miRwayDB: a database for experimentally validated microRNA-pathway associations in pathophysiological conditions

Association of Genetic Polymorphisms in Long Noncoding RNA HOTTIP with Risk of Idiopathic Recurrent Spontaneous Abortion

The clustered homeobox gene family known as the Hox family plays a fundamental role in the morphogenesis of the vertebrate's embryo. A long noncoding RNA (lncRNA), known as HOTTIP (HOXA transcript at the distal tip), has been functionally characterized and contributed to the pathogenesis of various conditions. The current case-control study was undertaken to examine the gene frequencies and shared alleles of the HOTTIP  gene in Iranian participants with or without idiopathic recurrent spontaneous abortion (RSA). Both ARMS-PCR reaction and RFLP-PCR techniques were employed to detect three HOTTIP polymorphisms (rs2023843C/T, rs78248039A/T, and rs1859168C/A) in a DNA sample of 161 women with RSA and 177 healthy women. We found that the TT genotype of the HOTTIP rs2023843 C/T polymorphism was associated with a lower risk for idiopathic RSA. In contrast, the TT genotype of the HOTTIP rs78248039 A/T polymorphism was correlated with an enhanced risk of RSA. The presence of the A-allele for HOTTIP rs1859168 C/A polymorphism was associated with an increased risk for idiopathic RSA. Haplotype analysis showed that the T/T/A, C/T/A, T/T/C, and T/A/A haplotypes of rs2023843/rs78248039/rs1859168 enhanced RSA susceptibility. Computational analysis predicted that this lncRNA might act as a potential sponge for some microRNAs; therefore, affecting the expression of genes being targeted by them. In addition, both rs2023843 and rs1859168 variants could alter the local secondary structure of HOTTIP. Our results showed that HOTTIP rs2023843C/T, rs78248039A/T, and rs1859168C/A polymorphisms may confer genetic susceptibility to idiopathic RSA in an Iranian population.

Decoding the Non-coding: Tools and Databases Unveiling the Hidden World of "Junk" RNAs for Innovative Therapeutic Exploration

Non-coding RNAs are pivotal regulators of gene and protein expression, exerting crucial influences on diverse biological processes. Their dysregulation is frequently implicated in the onset and progression of diseases, notably cancer. A profound comprehension of the intricate mechanisms governing ncRNAs is imperative for devising innovative therapeutic interventions against these debilitating conditions. Significantly, nearly 80% of our genome comprises ncRNAs, underscoring their centrality in cellular processes. The elucidation of ncRNA functions is pivotal for grasping the complexities of gene regulation and its implications for human health. Modern genome sequencing techniques yield vast datasets, stored in specialized databases. To harness this wealth of information and to understand the crosstalk of non-coding RNAs, knowledge of available databases is required, and many new sophisticated computational tools have emerged. These tools play a pivotal role in the identification, prediction, and annotation of ncRNAs, thereby facilitating their experimental validation. This Review succinctly outlines the current understanding of ncRNAs, emphasizing their involvement in disease development. It also highlights the databases and tools instrumental in classifying, annotating, and evaluating ncRNAs. By extracting meaningful biological insights from seemingly "junk" data, these tools empower scientists to unravel the intricate roles of ncRNAs in shaping human health.

A Survey of Deep Learning for Detecting miRNA- Disease Associations: Databases, Computational Methods, Challenges, and Future Directions

MicroRNAs (miRNAs) are an important class of non-coding RNAs that play an essential role in the occurrence and development of various diseases. Identifying the potential miRNA-disease associations (MDAs) can be beneficial in understanding disease pathogenesis. Traditional laboratory experiments are expensive and time-consuming. Computational models have enabled systematic large-scale prediction of potential MDAs, greatly improving the research efficiency. With recent advances in deep learning, it has become an attractive and powerful technique for uncovering novel MDAs. Consequently, numerous MDA prediction methods based on deep learning have emerged. In this review, we first summarize publicly available databases related to miRNAs and diseases for MDA prediction. Next, we outline commonly used miRNA and disease similarity calculation and integration methods. Then, we comprehensively review the 48 existing deep learning-based MDA computation methods, categorizing them into classical deep learning and graph neural network-based techniques. Subsequently, we investigate the evaluation methods and metrics that are frequently used to assess MDA prediction performance. Finally, we discuss the performance trends of different computational methods, point out some problems in current research, and propose 9 potential future research directions. Data resources and recent advances in MDA prediction methods are summarized in the GitHub repository https://github.com/sheng-n/DL-miRNA-disease-association-methods.

Hepatitis B Virus and microRNAs: A Bioinformatics Approach

In recent decades, microRNAs (miRNAs) have emerged as key regulators of gene expression, and the identification of viral miRNAs (v-miRNAs) within some viruses, including hepatitis B virus (HBV), has attracted significant attention. HBV infections often progress to chronic states (CHB) and may induce fibrosis/cirrhosis and hepatocellular carcinoma (HCC). The presence of HBV can dysregulate host miRNA expression, influencing several biological pathways, such as apoptosis, innate and immune response, viral replication, and pathogenesis. Consequently, miRNAs are considered a promising biomarker for diagnostic, prognostic, and treatment response. The dynamics of miRNAs during HBV infection are multifaceted, influenced by host variability and miRNA interactions. Given the ability of miRNAs to target multiple messenger RNA (mRNA), understanding the viral-host (human) interplay is complex but essential to develop novel clinical applications. Therefore, bioinformatics can help to analyze, identify, and interpret a vast amount of miRNA data. This review explores the bioinformatics tools available for viral and host miRNA research. Moreover, we introduce a brief overview focusing on the role of miRNAs during HBV infection. In this way, this review aims to help the selection of the most appropriate bioinformatics tools based on requirements and research goals.

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

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

Updated review of advances in microRNAs and complex diseases: experimental results, databases, webservers and data fusion

MicroRNAs (miRNAs) are gene regulators involved in the pathogenesis of complex diseases such as cancers, and thus serve as potential diagnostic markers and therapeutic targets. The prerequisite for designing effective miRNA therapies is accurate discovery of miRNA-disease associations (MDAs), which has attracted substantial research interests during the last 15 years, as reflected by more than 55 000 related entries available on PubMed. Abundant experimental data gathered from the wealth of literature could effectively support the development of computational models for predicting novel associations. In 2017, Chen et al. published the first-ever comprehensive review on MDA prediction, presenting various relevant databases, 20 representative computational models, and suggestions for building more powerful ones. In the current review, as the continuation of the previous study, we revisit miRNA biogenesis, detection techniques and functions; summarize recent experimental findings related to common miRNA-associated diseases; introduce recent updates of miRNA-relevant databases and novel database releases since 2017, present mainstream webservers and new webserver releases since 2017 and finally elaborate on how fusion of diverse data sources has contributed to accurate MDA prediction.

CmirC: an integrated database of clustered miRNAs co-localized with copy number variations in cancer

Genomic rearrangements and copy number variations (CNVs) are the major regulators of clustered microRNAs (miRNAs) expression. Several clustered miRNAs are harbored in and around chromosome fragile sites (CFSs) and cancer-associated genomic hotspots. Aberrant expression of such clusters can lead to oncogenic or tumor suppressor activities. Here, we developed CmirC (Clustered miRNAs co-localized with CNVs), a comprehensive database of clustered miRNAs co-localized with CNV regions. The database consists of 481 clustered miRNAs co-localized with CNVs and their expression patterns in 35 cancer types of the TCGA. The portal also provides information on CFSs, miRNA cluster candidates, genomic coordinates, target gene networks, and gene functionality. The web portal is integrated with advanced tools such as JBrowse, NCBI-BLAST, GeneSCF, visNetwork, and NetworkD3 to help the researchers in data analysis, visualization, and browsing. This portal provides a promising avenue for integrated data analytics and offers additional evidence for the complex regulation of clustered miRNAs in cancer. The web portal is freely accessible at http://slsdb.manipal.edu/cmirclust to explore clinically significant miRNAs.

Genetic Polymorphisms in miR-137 and Its Target Genes, TCF4 and CACNA1C, Contribute to the Risk of Bipolar Disorder: A Preliminary Case-Control Study and Bioinformatics Analysis

Accumulating evidence has suggested that miR-137 and its target genes, CACNA1C, and TCF4, are amongst the most robustly implicated genes in psychiatric disorders. This preliminary study is aimed at investigating the effects of genetic variations in miR-137 (rs1625579A/C), TCF4 (rs1261084C/T), and CACNA1C (rs10774053A/G and rs10466907G/T) on BD susceptibility. We recruited 252 BD patients and 213 healthy subjects as the control group. Genotyping was performed using PCR-RFLP and ARMS-PCR methods. Enhanced risk of BD was found under the codominant homozygous, dominant, and allelic models of TCF4 rs1261084C/T, codominant homozygous and allelic models of CACNA1C rs10466907G/T polymorphisms, as well as codominant homozygous, dominant, recessive, and allelic models of the CACNA1C rs10774053A/G. Moreover, both TT/AG/GT/AA and TT/GG/GT/AC genotype combinations strongly increased the risk of BD in the participants. The bioinformatics analyses revealed that rs1261084C/T and rs10466907G/T created and disrupted binding sites of some miRNAs in the 3'-untranslated region of TCF4 and CACNA1C genes. In contrast, the rs10774053A/G created a new binding site for a major splicing factor and might have an effective role in the function of the CACNA1C protein. We have found that all the studied SNPs are positively associated with BD susceptibility. Replicated studies on different ethnicities are required to confirm these findings.

HT-SELEX-based identification of binding pre-miRNA hairpin-motif for small molecules

Selective targeting of biologically relevant RNAs with small molecules is a long-standing challenge due to the lack of clear understanding of the binding RNA motifs for small molecules. The standard SELEX procedure allows the identification of specific RNA binders (aptamers) for the target of interest. However, more effort is needed to identify and characterize the sequence-structure motifs in the aptamers important for binding to the target. Herein, we described a strategy integrating high-throughput (HT) sequencing with conventional SELEX followed by bioinformatic analysis to identify aptamers with high binding affinity and target specificity to unravel the sequence-structure motifs of pre-miRNA, which is essential for binding to the recently developed new water-soluble small-molecule CMBL3aL. To confirm the fidelity of this approach, we investigated the binding of CMBL3aL to the identified motifs by surface plasmon resonance (SPR) spectroscopy and its potential regulatory activity on dicer-mediated cleavage of the obtained aptamers and endogenous pre-miRNAs comprising the identified motif in its hairpin loop. This new approach would significantly accelerate the identification process of binding sequence-structure motifs of pre-miRNA for the compound of interest and would contribute to increase the spectrum of biomedical application.

Potential COVID-19 therapeutic approaches targeting angiotensin-converting enzyme 2; An updated review

COVID-19 has spread swiftly throughout the world posing a global health emergency. The significant numbers of deaths attributed to this pandemic have researchers battling to understand this new, dangerous virus. Researchers are looking to find possible treatment regimens and develop effective therapies. This study aims to provide an overview of published scientific information on potential treatments, emphasizing angiotensin-converting enzyme II (ACE2) inhibitors as one of the most important drug targets. SARS-CoV-2 receptor-binding domain (RBD); as a viral attachment or entry inhibitor against SARS-CoV-2, human recombinant soluble ACE2; as a genetically modified soluble form of ACE2 to compete with membrane-bound ACE2, and microRNAs (miRNAs); as a negative regulator of the expression of ACE2/TMPRSS2 to inhibit SARS-CoV2 entry into cells, are the potential therapeutic approaches discussed thoroughly in this article. This review provides the groundwork for the ongoing development of therapeutic agents and effective treatments against SARS-COV-2.

The Emerging Role of Long Non-Coding RNAs and MicroRNAs in Neurodegenerative Diseases: A Perspective of Machine Learning

Neurodegenerative diseases (NDs) are characterized by progressive neuronal dysfunction and death of brain cells population. As the early manifestations of NDs are similar, their symptoms are difficult to distinguish, making the timely detection and discrimination of each neurodegenerative disorder a priority. Several investigations have revealed the importance of microRNAs and long non-coding RNAs in neurodevelopment, brain function, maturation, and neuronal activity, as well as its dysregulation involved in many types of neurological diseases. Therefore, the expression pattern of these molecules in the different NDs have gained significant attention to improve the diagnostic and treatment at earlier stages. In this sense, we gather the different microRNAs and long non-coding RNAs that have been reported as dysregulated in each disorder. Since there are a vast number of non-coding RNAs altered in NDs, some sort of synthesis, filtering and organization method should be applied to extract the most relevant information. Hence, machine learning is considered as an important tool for this purpose since it can classify expression profiles of non-coding RNAs between healthy and sick people. Therefore, we deepen in this branch of computer science, its different methods, and its meaningful application in the diagnosis of NDs from the dysregulated non-coding RNAs. In addition, we demonstrate the relevance of machine learning in NDs from the description of different investigations that showed an accuracy between 85% to 95% in the detection of the disease with this tool. All of these denote that artificial intelligence could be an excellent alternative to help the clinical diagnosis and facilitate the identification diseases in early stages based on non-coding RNAs.

Decrypting the role of predicted SARS-CoV-2 miRNAs in COVID-19 pathogenesis: A bioinformatics approach

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is a highly transmissible virus causing the ongoing global pandemic, COVID-19. Evidence suggests that viral and host microRNAs play pivotal roles in progression of such infections. The decisive impact of viral miRNAs and their putative targets in modulating the transcriptomic profile of its host, however remains unexplored. We hypothesized that the SARS-CoV-2 derived miRNAs can potentially play a contributory role in its pathogenicity and aid in its survival. A series of computational tools predicted 34 SARS-CoV-2 encoded miRNAs and their putative targets in the host. Immune and apoptotic pathways were identified as most enriched pathways. Further investigation using a dataset of SARS-CoV-2 infected cells (available from public repository- GSE150392) revealed that 46 genes related to immune and apoptosis-related functions were deregulated. Of these 46 genes, 42 genes were identified to be significantly up-regulated and 4 genes were down-regulated. In silico analysis revealed all of the these significantly down-regulated genes to be putative targets of 9 out of 34 of our predicted viral miRNAs. Overall, 123 out of 324 genes that are differentially regulated in SARS-CoV2 infected cells, and also identified as putative targets of viral miRNAs, were found to be significantly down-regulated. KEGG pathway analysis using these genes revealed p53 signaling as the most enriched pathway – a pathway that is known to influence immune responses. This study thus provides the theoretical foundation for the underlying molecular mechanisms involved in progression of viral pathogenesis.

RSCMDA: Prediction of Potential miRNA-Disease Associations Based on a Robust Similarity Constraint Learning Method

With the rapid development of biotechnology and computer technology, increasing studies have shown that the occurrence of many diseases in the human body is closely related to the dysfunction of miRNA, and the relationship between them has become a new research hotspot. Exploring disease-related miRNAs information provides a new perspective for understanding the etiology and pathogenesis of diseases. In this study, we proposed a new method based on similarity constrained learning (RSCMDA) to infer disease-associated miRNAs. Considering the problems of noise and incomplete information in current biological datasets, we designed a new framework RSCMDA, which can learn a new disease similarity network and miRNA similarity network based on the existing biological information, and then update the predicted miRNA-disease associations using robust similarity constraint learning method. Consequently, the AUC scores obtained in the global and local cross-validation of RSCMDA are 0.9465 and 0.8494, respectively, which are superior to the other methods. Besides, the prediction performance of RSCMDA is further confirmed by the case study on lung Neoplasms, because 94% of the top 50 miRNAs predicted by the RSCMDA method are confirmed from the existing biological databases or research results. All the results show that RSCMDA is a reliable and effective framework, which can be used as new technology to explore the relationship between miRNA and disease.

emiRIT: a text-mining-based resource for microRNA information

microRNAs (miRNAs) are essential gene regulators, and their dysregulation often leads to diseases. Easy access to miRNA information is crucial for interpreting generated experimental data, connecting facts across publications and developing new hypotheses built on previous knowledge. Here, we present extracting miRNA Information from Text (emiRIT), a text-miningbased resource, which presents miRNA information mined from the literature through a user-friendly interface. We collected 149 ,233 miRNA –PubMed ID pairs from Medline between January 1997 and May 2020. emiRIT currently contains ‘miRNA –gene regulation’ (69 ,152 relations), ‘miRNA disease (cancer)’ (12 ,300 relations), ‘miRNA –biological process and pathways’ (23, 390 relations) and circulatory ‘miRNAs in extracellular locations’ (3782 relations). Biological entities and their relation to miRNAs were extracted from Medline abstracts using publicly available and in-house developed text-mining tools, and the entities were normalized to facilitate querying and integration. We built a database and an interface to store and access the integrated data, respectively. We provide an up-to-date and user-friendly resource to facilitate access to comprehensive miRNA information from the literature on a large scale, enabling users to navigate through different roles of miRNA and examine them in a context specific to their information needs. To assess our resource’s information coverage, we have conducted two case studies focusing on the target and differential expression information of miRNAs in the context of cancer and a third case study to assess the usage of emiRIT in the curation of miRNA information.

Database URL: https://research.bioinformatics.udel.edu/emirit/

MicroRNA expression patterns in HbE/β-thalassemia patients: The passwords to unlock fetal hemoglobin expression in β-hemoglobinopathies

Hemoglobin E (HbE)/β-thalassemia is a form of β-hemoglobinopathy that is well-known for its clinical heterogeneity. Individuals suffering from this condition are often found to exhibit increased fetal hemoglobin (HbF) levels - a factor that may contribute to their reduced blood transfusion requirements. This study hypothesized that the high HbF levels in HbE/β-thalassemia individuals may be guided by microRNAs and explored their involvement in the disease pathophysiology. The miRNA expression profile of hematopoietic progenitor cells in HbE/β-thalassemia patients was investigated and compared with that of healthy controls. Using miRNA PCR array experiments, eight miRNAs (hsa-miR-146a-5p, hsa-miR-146b-5p, hsa-miR-148b-3p, hsa-miR-155-5p, hsa-miR-192-5p, hsa-miR-335-5p, hsa-miR-7-5p, hsa-miR-98-5p) were identified to be significantly up-regulated whereas four miRNAs (hsa-let-7a-5p, hsa-miR-320a, hsa-let-7b-5p, hsa-miR-92a-3p) were significantly down-regulated. Target analysis found them to be associated with several biological processes and molecular functions including MAPK and HIF-1 signaling pathways - the pathways known to be associated with HbF upregulation. Results of dysregulated miRNAs further indicated that miR-17/92 cluster might be of critical importance in HbF regulation. The findings of our study thus identify key miRNAs that can be extrinsically manipulated to elevate HbF levels in β-hemoglobinopathies.

An association study of polymorphisms in the H19 imprinted gene in an Iranian population with the risk of polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is one of the most common endocrinopathies that causes problems in female fertility at the reproductive age. PCOS is a multifactorial disease, with genetic factors playing a crucial role in its development. H19 is a long non-coding RNA (lncRNA) expressed from the maternal chromosome, which is correlated with PCOS. In this study, 115 women suffering from PCOS and 130 healthy women with regular menstrual cycles were recruited as case and control groups, respectively. After the extraction of genomic DNA, the restriction fragment length polymorphism polymerase chain reaction was employed for genotyping of rs2067051G>A and rs3741219T>C. Statistical analysis was done using SPSS package V.22 for Windows. In silico analysis was recruited to determine the effects of SNPs on the secondary structure of gene transcript as well as miRNA binding sites. The obtained data showed that the A allele of rs2067051G>A was associated with the high risk of PCOS (OR = 2.00, 95%CI = 1.38-2.91, P = 0.00). AG and AA genotypes led to a 3.64- and (about) a five-fold increase in the risk of PCOS, respectively (95%CI = 2.02-6.54, P = 0.00, and 95%CI = 1.51-16.52, P = 0.00, respectively). These variants caused a significant increase in the risk of this disorder in all genotype models except in the recessive model. However, no association was found between rs3741219T>C and the increased risk of PCOS, either in the allele or in the genotype models. According to the findings, rs2067051G>A is associated with an increased risk of PCOS in the Iranian population.

Systematics for types and effects of RNA variations

Systematics is described for annotation of variations in RNA molecules. The conceptual framework is part of Variation Ontology (VariO) and facilitates depiction of types of variations, their functional and structural effects and other consequences in any RNA molecule in any organism. There are more than 150 RNA related VariO terms in seven levels, which can be further combined to generate even more complicated and detailed annotations. The terms are described together with examples, usually for variations and effects in human and in diseases. RNA variation type has two subcategories: variation classification and origin with subterms. Altogether six terms are available for function description. Several terms are available for affected RNA properties. The ontology contains also terms for structural description for affected RNA type, post-transcriptional RNA modifications, secondary and tertiary structure effects and RNA sugar variations. Together with the DNA and protein concepts and annotations, RNA terms allow comprehensive description of variations of genetic and non-genetic origin at all possible levels. The VariO annotations are readable both for humans and computer programs for advanced data integration and mining.

Khademul Islam AB. Perversely expressed long noncoding RNAs can alter host response and viral proliferation in SARS-CoV-2 infection

BACKGROUND

Regulatory roles of long noncoding RNAs (lncRNAs) during viral infection has become more evident in last decade, but are yet to be explored for SARS-CoV-2.

MATERIALS & METHODS

We analyzed RNA-seq dataset of SARS-CoV-2 infected lung epithelial cells to identify differentially expressed genes.

RESULTS

Our analyses uncover 21 differentially expressed lncRNAs broadly involved in cell survival and regulation of gene expression. These lncRNAs can directly interact with six differentially expressed protein-coding genes, and ten host genes that interact with SARS-CoV-2 proteins. Also, they can block the suppressive effect of nine microRNAs induced in viral infections.

CONCLUSION

Our investigation determines that deregulated lncRNAs in SARS-CoV-2 infection are involved in viral proliferation, cellular survival, and immune response, ultimately determining disease outcome.

Circulating MicroRNAs from Serum Exosomes May Serve as a Putative Biomarker in the Diagnosis and Treatment of Patients with Focal Cortical Dysplasia

Focal cortical dysplasia (FCD) is a congenital malformation of cortical development where the cortical neurons located in the brain area fail to migrate in the proper formation. Epilepsy, particularly medically refractory epilepsy, is the most common clinical presentation for all types of FCD. This study aimed to explore the expression change of circulating miRNAs in patients with FCD from serum exosomes. A total of nine patients with FCD and four healthy volunteers were enrolled in this study. The serum exosomes were isolated from the peripheral blood of the subjects. Transmission electron microscopy (TEM) was used to identify the exosomes. Both exosomal markers and neuronal markers were detected by Western blotting analysis to prove that we could obtain central nervous system-derived exosomes from the circulation. The expression profiles of circulating exosomal miRNAs were assessed using next-generation sequencing analysis (NGS). We obtained a total of 107 miRNAs with dominant fold change (>2-fold) from both the annotated 5p-arm and 3p-arm of 2780 mature miRNAs. Based on the integrated platform of HMDD v3.2, miRway DB and DIANA-miRPath v3.0 online tools, and confirmed by MiRBase analysis, four potentially predicted miRNAs from serum exosomes in patients with FCD were identified, including miR194-2-5p, miR15a-5p, miR-132-3p, and miR-145-5p. All four miRNAs presented upregulated expression in patients with FCD compared with controls. Through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and pathway category of four target miRNAs, we found eight possible signaling pathways that may be related to FCD. Among them, we suggest that the mTOR signaling pathway, PI3K-Akt signaling pathway, p53 signaling pathway, and cell cycle regulation and TGF-beta signaling pathway are high-risk pathways that play a crucial role in the pathogenesis of FCD and refractory epilepsy. Our results suggest that the circulating miRNAs from exosomes may provide a potential biomarker for diagnostic, prognostic, and therapeutic adjuncts in patients with FCD and refractory epilepsy.

mirtronDB: a mirtron knowledge base

Motivation

Mirtrons arise from short introns with atypical cleavage by using the splicing mechanism. In the current literature, there is no repository centralizing and organizing the data available to the public. To fill this gap, we developed mirtronDB, the first knowledge database dedicated to mirtron, and it is available at http://mirtrondb.cp.utfpr.edu.br/. MirtronDB currently contains a total of 1407 mirtron precursors and 2426 mirtron mature sequences in 18 species.

Results

Through a user-friendly interface, users can now browse and search mirtrons by organism, organism group, type and name. MirtronDB is a specialized resource that provides free and user-friendly access to knowledge on mirtron data.

Availability and implementation

MirtronDB is available at http://mirtrondb.cp.utfpr.edu.br/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Frequency of MicroRNA Response Elements Identifies Pathologically Relevant Signaling Pathways in Triple-Negative Breast Cancer

Complex interactions between mRNAs and microRNAs influence cellular functions. The mRNA-microRNA interactions also determine the post-transcriptional availability of mRNAs and unbound microRNAs. MicroRNAs binds to one or more microRNA response elements (MREs) located on the 3′UTR of mRNAs. In this study, we leveraged MREs and their frequencies in cancer and matched normal tissues to obtain insights into disease-specific interactions between mRNAs and microRNAs. We developed a bioinformatics method “ReMIx” that utilizes RNA sequencing (RNA-Seq) data to quantify MRE frequencies across the transcriptome. We applied ReMIx to triple-negative (TN) breast cancer tumor-normal adjacent pairs and identified MREs specific to TN tumors. ReMIx identified candidate mRNAs and microRNAs in the MAPK signaling cascade. Further analysis of MAPK gene regulatory networks revealed microRNA partners that influence and modulate MAPK signaling. In conclusion, we demonstrate a novel method of using MREs in the identification of functionally relevant mRNA-microRNA interactions in TN breast cancer.

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Bioinformatics method ReMIx identify differential microRNA response rlements (MRE)

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Tumor-specific MREs frequency observed in triple-negative breast cancer (TNBC)

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MRE analysis identify MAPK signaling genes as therapeutic target for TNBC

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MREs frequency can be used to identify pathologically relevant pathways

IMPMD: An Integrated Method for Predicting Potential Associations Between miRNAs and Diseases

Background

With the rapid development of biological research, microRNAs (miRNAs) have increasingly attracted worldwide attention. The increasing biological studies and scientific experiments have proven that miRNAs are related to the occurrence and development of a large number of key biological processes which cause complex human diseases. Thus, identifying the association between miRNAs and disease is helpful to diagnose the diseases. Although some studies have found considerable associations between miRNAs and diseases, there are still a lot of associations that need to be identified. Experimental methods to uncover miRNA-disease associations are time-consuming and expensive. Therefore, effective computational methods are urgently needed to predict new associations.

Methodology

In this work, we propose an integrated method for predicting potential associations between miRNAs and diseases (IMPMD). The enhanced similarity for miRNAs is obtained by combination of functional similarity, gaussian similarity and Jaccard similarity. To diseases, it is obtained by combination of semantic similarity, gaussian similarity and Jaccard similarity. Then, we use these two enhanced similarities to construct the features and calculate cumulative score to choose robust features. Finally, the general linear regression is applied to assign weights for Support Vector Machine, K-Nearest Neighbor and Logistic Regression algorithms.

Results

IMPMD obtains AUC of 0.9386 in 10-fold cross-validation, which is better than most of the previous models. To further evaluate our model, we implement IMPMD on two types of case studies for lung cancer and breast cancer. 49 (Lung Cancer) and 50 (Breast Cancer) out of the top 50 related miRNAs are validated by experimental discoveries.

Conclusion

We built a software named IMPMD which can be freely downloaded from https://github.com/Sunmile/IMPMD.

Intragenic MicroRNAs Autoregulate Their Host Genes in Both Direct and Indirect Ways-A Cross-Species Analysis

MicroRNAs (miRNAs) function as master switches for post-transcriptional gene expression. Their genes are either located in the extragenic space or within host genes, but these intragenic miRNA::host gene interactions are largely enigmatic. The aim of this study was to investigate the location and co-regulation of all to date available miRNA sequences and their host genes in an unbiased computational approach. The majority of miRNAs were located within intronic regions of protein-coding and non-coding genes. These intragenic miRNAs exhibited both increased target probability as well as higher target prediction scores as compared to a model of randomly permutated genes. This was associated with a higher number of miRNA recognition elements for the hosted miRNAs within their host genes. In addition, strong indirect autoregulation of host genes through modulation of functionally connected gene clusters by intragenic miRNAs was demonstrated. In addition to direct miRNA-to-host gene targeting, intragenic miRNAs also appeared to interact with functionally related genes, thus affecting their host gene function through an indirect autoregulatory mechanism. This strongly argues for the biological relevance of autoregulation not only for the host genes themselves but, more importantly, for the entire gene cluster interacting with the host gene.

Gene set analysis methods for the functional interpretation of non-mRNA data—Genomic range and ncRNA data

Gene set analysis (GSA) is one of the methods of choice for analyzing the results of current omics studies; however, it has been mainly developed to analyze mRNA (microarray, RNA-Seq) data. The following review includes an update regarding general methods and resources for GSA and then emphasizes GSA methods and tools for non-mRNA omics datasets, specifically genomic range data (ChIP-Seq, SNP and methylation) and ncRNA data (miRNAs, lncRNAs and others). In the end, the state of the GSA field for non-mRNA datasets is discussed, and some current challenges and trends are highlighted, especially the use of network approaches to face complexity issues.

miRDRN-miRNA disease regulatory network: a tool for exploring disease and tissue-specific microRNA regulatory networks

BACKGROUND

MicroRNA (miRNA) regulates cellular processes by acting on specific target genes, and cellular processes proceed through multiple interactions often organized into pathways among genes and gene products. Hundreds of miRNAs and their target genes have been identified, as are many miRNA-disease associations. These, together with huge amounts of data on gene annotation, biological pathways, and protein-protein interactions are available in public databases. Here, using such data we built a database and web service platform, miRNA disease regulatory network (miRDRN), for users to construct disease and tissue-specific miRNA-protein regulatory networks, with which they may explore disease related molecular and pathway associations, or find new ones, and possibly discover new modes of drug action.

METHODS

Data on disease-miRNA association, miRNA-target association and validation, gene-tissue association, gene-tumor association, biological pathways, human protein interaction, gene ID, gene ontology, gene annotation, and product were collected from publicly available databases and integrated. A large set of miRNA target-specific regulatory sub-pathways (RSPs) having the form (T, G 1, G 2) was built from the integrated data and stored, where T is a miRNA-associated target gene, G 1 (G 2) is a gene/protein interacting with T (G 1). Each sequence (T, G 1, G 2) was assigned a p-value weighted by the participation of the three genes in molecular interactions and reaction pathways.

RESULTS

A web service platform, miRDRN (http://mirdrn.ncu.edu.tw/mirdrn/), was built. The database part of miRDRN currently stores 6,973,875 p-valued RSPs associated with 116 diseases in 78 tissue types built from 207 diseases-associated miRNA regulating 389 genes. miRDRN also provides facilities for the user to construct disease and tissue-specific miRNA regulatory networks from RSPs it stores, and to download and/or visualize parts or all of the product. User may use miRDRN to explore a single disease, or a disease-pair to gain insights on comorbidity. As demonstrations, miRDRN was applied: to explore the single disease colorectal cancer (CRC), in which 26 novel potential CRC target genes were identified; to study the comorbidity of the disease-pair Alzheimer's disease-Type 2 diabetes, in which 18 novel potential comorbid genes were identified; and, to explore possible causes that may shed light on recent failures of late-phase trials of anti-AD, BACE1 inhibitor drugs, in which genes downstream to BACE1 whose suppression may affect signal transduction were identified.

A novel semi-supervised model for miRNA-disease association prediction based on ℓ 1 -norm graph

BACKGROUND

Identification of miRNA-disease associations has attracted much attention recently due to the functional roles of miRNAs implicated in various biological and pathological processes. Great efforts have been made to discover the potential associations between miRNAs and diseases both experimentally and computationally. Although reliable, the experimental methods are in general time-consuming and labor-intensive. In comparison, computational methods are more efficient and applicable to large-scale datasets.

METHODS

In this paper, we propose a novel semi-supervised model to predict miRNA-disease associations viaℓ 1 -norm graph. Specifically, we first recalculate the miRNA functional similarities as well as the disease semantic similarities based on the latest version of MeSH descriptors and HMDD. We then update the similarity matrices and association matrix iteratively in both miRNA space and disease space. The optimized association matrices from each space are combined together as the final output.

RESULTS

Compared with four state-of-the-art prediction methods, our method achieved favorable performance with AUCs of 0.943 and 0.946 in both global LOOCV and local LOOCV, respectively. In addition, we carried out three types of case studies on five common human diseases, and most of the top 50 predicted miRNAs were confirmed to be associated with the investigated diseases by four databases dbDEMC, PheomiR, miR2Disease and miRwayDB. Specifically, our results provided potential evidence that miRNAs within the same family or cluster were likely to play functional roles together in given diseases.

CONCLUSIONS

Taken together, the experimental results clearly demonstrated the utility of the proposed method. We anticipated that our method could serve as a reliable and efficient tool for miRNA-disease association prediction.

LLCMDA: A Novel Method for Predicting miRNA Gene and Disease Relationship Based on Locality-Constrained Linear Coding

MiRNAs are small non-coding regulatory RNAs which are associated with multiple diseases. Increasing evidence has shown that miRNAs play important roles in various biological and physiological processes. Therefore, the identification of potential miRNA-disease associations could provide new clues to understanding the mechanism of pathogenesis. Although many traditional methods have been successfully applied to discover part of the associations, they are in general time-consuming and expensive. Consequently, computational-based methods are urgently needed to predict the potential miRNA-disease associations in a more efficient and resources-saving way. In this paper, we propose a novel method to predict miRNA-disease associations based on Locality-constrained Linear Coding (LLC). Specifically, we first reconstruct similarity networks for both miRNAs and diseases using LLC and then apply label propagation on the similarity networks to get relevant scores. To comprehensively verify the performance of the proposed method, we compare our method with several state-of-the-art methods under different evaluation metrics. Moreover, two types of case studies conducted on two common diseases further demonstrate the validity and utility of our method. Extensive experimental results indicate that our method can effectively predict potential associations between miRNAs and diseases.

GLNMDA: a novel method for miRNA-disease association prediction based on global linear neighborhoods

Recently, increasing studies have shown that miRNAs are involved in the development and progression of various complex diseases. Consequently, predicting potential miRNA-disease associations makes an important contribution to understanding the pathogenesis of diseases, developing new drugs as well as designing individualized diagnostic and therapeutic approaches for different human diseases. Nonetheless, the inherent noise and incompleteness in the existing biological datasets have limited the prediction accuracy of current computational models. To solve this issue, in this paper, we propose a novel method for miRNA-disease association prediction based on global linear neighborhoods (GLNMDA). Specifically, our method obtains a new miRNA/disease similarity matrix by linearly reconstructing each miRNA/disease according to the known experimentally verified miRNA-disease associations. We then adopt label propagation to infer the potential associations between miRNAs and diseases. As a result, GLNMDA achieved reliable performance in the frameworks of both local and global LOOCV (AUCs of 0.867 and 0.929, respectively) and 5-fold cross validation (average AUC of 0.926). Case studies on five common human diseases further confirmed the utility of our method in discovering latent miRNA-disease pairs. Taken together, GLNMDA could serve as a reliable computational tool for miRNA-disease association prediction.