MDCAP: A Computational Model to Prioritize Causal miRNA-Disease Associations

MicroRNAs (miRNAs) are associated with various complex human diseases and some miRNAs can be directly involved in the mechanisms of disease. Identifying disease-causative miRNAs can provide novel insight in disease pathogenesis from a miRNA perspective and facilitate disease treatment. To date, various computational models have been developed to predict general miRNA-disease associations, but few models are available to further prioritize causal miRNA-disease associations from non-causal associations. Therefore, in this study, we constructed a Levenshtein-Distance-Enhanced miRNA-disease Causal Association Predictor (LE-MDCAP), to predict potential causal miRNA-disease associations. Specifically, Levenshtein distance matrixes covering the sequence, expression and functional miRNA similarities were introduced to enhance the previous Gaussian interaction profile kernel-based similarity matrix. LE-MDCAP integrated miRNA similarity matrices, disease semantic similarity matrix and known causal miRNA-disease associations to make predictions. For regular causal vs. non-disease association discrimination task, LF-MDCAP achieved area under the receiver operating characteristic curve (AUROC) of 0.911 and 0.906 in 10-fold cross-validation and independent test, respectively. More importantly, LE-MDCAP prominently outperformed the previous MDCAP model in distinguishing causal versus non-causal miRNA-disease associations (AUROC 0.820 vs. 0.695). Case studies performed on diabetic retinopathy and hsa-mir-361 also validated the accuracy of our model. In summary, LE-MDCAP could be useful for screening causal miRNA-disease associations from general miRNA-disease associations.

ILPMDA: Predicting miRNA-Disease Association Based on Improved Label Propagation

MicroRNAs (miRNAs) are small non-coding RNAs that have been demonstrated to be related to numerous complex human diseases. Considerable studies have suggested that miRNAs affect many complicated bioprocesses. Hence, the investigation of disease-related miRNAs by utilizing computational methods is warranted. In this study, we presented an improved label propagation for miRNA-disease association prediction (ILPMDA) method to observe disease-related miRNAs. First, we utilized similarity kernel fusion to integrate different types of biological information for generating miRNA and disease similarity networks. Second, we applied the weighted k-nearest known neighbor algorithm to update verified miRNA-disease association data. Third, we utilized improved label propagation in disease and miRNA similarity networks to make association prediction. Furthermore, we obtained final prediction scores by adopting an average ensemble method to integrate the two kinds of prediction results. To evaluate the prediction performance of ILPMDA, two types of cross-validation methods and case studies on three significant human diseases were implemented to determine the accuracy and effectiveness of ILPMDA. All results demonstrated that ILPMDA had the ability to discover potential miRNA-disease associations.

Functional microRNA targetome undergoes degeneration-induced shift in the retina

BACKGROUND

MicroRNA (miRNA) play a significant role in the pathogenesis of complex neurodegenerative diseases including age-related macular degeneration (AMD), acting as post-transcriptional gene suppressors through their association with argonaute 2 (AGO2) - a key member of the RNA Induced Silencing Complex (RISC). Identifying the retinal miRNA/mRNA interactions in health and disease will provide important insight into the key pathways miRNA regulate in disease pathogenesis and may lead to potential therapeutic targets to mediate retinal degeneration.

METHODS

To identify the active miRnome targetome interactions in the healthy and degenerating retina, AGO2 HITS-CLIP was performed using a rodent model of photoreceptor degeneration. Analysis of publicly available single-cell RNA sequencing (scRNAseq) data was performed to identify the cellular location of AGO2 and key members of the microRNA targetome in the retina. AGO2 findings were verified by in situ hybridization (RNA) and immunohistochemistry (protein).

RESULTS

Analysis revealed a similar miRnome between healthy and damaged retinas, however, a shift in the active targetome was observed with an enrichment of miRNA involvement in inflammatory pathways. This shift was further demonstrated by a change in the seed binding regions of miR-124-3p, the most abundant retinal AGO2-bound miRNA, and has known roles in regulating retinal inflammation. Additionally, photoreceptor cluster miR-183/96/182 were all among the most highly abundant miRNA bound to AGO2. Following damage, AGO2 expression was localized to the inner retinal layers and more in the OLM than in healthy retinas, indicating a locational miRNA response to retinal damage.

CONCLUSIONS

This study provides important insight into the alteration of miRNA regulatory activity that occurs as a response to retinal degeneration and explores the miRNA-mRNA targetome as a consequence of retinal degenerations. Further characterisation of these miRNA/mRNA interactions in the context of the degenerating retina may provide an important insight into the active role these miRNA may play in diseases such as AMD.

SCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization

miRNAs belong to small non-coding RNAs that are related to a number of complicated biological processes. Considerable studies have suggested that miRNAs are closely associated with many human diseases. In this study, we proposed a computational model based on Similarity Constrained Matrix Factorization for miRNA-Disease Association Prediction (SCMFMDA). In order to effectively combine different disease and miRNA similarity data, we applied similarity network fusion algorithm to obtain integrated disease similarity (composed of disease functional similarity, disease semantic similarity and disease Gaussian interaction profile kernel similarity) and integrated miRNA similarity (composed of miRNA functional similarity, miRNA sequence similarity and miRNA Gaussian interaction profile kernel similarity). In addition, the L₂ regularization terms and similarity constraint terms were added to traditional Nonnegative Matrix Factorization algorithm to predict disease-related miRNAs. SCMFMDA achieved AUCs of 0.9675 and 0.9447 based on global Leave-one-out cross validation and five-fold cross validation, respectively. Furthermore, the case studies on two common human diseases were also implemented to demonstrate the prediction accuracy of SCMFMDA. The out of top 50 predicted miRNAs confirmed by experimental reports that indicated SCMFMDA was effective for prediction of relationship between miRNAs and diseases.

Considerable studies have suggested that miRNAs are closely associated with many human diseases, so predicting potential associations between miRNAs and diseases can contribute to the diagnose and treatment of diseases. Several models of discovering unknown miRNA-diseases associations make the prediction more productive and effective. We proposed SCMFMDA to obtain more accuracy prediction result by applying similarity network fusion to fuse multi-source disease and miRNA information and utilizing similarity constrained matrix factorization to make prediction based on biological information. The global Leave-one-out cross validation and five-fold cross validation were applied to evaluate our model. Consequently, SCMFMDA could achieve AUCs of 0.9675 and 0.9447 that were obviously higher than previous computational models. Furthermore, we implemented case studies on significant human diseases including colon neoplasms and lung neoplasms, 47 and 46 of top-50 were confirmed by experimental reports. All results proved that SCMFMDA could be regard as an effective way to discover unverified connections of miRNA-disease.

Profound downregulation of neural transcription factor Npas4 and Nr4a family in fetal mice neurons infected with Zika virus

Zika virus (ZIKV) infection of neurons leads to neurological complications and congenital malformations of the brain of neonates. To date, ZIKV mechanism of infection and pathogenesis is not entirely understood and different studies on gene regulation of ZIKV-infected cells have identified a dysregulation of inflammatory and stem cell maintenance pathways. MicroRNAs (miRNAs) are post-transcriptional regulators of cellular genes and they contribute to cell development in normal function and disease. Previous reports with integrative analyses of messenger RNAs (mRNAs) and miRNAs during ZIKV infection have not identified neurological pathway defects. We hypothesized that dysregulation of pathways involved in neurological functions will be identified by RNA profiling of ZIKV-infected fetal neurons. We therefore used microarrays to analyze gene expression levels following ZIKV infection of fetal murine neurons. We observed that the expression levels of transcription factors such as neural PAS domain protein 4 (Npas4) and of three members of the orphan nuclear receptor 4 (Nr4a) were severely decreased after viral infection. We confirmed that their downregulation was at both the mRNA level and at the protein level. The dysregulation of these transcription factors has been previously linked to aberrant neural functions and development. We next examined the miRNA expression profile in infected primary murine neurons by microarray and found that various miRNAs were dysregulated upon ZIKV infection. An integrative analysis of the differentially expressed miRNAs and mRNAs indicated that miR-7013-5p targets Nr4a3 gene. Using miRmimics, we corroborated that miR-7013-5p downregulates Nr4a3 mRNA and protein levels. Our data identify a profound dysregulation of neural transcription factors with an overexpression of miR-7013-5p that results in decreased Nr4a3 expression, likely a main contributor to ZIKV-induced neuronal dysfunction.

Zika virus (ZIKV) is an emerging virus transmitted horizontally between humans through mosquito bites, and sexual intercourse generally inducing a mild disease. ZIKV is also transmitted vertically from mother-to-child producing congenital ZIKV syndrome (CZVS) in neonates. CZVS leads to severe microcephaly associated with neurological, ocular, musculoskeletal, genitourinary disorders and other disabilities. Although numerous studies have been performed on ZIKV infection of brain cells, we are still far from understanding how ZIKV infection leads to dysregulation of host genes, virus-induced cytopathicity and consequent pathology. Micro (mi)RNAs are small noncoding RNAs encoded and processed by the host cell. They regulate gene expression at the post-transcriptional level in a process called RNA interference (RNAi). Here, we evaluated the relationship between ZIKV infection and the level of mRNAs and miRNAs expressed in the cell. ZIKV infection of mouse embryo neurons downregulated several neural immediate-early genes (IEG). Moreover, we revealed that ZIKV infection led to aberrant regulation of several miRNAs, and identified one whose cognate target was a neural IEG. Our work identifies novel genes and miRNAs that are modulated upon ZIKV infection of fetal murine neurons, therefore linking neuronal dysfunction to transcription and the RNA interference pathway.

MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph

Accurate identification of the miRNA-disease associations (MDAs) helps to understand the etiology and mechanisms of various diseases. However, the experimental methods are costly and time-consuming. Thus, it is urgent to develop computational methods towards the prediction of MDAs. Based on the graph theory, the MDA prediction is regarded as a node classification task in the present study. To solve this task, we propose a novel method MDA-GCNFTG, which predicts MDAs based on Graph Convolutional Networks (GCNs) via graph sampling through the Feature and Topology Graph to improve the training efficiency and accuracy. This method models both the potential connections of feature space and the structural relationships of MDA data. The nodes of the graphs are represented by the disease semantic similarity, miRNA functional similarity and Gaussian interaction profile kernel similarity. Moreover, we considered six tasks simultaneously on the MDA prediction problem at the first time, which ensure that under both balanced and unbalanced sample distribution, MDA-GCNFTG can predict not only new MDAs but also new diseases without known related miRNAs and new miRNAs without known related diseases. The results of 5-fold cross-validation show that the MDA-GCNFTG method has achieved satisfactory performance on all six tasks and is significantly superior to the classic machine learning methods and the state-of-the-art MDA prediction methods. Moreover, the effectiveness of GCNs via the graph sampling strategy and the feature and topology graph in MDA-GCNFTG has also been demonstrated. More importantly, case studies for two diseases and three miRNAs are conducted and achieved satisfactory performance.

SNFIMCMDA: Similarity Network Fusion and Inductive Matrix Completion for miRNA-Disease Association Prediction

MicroRNAs (miRNAs) that belong to non-coding RNAs are verified to be closely associated with several complicated biological processes and human diseases. In this study, we proposed a novel model that was Similarity Network Fusion and Inductive Matrix Completion for miRNA-Disease Association Prediction (SNFIMCMDA). We applied inductive matrix completion (IMC) method to acquire possible associations between miRNAs and diseases, which also could obtain corresponding correlation scores. IMC was performed based on the verified connections of miRNA-disease, miRNA similarity, and disease similarity. In addition, miRNA similarity and disease similarity were calculated by similarity network fusion, which could masterly integrate multiple data types to obtain target data. We integrated miRNA functional similarity and Gaussian interaction profile kernel similarity by similarity network fusion to obtain miRNA similarity. Similarly, disease similarity was integrated in this way. To indicate the utility and effectiveness of SNFIMCMDA, we both applied global leave-one-out cross-validation and five-fold cross-validation to validate our model. Furthermore, case studies on three significant human diseases were also implemented to prove the effectiveness of SNFIMCMDA. The results demonstrated that SNFIMCMDA was effective for prediction of possible associations of miRNA-disease.

PmDNE: Prediction of miRNA-Disease Association Based on Network Embedding and Network Similarity Analysis

Successful prediction of miRNA-disease association is nontrivial for the diagnosis and prognosis of genetic diseases. There are many methods to predict miRNA and disease, but biological data are numerous and complex, and they often exist in the form of network. How to accurately use the features of miRNA and disease-related biological networks to predict unknown association has always been a challenge. Here, we propose PmDNE, a method based on network embedding and network similarity analysis, to predict the miRNA-disease association. In PmDNE, the structure of network bipartite graph is improved, and a random walk generator is designed. For embedded vectors, 128 dimensions are used, and the accuracy of prediction is significantly improved. Compared with other network embedding methods, PmDNE is comparable and competitive with the state of art methods. Our method can solve the problem of feature extraction, reduce the dimension of features, and improve the efficiency of miRNA-disease association prediction. This method can also be extended to other area for biomedical network prediction.

Polymorphism analysis of miR182 and CDKN2B genes in Greek patients with primary open angle glaucoma

Glaucoma is a progressive optic neuropathy resulting from retinal ganglion cells death; it represents one of the leading causes of irreversible blindness worldwide. Although, primary open angle glaucoma (POAG) is the most common type of the disease, the pathogenesis of POAG and the genetic factors contributing to disease development remain poorly understood. The aim of this study was to investigate whether the polymorphisms rs76481776 in miR182 gene and rs3217992 in cyclin-dependent kinase inhibitor-2B (CDKN2B) gene are risk factors for POAG in a series of patients of Greek origin. A case-control study was conducted including 120 patients with POAG and 113 unaffected healthy controls of Greek origin, surveyed for polymorphisms with potential correlation to POAG. DNA from each individual was tested for the miR182 rs76481776 and CDKN2B rs3217992 polymorphisms. Regarding the miR182 rs76481776 polymorphism, the T allele occurred with significantly higher frequency in POAG patients compared to controls (OR: 2.62, 95% CI: 1.56-4.39; p = 0.0002). The CDKN2B rs3217992 A allele frequency was found significantly increased in POAG patients compared to healthy individuals (OR: 1.72, 95% CI: 1.18-2.49; p = 0.005). Therefore, both rs76481776 polymorphism in miR182 gene and rs3217992 polymorphism in CDKN2B gene seem to be associated with the development of POAG in a Greek population. The carriers of the T allele of rs76481776 in miR182 and the carriers of the A allele of rs3217992 in CDKN2B have an increased risk of developing POAG.

Graph regularized L2,1-nonnegative matrix factorization for miRNA-disease association prediction

BACKGROUND

The aberrant expression of microRNAs is closely connected to the occurrence and development of a great deal of human diseases. To study human diseases, numerous effective computational models that are valuable and meaningful have been presented by researchers.

RESULTS

Here, we present a computational framework based on graph Laplacian regularized L_{2, 1}-nonnegative matrix factorization (GRL_{2, 1}-NMF) for inferring possible human disease-connected miRNAs. First, manually validated disease-connected microRNAs were integrated, and microRNA functional similarity information along with two kinds of disease semantic similarities were calculated. Next, we measured Gaussian interaction profile (GIP) kernel similarities for both diseases and microRNAs. Then, we adopted a preprocessing step, namely, weighted K nearest known neighbours (WKNKN), to decrease the sparsity of the miRNA-disease association matrix network. Finally, the GRL_2,1-NMF framework was used to predict links between microRNAs and diseases.

CONCLUSIONS

The new method (GRL_{2, 1}-NMF) achieved AUC values of 0.9280 and 0.9276 in global leave-one-out cross validation (global LOOCV) and five-fold cross validation (5-CV), respectively, showing that GRL_{2, 1}-NMF can powerfully discover potential disease-related miRNAs, even if there is no known associated disease.

A Computational Study of Potential miRNA-Disease Association Inference Based on Ensemble Learning and Kernel Ridge Regression

As increasing experimental studies have shown that microRNAs (miRNAs) are closely related to multiple biological processes and the prevention, diagnosis and treatment of human diseases, a growing number of researchers are focusing on the identification of associations between miRNAs and diseases. Identifying such associations purely via experiments is costly and demanding, which prompts researchers to develop computational methods to complement the experiments. In this paper, a novel prediction model named Ensemble of Kernel Ridge Regression based MiRNA-Disease Association prediction (EKRRMDA) was developed. EKRRMDA obtained features of miRNAs and diseases by integrating the disease semantic similarity, the miRNA functional similarity and the Gaussian interaction profile kernel similarity for diseases and miRNAs. Under the computational framework that utilized ensemble learning and feature dimensionality reduction, multiple base classifiers that combined two Kernel Ridge Regression classifiers from the miRNA side and disease side, respectively, were obtained based on random selection of features. Then average strategy for these base classifiers was adopted to obtain final association scores of miRNA-disease pairs. In the global and local leave-one-out cross validation, EKRRMDA attained the AUCs of 0.9314 and 0.8618, respectively. Moreover, the model’s average AUC with standard deviation in 5-fold cross validation was 0.9275 ± 0.0008. In addition, we implemented three different types of case studies on predicting miRNAs associated with five important diseases. As a result, there were 90% (Esophageal Neoplasms), 86% (Kidney Neoplasms), 86% (Lymphoma), 98% (Lung Neoplasms), and 96% (Breast Neoplasms) of the top 50 predicted miRNAs verified to have associations with these diseases.

DBMDA: A Unified Embedding for Sequence-Based miRNA Similarity Measure with Applications to Predict and Validate miRNA-Disease Associations

MicroRNAs (miRNAs) play a critical role in human diseases. Determining the association between miRNAs and disease contributes to elucidating the pathogenesis of liver diseases and seeking the effective treatment method. Despite great recent advances in the field of the associations between miRNAs and diseases, implementing association verification and recognition efficiently at scale presents serious challenges to biological experimental approaches. Thus, computational methods for predicting miRNA-disease association have become a research hotspot. In this paper, we present a new computational method, named distance-based sequence similarity for miRNA-disease association prediction (DBMDA), that directly learns a mapping from miRNA sequence to a Euclidean space. The notable feature of our approach consists of inferring global similarity from region distances that can be figured by chaos game representation algorithm based on the miRNA sequences. In the 5-fold cross-validation experiment, the area under the curve (AUC) obtained by DBMDA in predicting potential miRNA-disease associations reached 0.9129. To assess the effectiveness of DBMDA more effectively, we compared it with different classifiers and former prediction models. Besides, we constructed two case studies for prostate neoplasms and colon neoplasms. Results show that 39 and 39 out of the top 40 predicted miRNAs were confirmed by other databases, respectively. BDMDA has made new attempts in sequence similarity and achieved excellent results, while at the same time providing a new perspective for predicting the relationship between diseases and miRNAs. The source code and datasets explored in this work are available online from the University of Chinese Academy of Sciences (http://220.171.34.3:81/).

BNPMDA: Bipartite Network Projection for MiRNA-Disease Association prediction

Motivation

A large number of resources have been devoted to exploring the associations between microRNAs (miRNAs) and diseases in the recent years. However, the experimental methods are expensive and time-consuming. Therefore, the computational methods to predict potential miRNA-disease associations have been paid increasing attention.

Results

In this paper, we proposed a novel computational model of Bipartite Network Projection for MiRNA-Disease Association prediction (BNPMDA) based on the known miRNA-disease associations, integrated miRNA similarity and integrated disease similarity. We firstly described the preference degree of a miRNA for its related disease and the preference degree of a disease for its related miRNA with the bias ratings. We constructed bias ratings for miRNAs and diseases by using agglomerative hierarchical clustering according to the three types of networks. Then, we implemented the bipartite network recommendation algorithm to predict the potential miRNA-disease associations by assigning transfer weights to resource allocation links between miRNAs and diseases based on the bias ratings. BNPMDA had been shown to improve the prediction accuracy in comparison with previous models according to the area under the receiver operating characteristics (ROC) curve (AUC) results of three typical cross validations. As a result, the AUCs of Global LOOCV, Local LOOCV and 5-fold cross validation obtained by implementing BNPMDA were 0.9028, 0.8380 and 0.8980 ± 0.0013, respectively. We further implemented two types of case studies on several important human complex diseases to confirm the effectiveness of BNPMDA. In conclusion, BNPMDA could effectively predict the potential miRNA-disease associations at a high accuracy level.

Availability and implementation

BNPMDA is available via http://www.escience.cn/system/file?fileId=99559.

Supplementary information

Supplementary data are available at Bioinformatics online.

Predicting miRNA-Disease Associations by Incorporating Projections in Low-Dimensional Space and Local Topological Information

Predicting the potential microRNA (miRNA) candidates associated with a disease helps in exploring the mechanisms of disease development. Most recent approaches have utilized heterogeneous information about miRNAs and diseases, including miRNA similarities, disease similarities, and miRNA-disease associations. However, these methods do not utilize the projections of miRNAs and diseases in a low-dimensional space. Thus, it is necessary to develop a method that can utilize the effective information in the low-dimensional space to predict potential disease-related miRNA candidates. We proposed a method based on non-negative matrix factorization, named DMAPred, to predict potential miRNA-disease associations. DMAPred exploits the similarities and associations of diseases and miRNAs, and it integrates local topological information of the miRNA network. The likelihood that a miRNA is associated with a disease also depends on their projections in low-dimensional space. Therefore, we project miRNAs and diseases into low-dimensional feature space to yield their low-dimensional and dense feature representations. Moreover, the sparse characteristic of miRNA-disease associations was introduced to make our predictive model more credible. DMAPred achieved superior performance for 15 well-characterized diseases with AUCs (area under the receiver operating characteristic curve) ranging from 0.860 to 0.973 and AUPRs (area under the precision-recall curve) ranging from 0.118 to 0.761. In addition, case studies on breast, prostatic, and lung neoplasms demonstrated the ability of DMAPred to discover potential disease-related miRNAs.

Inferring the Disease-Associated miRNAs Based on Network Representation Learning and Convolutional Neural Networks. Int J Mol Sci. 2019;20:3648. [PMID: 31349729 PMCID: PMC6696449 DOI: 10.3390/ijms20153648]

Identification of disease-associated miRNAs (disease miRNAs) are critical for understanding etiology and pathogenesis. Most previous methods focus on integrating similarities and associating information contained in heterogeneous miRNA-disease networks. However, these methods establish only shallow prediction models that fail to capture complex relationships among miRNA similarities, disease similarities, and miRNA-disease associations. We propose a prediction method on the basis of network representation learning and convolutional neural networks to predict disease miRNAs, called CNNMDA. CNNMDA deeply integrates the similarity information of miRNAs and diseases, miRNA-disease associations, and representations of miRNAs and diseases in low-dimensional feature space. The new framework based on deep learning was built to learn the original and global representation of a miRNA-disease pair. First, diverse biological premises about miRNAs and diseases were combined to construct the embedding layer in the left part of the framework, from a biological perspective. Second, the various connection edges in the miRNA-disease network, such as similarity and association connections, were dependent on each other. Therefore, it was necessary to learn the low-dimensional representations of the miRNA and disease nodes based on the entire network. The right part of the framework learnt the low-dimensional representation of each miRNA and disease node based on non-negative matrix factorization, and these representations were used to establish the corresponding embedding layer. Finally, the left and right embedding layers went through convolutional modules to deeply learn the complex and non-linear relationships among the similarities and associations between miRNAs and diseases. Experimental results based on cross validation indicated that CNNMDA yields superior performance compared to several state-of-the-art methods. Furthermore, case studies on lung, breast, and pancreatic neoplasms demonstrated the powerful ability of CNNMDA to discover potential disease miRNAs.

Down-regulation of miR-378a-3p induces decidual cell apoptosis: a possible mechanism for early pregnancy loss

STUDY QUESTION

Do microRNAs (miRNAs) contribute to human early pregnancy loss (EPL)?

SUMMARY ANSWER

miR-378a-3p expression is regulated by progesterone and is down-regulated in ducidua of EPL patients which may contribute to decidual apoptosis through Caspase-3 activation.

WHAT IS KNOWN ALREADY

A variety of miRNAs have been demonstrated to be associated with the development of decidualization and placental formation. However, little has been reported on the roles of miRNA in the pathogenesis of EPL.

STUDY DESIGN, SIZE, DURATION

Normal and EPL decidual tissues were collected from patients with normal pregnancies undergoing elective termination of gestation, and from patients with EPL, respectively.

PARTICIPANTS/MATERIALS, SETTING, METHODS

miRNA microarrays were used to identify the differentially expressed miRNAs between normal and EPL decidua, and miRNA expression was confirmed by qRT-PCR, qRT-PCR, western blotting and luciferase reporter assays were employed to validate the downstream targets of miR-378a-3p. The effects of miR-378a-3p were evaluated using miR-378a-3p-transfected decidual cells.

MAIN RESULTS AND THE ROLE OF CHANCE

Of note, 32 up-regulated miRNAs and 38 down-regulated miRNAs were identified by microarray analysis when comparing EPL to normal decidua. MiR-378a-3p was significantly down-regulated in the EPL decidua and was found to inversely regulate the expression of Caspase-3 by directly binding to its 3'-UTRs. In decidual cells, transfection of miR-378a-3p mimics resulted in the inhibition of cell apoptosis and in the increase of cell proliferation through Caspase-3 suppression. Moreover, we found that progesterone could induce the expression of miR-378a-3p in decidual cells.

LIMITATIONS, REASONS FOR CAUTION

This study focused on the function of miR-378a-3p and its target Caspase-3, however, numerous other targets and miRNAs may also be responsible for the pathogenesis of EPL. Therefore, further studies are required to elucidate the role of miRNAs in EPL.

WIDER IMPLICATIONS OF THE FINDINGS

Our findings indicate that miR-378a-3p may contribute to the development of EPL, and that it could serve as a new potential predictive and therapeutic target of progesterone-treatment for EPL.

STUDY FUNDING/COMPETING INTEREST

This study was supported by National Basic Research Program of China (No.2012CB944900); National Science Foundation of China (No.31471405 and 81490742, No.81361120246); The National Science and Technology Support Program (No.2012BA132B00). Authors declare no competing interests.

Differential microRNA expression in cultured palatal fibroblasts from infants with cleft palate and controls

Background

The role of microRNAs (miRNAs) in animal models of palatogenesis has been shown, but only limited research has been carried out in humans. To date, no miRNA expression study on tissues or cells from cleft palate patients has been published. We compared miRNA expression in palatal fibroblasts from cleft palate patients and age-matched controls.

Material and Methods

Cultured palatal fibroblasts from 10 non-syndromic cleft lip and palate patients (nsCLP; mean age: 18 ± 2 months), 5 non-syndromic cleft palate only patients (nsCPO; mean age: 17 ± 2 months), and 10 controls (mean age: 24 ± 5 months) were analysed with next-generation small RNA sequencing. All subjects are from Western European descent. Sequence reads were bioinformatically processed and the differentially expressed miRNAs were technically validated using quantitative reverse-transcription polymerase chain reaction (RT-qPCR).

Results

Using RNA sequencing, three miRNAs (hsa-miR-93-5p, hsa-miR-18a-5p, and hsa-miR-92a-3p) were up-regulated and six (hsa-miR-29c-5p, hsa-miR-549a, hsa-miR-3182, hsa-miR-181a-5p, hsa-miR-451a, and hsa-miR-92b-5p) were down-regulated in nsCPO fibroblasts. One miRNA (hsa-miR-505-3p) was down-regulated in nsCLP fibroblasts. Of these, hsa-miR-505-3p, hsa-miR-92a, hsa-miR-181a, and hsa-miR-451a were also differentially expressed using RT-PCR with a higher fold change than in RNAseq.

Limitations

The small sample size may limit the value of the data. In addition, interpretation of the data is complicated by the fact that biopsy samples are taken after birth, while the origin of the cleft lies in the embryonic period. This, together with possible effects of the culture medium, implies that only cell-autonomous genetic and epigenetic differences might be detected.

Conclusions

For the first time, we have shown that several miRNAs appear to be dysregulated in palatal fibroblasts from patients with nsCLP and nsCPO. Furthermore, large-scale genomic and expression studies are needed to validate these findings.

A heterogeneous label propagation approach to explore the potential associations between miRNA and disease

Background

Research on microRNAs (miRNAs) has attracted increasingly worldwide attention over recent years as growing experimental results have made clear that miRNA correlates with masses of critical biological processes and the occurrence, development, and diagnosis of human complex diseases. Nonetheless, the known miRNA-disease associations are still insufficient considering plenty of human miRNAs discovered now. Therefore, there is an urgent need for effective computational model predicting novel miRNA-disease association prediction to save time and money for follow-up biological experiments.

Methods

In this study, considering the insufficiency of the previous computational methods, we proposed the model named heterogeneous label propagation for MiRNA-disease association prediction (HLPMDA), in which a heterogeneous label was propagated on the multi-network of miRNA, disease and long non-coding RNA (lncRNA) to infer the possible miRNA-disease association. The strength of the data about lncRNA–miRNA association and lncRNA-disease association enabled HLPMDA to produce a better prediction.

Results

HLPMDA achieved AUCs of 0.9232, 0.8437 and 0.9218 ± 0.0004 based on global and local leave-one-out cross validation and 5-fold cross validation, respectively. Furthermore, three kinds of case studies were implemented and 47 (esophageal neoplasms), 49 (breast neoplasms) and 46 (lymphoma) of top 50 candidate miRNAs were proved by experiment reports.

Conclusions

All the results adequately showed that HLPMDA is a recommendable miRNA-disease association prediction method. We anticipated that HLPMDA could help the follow-up investigations by biomedical researchers.

Electronic supplementary material

The online version of this article (10.1186/s12967-018-1722-1) contains supplementary material, which is available to authorized users.

Network embedding in biomedical data science

Owning to the rapid development of computer technologies, an increasing number of relational data have been emerging in modern biomedical research. Many network-based learning methods have been proposed to perform analysis on such data, which provide people a deep understanding of topology and knowledge behind the biomedical networks and benefit a lot of applications for human healthcare. However, most network-based methods suffer from high computational and space cost. There remain challenges on handling high dimensionality and sparsity of the biomedical networks. The latest advances in network embedding technologies provide new effective paradigms to solve the network analysis problem. It converts network into a low-dimensional space while maximally preserves structural properties. In this way, downstream tasks such as link prediction and node classification can be done by traditional machine learning methods. In this survey, we conduct a comprehensive review of the literature on applying network embedding to advance the biomedical domain. We first briefly introduce the widely used network embedding models. After that, we carefully discuss how the network embedding approaches were performed on biomedical networks as well as how they accelerated the downstream tasks in biomedical science. Finally, we discuss challenges the existing network embedding applications in biomedical domains are faced with and suggest several promising future directions for a better improvement in human healthcare.

Dual Convolutional Neural Network Based Method for Predicting Disease-Related miRNAs

Identification of disease-related microRNAs (disease miRNAs) is helpful for understanding and exploring the etiology and pathogenesis of diseases. Most of recent methods predict disease miRNAs by integrating the similarities and associations of miRNAs and diseases. However, these methods fail to learn the deep features of the miRNA similarities, the disease similarities, and the miRNA–disease associations. We propose a dual convolutional neural network-based method for predicting candidate disease miRNAs and refer to it as CNNDMP. CNNDMP not only exploits the similarities and associations of miRNAs and diseases, but also captures the topology structures of the miRNA and disease networks. An embedding layer is constructed by combining the biological premises about the miRNA–disease associations. A new framework based on the dual convolutional neural network is presented for extracting the deep feature representation of associations. The left part of the framework focuses on integrating the original similarities and associations of miRNAs and diseases. The novel miRNA and disease similarities which contain the topology structures are obtained by random walks on the miRNA and disease networks, and their deep features are learned by the right part of the framework. CNNDMP achieves the superior prediction performance than several state-of-the-art methods during the cross-validation process. Case studies on breast cancer, colorectal cancer and lung cancer further demonstrate CNNDMP’s powerful ability of discovering potential disease miRNAs.

Prediction of potential disease-associated microRNAs by composite network based inference

MicroRNAs (miRNAs) act a significant role in multiple biological processes and their associations with the development of all kinds of complex diseases are much close. In the research area of biology, medicine, and bioinformatics, prediction of potential miRNA-disease associations (MDAs) on the base of a variety of heterogeneous biological datasets in a short time is an important subject. Therefore, we proposed the model of Composite Network based inference for MiRNA-Disease Association prediction (CNMDA) through applying random walk to a multi-level composite network constructed by heterogeneous dataset of disease, long noncoding RNA (lncRNA) and miRNA. The results showed that CNMDA achieved an AUC of 0.8547 in leave-one-out cross validation and an AUC of 0.8533+/−0.0009 in 5-fold cross validation. In addition, we employed CNMDA to infer novel miRNAs for kidney neoplasms, breast neoplasms and lung neoplasms on the base of HMDD v2.0. Also, we employed the approach for lung neoplasms on the base of HMDD v1.0 and for breast neoplasms that have no known related miRNAs. It was found that CNMDA could be seen as an applicable tool for potential MDAs prediction.

Inferring disease-associated microRNAs in heterogeneous networks with node attributes

Identification of disease-associated microRNAs (disease miRNAs) is an essential step towards discovering causal miRNAs and understanding disease pathogenesis. Two sources of information can be exploited for predicting disease miRNAs: one includes the connections between miRNAs, between diseases, and between miRNAs and diseases, and the other has the attributes of miRNA nodes. The former contains information of miRNA similarities, disease similarities, and miRNA-disease associations. The latter includes the information of the families and clusters that miRNAs belong to. Similar diseases are usually associated with miRNAs that have similar functions and common attributes. However, most of the existing methods for disease miRNA prediction focus only on the connections of miRNAs and diseases. It remains challenging to adequately integrate the connections and miRNA node attributes to identify more reliable candidate disease miRNAs. We propose a non-negative matrix factorization based method, FamCluRank, for predicting disease miRNAs in heterogeneous networks with node attributes. One of the novelties of FamCluRank is to fully utilize these two oversighted characteristics of miRNAs and focuses particularly on a deep integration of miRNA families and cluster attributes. In particular, the integration was achieved by three different means. We first constructed a miRNA-disease heterogeneous network with node attributes where the miRNA nodes have their family and cluster attributes. Second, miRNAs sharing more common families and clusters are more likely to be associated with the diseases that are also related to these families and clusters. On the basis of the biological premise, we constructed a novel prediction model of FamCluRank to deeply integrate the family and cluster attributes of miRNAs. Third, two similar diseases tend to be associated with more common miRNA families and clusters, and vice versa. Hence FamCluRank's prediction model is constructed by concerning not only the possible associations between miRNAs and diseases but also the possible disease-family and disease-cluster associations. Comparison with the state-of-the-art methods showed FamCluRank's superior performance not only on the well-characterized diseases but also on the new ones. Case studies on colorectal neoplasms, pancreatic neoplasms, lung neoplasms, and 32 new diseases demonstrated its ability for discovering potential disease miRNAs. FamCluRank is a potent prioritization tool for screening the reliable candidates for subsequent studies concerning their involvement in the pathogenesis of diseases. The web service of FamCluRank, the candidate disease miRNAs for 329 diseases, and the dataset used to develop FamCluRank are available at http://www.famclurank.top.

Genetic Aspects of Keratoconus: A Literature Review Exploring Potential Genetic Contributions and Possible Genetic Relationships with Comorbidities

Introduction

Keratoconus (KC) is a complex, genetically heterogeneous, multifactorial degenerative disorder that is accompanied by corneal ectasia which usually progresses asymmetrically. With an incidence of approximately 1 per 2000 and 2 cases per 100,000 population presenting annually, KC follows an autosomal recessive or dominant pattern of inheritance and is, apparently, associated with genes that interact with environmental, genetic, and/or other factors. This is an important consideration in refractive surgery in the case of familial KC, given the association of KC with other genetic disorders and the imbalance between dizygotic twins. The present review attempts to identify the genetic loci contributing to the different KC clinical presentations and relate them to the common genetically determined comorbidities associated with KC.

Methods

The PubMed, MEDLINE, Google Scholar, and GeneCards databases were screened for KC-related articles published in English between January 2006 and November 2017. Keyword combinations of “keratoconus,” “risk factor(s),” “genetics,” “genes,” “genetic association(s),” and “cornea” were used. In total, 217 articles were retrieved and analyzed, with greater weight placed on the more recent literature. Further bibliographic research based on the 217 articles revealed another 124 relevant articles that were included in this review. Using the reviewed literature, an attempt was made to correlate genes and genetic risk factors with KC characteristics and genetically related comorbidities associated with KC based on genome-wide association studies, family-based linkage analysis, and candidate-gene approaches.

Results

An association matrix between known KC-related genes and KC symptoms and/or clinical signs together with an association matrix between identified KC genes and genetically related KC comorbidities/syndromes were constructed.

Conclusion

Twenty-four genes were identified as potential contributors to KC and 49 KC-related comorbidities/syndromes were found. More than 85% of the known KC-related genes are involved in glaucoma, Down syndrome, connective tissue disorders, endothelial dystrophy, posterior polymorphous corneal dystrophy, and cataract.

Identifying and Exploiting Potential miRNA-Disease Associations With Neighborhood Regularized Logistic Matrix Factorization

With the rapid development of biological research, microRNAs (miRNA) have become an attractive topic because lots of experimental studies have revealed the significant associations between miRNAs and diseases. However, considering that experiments are expensive and time-consuming, computational methods for predicting associations between miRNAs and diseases have become increasingly crucial. In this study, we proposed a neighborhood regularized logistic matrix factorization method for miRNA-disease association prediction (NRLMFMDA) by integrating miRNA functional similarity, disease semantic similarity, Gaussian interaction profile kernel similarity, and experimentally validation of disease-miRNA association. We used Gaussian interaction profile kernel similarity to cover the shortage of the traditional similarity to make it more reasonable and complete. Furthermore, NRLMFMDA also considered the important influences of the neighborhood information and took full advantage of them to improve the accuracy of the miRNA-disease association prediction. We also improved the accuracy by giving higher weights to the known association data in the process of calculating the potential association probabilities. In the global and the local leave-one-out cross validation, NRLMFMDA got the AUCs of 0.9068 and 0.8239, respectively. Moreover, the average AUC of NRLMFMDA in 5-fold cross validation was 0.8976 ± 0.0034. All the three kinds of cross validations have shown significant advantages to a number of previous models. In the case studies of breast neoplasms, esophageal neoplasms and lymphoma according to known miRNA-disease associations in the recent version of HMDD database, there were 78, 80, and 74% of top 50 predicted related miRNAs verified to have associations with these three diseases, respectively. In the further case studies for new disease without any known related miRNAs and the previous version of HMDD database, there were also high proportions of the predicted miRNAs verified by experimental reports. All the validation experiment results have demonstrated the effectiveness and practicability of NRLFMDA to predict the potential miRNA-disease associations.

MDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction

Recently, a growing number of biological research and scientific experiments have demonstrated that microRNA (miRNA) affects the development of human complex diseases. Discovering miRNA-disease associations plays an increasingly vital role in devising diagnostic and therapeutic tools for diseases. However, since uncovering associations via experimental methods is expensive and time-consuming, novel and effective computational methods for association prediction are in demand. In this study, we developed a computational model of Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction (MDHGI) to discover new miRNA-disease associations by integrating the predicted association probability obtained from matrix decomposition through sparse learning method, the miRNA functional similarity, the disease semantic similarity, and the Gaussian interaction profile kernel similarity for diseases and miRNAs into a heterogeneous network. Compared with previous computational models based on heterogeneous networks, our model took full advantage of matrix decomposition before the construction of heterogeneous network, thereby improving the prediction accuracy. MDHGI obtained AUCs of 0.8945 and 0.8240 in the global and the local leave-one-out cross validation, respectively. Moreover, the AUC of 0.8794+/-0.0021 in 5-fold cross validation confirmed its stability of predictive performance. In addition, to further evaluate the model's accuracy, we applied MDHGI to four important human cancers in three different kinds of case studies. In the first type, 98% (Esophageal Neoplasms) and 98% (Lymphoma) of top 50 predicted miRNAs have been confirmed by at least one of the two databases (dbDEMC and miR2Disease) or at least one experimental literature in PubMed. In the second type of case study, what made a difference was that we removed all known associations between the miRNAs and Lung Neoplasms before implementing MDHGI on Lung Neoplasms. As a result, 100% (Lung Neoplasms) of top 50 related miRNAs have been indexed by at least one of the three databases (dbDEMC, miR2Disease and HMDD V2.0) or at least one experimental literature in PubMed. Furthermore, we also tested our prediction method on the HMDD V1.0 database to prove the applicability of MDHGI to different datasets. The results showed that 50 out of top 50 miRNAs related with the breast neoplasms were validated by at least one of the three databases (HMDD V2.0, dbDEMC, and miR2Disease) or at least one experimental literature.

Predicting microRNA-disease associations using label propagation based on linear neighborhood similarity

Interactions between microRNAs (miRNAs) and diseases can yield important information for uncovering novel prognostic markers. Since experimental determination of disease-miRNA associations is time-consuming and costly, attention has been given to designing efficient and robust computational techniques for identifying undiscovered interactions. In this study, we present a label propagation model with linear neighborhood similarity, called LPLNS, to predict unobserved miRNA-disease associations. Additionally, a preprocessing step is performed to derive new interaction likelihood profiles that will contribute to the prediction since new miRNAs and diseases lack known associations. Our results demonstrate that the LPLNS model based on the known disease-miRNA associations could achieve impressive performance with an AUC of 0.9034. Furthermore, we observed that the LPLNS model based on new interaction likelihood profiles could improve the performance to an AUC of 0.9127. This was better than other comparable methods. In addition, case studies also demonstrated our method's outstanding performance for inferring undiscovered interactions between miRNAs and diseases, especially for novel diseases.

Global and gene-specific DNA methylation and hydroxymethylation in human skin exposed and not exposed to sun radiation

Background

epigenomes can be influenced by environmental factors leading to the development of diseases.

Objective

To investigate the influence of sun exposure on global DNA methylation and hydroxymethylation status and at specific sites of the miR-9-1, miR-9-3 and MTHFR genes in skin samples of subjects with no history of skin diseases.

Methods

Skin samples were obtained by punch on sun-exposed and sun-protected arm areas from 24 corpses of 16-89 years of age. Genomic DNA was extracted from skin samples that were ranked according to Fitzpatrick's criteria as light, moderate, and dark brown. Global DNA methylation and hydroxymethylation and DNA methylation analyses at specific sites were performed using ELISA and MSP, respectively.

Results

No significant differences in global DNA methylation and hydroxymethylation levels were found among the skin areas, skin types, or age. However, gender-related differences were detected, where women showed higher methylation levels. Global DNA methylation levels were higher than hydroxymethylation levels, and the levels of these DNA modifications correlated in skin tissue. For specific sites, no differences among the areas were detected. Additional analyses showed no differences in the methylation status when age, gender, and skin type were considered; however, the methylation status of the miR-9-1 gene seems to be gender related.

Study limitations

there was no separation of dermis and epidermis and low sample size.

Conclusion

sun exposure does not induce changes in the DNA methylation and hydroxymethylation status or in miR-9-1, miR-9-3 and MTHFR genes for the studied skin types.

HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction

Recently, microRNAs (miRNAs) have drawn more and more attentions because accumulating experimental studies have indicated miRNA could play critical roles in multiple biological processes as well as the development and progression of human complex diseases. Using the huge number of known heterogeneous biological datasets to predict potential associations between miRNAs and diseases is an important topic in the field of biology, medicine, and bioinformatics. In this study, considering the limitations in the previous computational methods, we developed the computational model of Heterogeneous Graph Inference for MiRNA-Disease Association prediction (HGIMDA) to uncover potential miRNA-disease associations by integrating miRNA functional similarity, disease semantic similarity, Gaussian interaction profile kernel similarity, and experimentally verified miRNA-disease associations into a heterogeneous graph. HGIMDA obtained AUCs of 0.8781 and 0.8077 based on global and local leave-one-out cross validation, respectively. Furthermore, HGIMDA was applied to three important human cancers for performance evaluation. As a result, 90% (Colon Neoplasms), 88% (Esophageal Neoplasms) and 88% (Kidney Neoplasms) of top 50 predicted miRNAs are confirmed by recent experiment reports. Furthermore, HGIMDA could be effectively applied to new diseases and new miRNAs without any known associations, which overcome the important limitations of many previous computational models.

MiR-223-5p works as an oncomiR in vulvar carcinoma by TP63 suppression

MiR-223-5p has been previously mentioned to be associated with tumor metastasis in HPV negative vulvar carcinomas, such as in several other tumor types. In the present study, we hypothesized that this microRNA would be important in vulvar cancer carcinogenesis and progression. To investigate this, we artificially mimicked miR-223-5p expression in a cell line derived from lymph node metastasis of vulvar carcinoma (SW962) and performed in vitro assays. As results, lower cell proliferation (p < 0.01) and migration (p < 0.001) were observed when miR-223-5p was overexpressed. In contrast, increased invasive potential of these cells was verified (p < 0.004). In silico search indicated that miR-223-5p targets TP63, member of the TP53 family of proteins, largely described with importance in vulvar cancer. We experimentally demonstrated that this microRNA is capable to decrease levels of p63 at both mRNA and protein levels (p < 0.001, and p < 0.0001; respectively). Also, a significant inverse correlation was observed between miR-223-5p and p63 expressions in tumors from patients (p = 0.0365). Furthermore, low p63 protein expression was correlated with deeper tumor invasion (p = 0.0491) and lower patient overall survival (p = 0.0494). Our study points out miR-223-5p overexpression as a putative pathological mechanism of tumor invasion and a promising therapeutic target and highlights the importance of both miR-223-5p and p63 as prognostic factors in vulvar cancer. Also, it is plausible that the evaluation of p63 expression in vulvar cancer at the biopsy level may bring important contribution on prognostic establishment and in elaborating better surgical approaches for vulvar cancer patients.

GRMDA: Graph Regression for MiRNA-Disease Association Prediction

Nowadays, as more and more associations between microRNAs (miRNAs) and diseases have been discovered, miRNA has gradually become a hot topic in the biological field. Because of the high consumption of time and money on carrying out biological experiments, computational method which can help scientists choose the most likely associations between miRNAs and diseases for further experimental studies is desperately needed. In this study, we proposed a method of Graph Regression for MiRNA-Disease Association prediction (GRMDA) which combines known miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity. We used Gaussian interaction profile kernel similarity to supplement the shortage of miRNA functional similarity and disease semantic similarity. Furthermore, the graph regression was synchronously performed in three latent spaces, including association space, miRNA similarity space, and disease similarity space, by using two matrix factorization approaches called Singular Value Decomposition and Partial Least-Squares to extract important related attributes and filter the noise. In the leave-one-out cross validation and five-fold cross validation, GRMDA obtained the AUCs of 0.8272 and 0.8080 ± 0.0024, respectively. Thus, its performance is better than some previous models. In the case study of Lymphoma using the recorded miRNA-disease associations in HMDD V2.0 database, 88% of top 50 predicted miRNAs were verified by experimental literatures. In order to test the performance of GRMDA on new diseases with no known related miRNAs, we took Breast Neoplasms as an example by regarding all the known related miRNAs as unknown ones. We found that 100% of top 50 predicted miRNAs were verified. Moreover, 84% of top 50 predicted miRNAs in case study for Esophageal Neoplasms based on HMDD V1.0 were verified to have known associations. In conclusion, GRMDA is an effective and practical method for miRNA-disease association prediction.

Non-nucleotide Modification of Anti-miRNA Oligonucleotides

MicroRNAs (miRNAs) are important modulators of gene expression. Synthetic anti-microRNA oligonucleotides (AMOs, or anti-miRs) are a form of steric-blocking antisense oligonucleotides (ASOs) that inhibit miRNA function through high-affinity binding and subsequent inactivation and/or degradation of the targeted miRNA. AMOs are a primary tool used to empirically determine the biological targets of a miRNA and can also be used therapeutically when overexpression of a miRNA contributes to a disease state. Chemical modification of synthetic AMOs enhance potency by protecting the oligonucleotide from nuclease degradation and by increasing binding affinity to the target miRNA. A new steric-blocking ASO modification strategy with favorable properties for use in AMOs was recently developed that combines use of high-affinity 2'-O-methyl RNA with terminally positioned non-nucleotide "ZEN" modifiers. This protocol describes use of ZEN AMOs in a dual-luciferase reporter assay as a simplified means to validate AMO performance or to quickly test putative miRNA binding sites in target sequences. This protocol also describes a method using Western blot analysis for quantifying the level of upregulation of proteins made from an mRNA that is thought to be under miRNA regulation, following inhibition of that miRNA by ZEN AMO treatment.

Unraveling the Mechanistic Complexity of the Glomerulocystic Phenotype in Dicer Conditional KO Mice by 2D Gel Electrophoresis Coupled Mass Spectrometry

PURPOSE

Dicer, an RNase III type endonuclease, is a key enzyme involved in miRNA biogenesis. It has been shown that this enzyme is essential for several aspects of postnatal kidney functions and homeostasis. In this study, we have examined conditional knockout (cKO) mice for Dicer in Pax8 (Paired-box gene 8) expressing cells to investigate the kidney protein profile. This specific model develops a glomerulocystic phenotype coupled with urinary concentration impairment, proteinuria, and severe renal failure.

EXPERIMENTAL DESIGN

Proteomic analysis was performed on kidney tissue extracts from cKO and control (Ctr) mice by 2D Gel Electrophoresis coupled with mass spectrometry.

RESULTS

The analysis highlighted 120 protein spots differentially expressed in Dicer cKO tissue compared with control; some of these proteins were validated by Western blotting. Ingenuity Pathway Analysis led to the identification of some interesting networks; among them, the one having ERK as a central hub may explain, through the modulation of the expression of a number of identified protein targets, the metabolic and structural alterations occurring during kidney cyst development in Dicer cKO mouse model.

CONCLUSIONS AND CLINICAL RELEVANCE

Our results contribute to gain new insights into molecular mechanisms through which Dicer endonuclease controls kidney development and physiological functions.

SSCMDA: spy and super cluster strategy for MiRNA-disease association prediction

In the biological field, the identification of the associations between microRNAs (miRNAs) and diseases has been paid increasing attention as an extremely meaningful study for the clinical medicine. However, it is expensive and time-consuming to confirm miRNA-disease associations by experimental methods. Therefore, in recent years, several effective computational models for predicting the potential miRNA-disease associations have been developed. In this paper, we proposed the Spy and Super Cluster strategy for MiRNA-Disease Association prediction (SSCMDA) based on known miRNA-disease associations, integrated disease similarity and integrated miRNA similarity. For problems of mixed unknown miRNA-disease pairs containing both potential associations and real negative associations, which will lead to inaccurate prediction, spy strategy is adopted by SSCMDA to identify reliable negative samples from the unknown miRNA-disease pairs. Moreover, the super-cluster strategy could gather as many positive samples as possible to improve the accuracy of the prediction by overcoming the shortage of lacking sufficient positive training samples. As a result, the AUCs of global leave-one-out cross validation (LOOCV), local LOOCV and 5-fold cross validation were 0.9007, 0.8747 and 0.8806+/-0.0025, respectively. According to the AUC results, SSCMDA has shown a significant improvement compared with some previous models. We further carried out case studies based on various version of HMDD database to test the prediction performance robustness of SSCMDA. We also implemented case study to examine whether SSCMDA was effective for new diseases without any known associated miRNAs. As a result, a large proportion of the predicted miRNAs have been verified by experimental reports.

Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs

BACKGROUND

MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model.

RESULTS

Instead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of "disease modules" in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable, performing well when using both experimentally validated and predicted miRNA-target gene interaction data for network construction. Finally, using RWRMTN, we identified 76 novel miRNAs associated with 23 disease phenotypes which were present in a recent database of known disease-miRNA associations.

CONCLUSIONS

Summarizing, using random walks on mutual miRNA-target networks improves the prediction of novel disease-associated miRNAs because of the existence of "disease modules" in these networks.

Collective Prediction of Disease-Associated miRNAs Based on Transduction Learning

The discovery of human disease-related miRNA is a challenging problem for complex disease biology research. For existing computational methods, it is difficult to achieve excellent performance with sparse known miRNA-disease association verified by biological experiment. Here, we develop CPTL, a Collective Prediction based on Transduction Learning, to systematically prioritize miRNAs related to disease. By combining disease similarity, miRNA similarity with known miRNA-disease association, we construct a miRNA-disease network for predicting miRNA-disease association. Then, CPTL calculates relevance score and updates the network structure iteratively, until a convergence criterion is reached. The relevance score of node including miRNA and disease is calculated by the use of transduction learning based on its neighbors. The network structure is updated using relevance score, which increases the weight of important links. To show the effectiveness of our method, we compared CPTL with existing methods based on HMDD datasets. Experimental results indicate that CPTL outperforms existing approaches in terms of AUC, precision, recall, and F1-score. Moreover, experiments performed with different number of iterations verify that CPTL has good convergence. Besides, it is analyzed that the varying of weighted parameters affect predicted results. Case study on breast cancer has further confirmed the identification ability of CPTL.