Arena-Idb: a platform to build human non-coding RNA interaction networks

LINC01137/miR-186-5p/WWOX: a novel axis identified from WWOX-related RNA interactome in bladder cancer

Introduction: The discovery of non-coding RNA (ncRNA) dates back to the pre-genomics era, but the progress in this field is still dynamic and leverages current post-genomics solutions. WWOX is a global gene expression modulator that is scarcely investigated for its role in regulating cancer-related ncRNAs. In bladder cancer (BLCA), the link between WWOX and ncRNA remains unexplored. The description of AP-2α and AP-2γ transcription factors, known as WWOX-interacting proteins, is more commonplace regarding ncRNA but still merits investigation. Therefore, this in vitro and in silico study aimed to construct an ncRNA-containing network with WWOX/AP-2 and to investigate the most relevant observation in the context of BLCA cell lines and patients. Methods: RT-112, HT-1376, and CAL-29 cell lines were subjected to two stable lentiviral transductions. High-throughput sequencing of cellular variants (deposited in the Gene Expression Omnibus database under the GSE193659 record) enabled the investigation of WWOX/AP-2-dependent differences using various bioinformatics tools (e.g., limma-voom, FactoMineR, multiple Support Vector Machine Recursive Feature Elimination (mSVM-RFE), miRDB, Arena-Idb, ncFANs, RNAhybrid, TargetScan, Protein Annotation Through Evolutionary Relationships (PANTHER), Gene Transcription Regulation Database (GTRD), or Evaluate Cutpoints) and repositories such as The Cancer Genome Atlas (TCGA) and Cancer Cell Line Encyclopedia. The most relevant observations from cap analysis gene expression sequencing (CAGE-seq) were confirmed using real-time PCR, whereas TCGA data were validated using the GSE31684 cohort. Results: The first stage of the whole study justified focusing solely on WWOX rather than on WWOX combined with AP-2α/γ. The most relevant observation of the developed ncRNA-containing network was LINC01137, i.e., long non-coding RNAs (lncRNAs) that unraveled the core network containing UPF1, ZC3H12A, LINC01137, WWOX, and miR-186-5p, the last three being a novel lncRNA/miRNA/mRNA axis. Patients' data confirmed the LINC01137/miR-186-5p/WWOX relationship and provided a set of dependent genes (i.e., KRT18, HES1, VCP, FTH1, IFITM3, RAB34, and CLU). Together with the core network, the gene set was subjected to survival analysis for both TCGA-BLCA and GSE31684 patients, which indicated that the increased expression of WWOX or LINC01137 is favorable, similar to their combination with each other (WWOX↑ and LINC01137↑) or with MIR186 (WWOX↑/LINC01137↑ but MIR186↓). Conclusion: WWOX is implicated in the positive feedback loop with LINC01137 that sponges WWOX-targeting miR-186-5p. This novel WWOX-containing lncRNA/miRNA/mRNA axis should be further investigated to depict its relationships in a broader context, which could contribute to BLCA research and treatment.

SOCS3 gene silencing does not occur through methylation and mutations in gastric cancer

Gastric cancer (GC) is ranked the third leading cause of cancer-related deaths worldwide. Mutations and epigenetic alterations in several essential genes, including p53, KRAS, PIK3CA, FAT4 and ARID1A, are often reported. Furthermore, loss of SOCS3 expression was reported in GC, suggesting its tumor suppressor role. To assess the mutational and methylation status of SOCS3, we performed gene panel exome sequencing on 47 human GC samples. The SOCS3 gene was rarely mutated, suggesting alternative regulation mechanisms, such as promoter hypermethylation and/or long non-coding RNAs (lncRNAs). We first explored SOCS3 promoter methylation status in 44 human GC samples by methylation-specific PCR (MS-PCR). Thirteen out of forty-four patients (29.5%) displayed a methylation pattern. Then, to see whether SOCS3 expression is silenced by CpG methylation, we examined publicly available databases (cbioportal and The Cancer Genome Atlas (TCGA)). The analysis revealed β values lower than 0.1, indicating hypo-methylation in healthy and GC samples. Moreover, moderate methylation (β < 0.4) and high methylation (β > 0.4) did not affect the free survival, suggesting that methylation is unlikely to be the mechanism ruling SOCS3 silencing in GC. Next, to assess the regulatory effects of lncRNAs on SOCS3, we silenced the AC125807.2-lncRNA and quantified the SOCS3 gene expression in AGS and NCI-N87 gastric cancer cell line. SOCS3 was found to be downregulated following AC125807.2-lncRNA silencing in AGS cells, suggesting the potential implication of lncRNA AC125807.2 in SOCS3 regulation. However, in NCI-N87 cells, there was no significant change in SOCS3 expression. In conclusion, neither mutations nor hypermethylation was associated with the SOCS3 downregulation in GC, and alternative mechanisms, including non-coding RNAs-mediated gene silencing, may be proposed.

Integrated Analysis of Competitive Endogenous RNA Networks in Acute Ischemic Stroke

Background: Acute ischemic stroke (AIS) is a severe neurological disease with complex pathophysiology, resulting in the disability and death. The goal of this study is to explore the underlying molecular mechanisms of AIS and search for new potential biomarkers and therapeutic targets.

Methods: Integrative analysis of mRNA and miRNA profiles downloaded from Gene Expression Omnibus (GEO) was performed. We explored differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMirs) after AIS. Target mRNAs of DEMirs and target miRNAs of DEGs were predicted with target prediction tools, and the intersections between DEGs and target genes were determined. Subsequently, Gene Ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses, Gene set enrichment analysis (GSEA), Gene set variation analysis (GSVA), competitive endogenous RNA (ceRNA) (lncRNA-miRNA-mRNA) network, protein–protein interaction (PPI) network, and gene transcription factors (TFs) network analyses were performed to identify hub genes and associated pathways. Furthermore, we obtained AIS samples with evaluation of immune cell infiltration and used CIBERSORT to determine the relationship between the expression of hub genes and infiltrating immune cells. Finally, we used the Genomics of Drug Sensitivity in Cancer (GDSC) database to predict the effect of the identified targets on drug sensitivity.

Result: We identified 293 DEGs and 26 DEMirs associated with AIS. DEGs were found to be mainly enriched in inflammation and immune-related signaling pathways through enrichment analysis. The ceRNA network included nine lncRNAs, 13 miRNAs, and 21 mRNAs. We used the criterion AUC >0.8, to screen a 3-gene signature (FBL, RPS3, and RPS15) and the aberrantly expressed miRNAs (hsa-miR-125a-5p, hsa-miR-125b-5p, hsa-miR-148b-3p, and hsa-miR-143-3p) in AIS, which were verified by a method of quantitative PCR (qPCR) in HT22 cells. T cells CD8, B cells naïve, and activated NK cells had statistical increased in number compared with the acute cerebral infarction group. By predicting the IC50 of the patient to the drug, AZD0530, Z.LLNle.CHO and NSC-87877 with significant differences between the groups were screened out. AIS demonstrated heterogeneity in immune infiltrates that correlated with the occurrence and development of diseases.

Conclusion: These findings may contribute to a better understanding of the molecular mechanisms of AIS and provide the basis for the development of novel treatment targets in AIS.

Analysis of Faecal Microbiota and Small ncRNAs in Autism: Detection of miRNAs and piRNAs with Possible Implications in Host-Gut Microbiota Cross-Talk

Intestinal microorganisms impact health by maintaining gut homeostasis and shaping the host immunity, while gut dysbiosis associates with many conditions, including autism, a complex neurodevelopmental disorder with multifactorial aetiology. In autism, gut dysbiosis correlates with symptom severity and is characterised by a reduced bacterial variability and a diminished beneficial commensal relationship. Microbiota can influence the expression of host microRNAs that, in turn, regulate the growth of intestinal bacteria by means of bidirectional host-gut microbiota cross-talk. We investigated possible interactions among intestinal microbes and between them and host transcriptional modulators in autism. To this purpose, we analysed, by "omics" technologies, faecal microbiome, mycobiome, and small non-coding-RNAs (particularly miRNAs and piRNAs) of children with autism and neurotypical development. Patients displayed gut dysbiosis related to a reduction of healthy gut micro- and mycobiota as well as up-regulated transcriptional modulators. The targets of dysregulated non-coding-RNAs are involved in intestinal permeability, inflammation, and autism. Furthermore, microbial families, underrepresented in patients, participate in the production of human essential metabolites negatively influencing the health condition. Here, we propose a novel approach to analyse faeces as a whole, and for the first time, we detected miRNAs and piRNAs in faecal samples of patients with autism.

CAD increases the long noncoding RNA PUNISHER in small extracellular vesicles and regulates endothelial cell function via vesicular shuttling

Long noncoding RNAs (lncRNAs) have emerged as biomarkers and regulators of cardiovascular disease. However, the expression pattern of circulating extracellular vesicle (EV)-incorporated lncRNAs in patients with coronary artery disease (CAD) is still poorly investigated. A human lncRNA array revealed that certain EV-lncRNAs are significantly dysregulated in CAD patients. Circulating small EVs (sEVs) from patients with (n = 30) or without (n = 30) CAD were used to quantify PUNISHER (also known as AGAP2-antisense RNA 1 [AS1]), GAS5, MALAT1, and H19 RNA levels. PUNISHER (p = 0.002) and GAS5 (p = 0.02) were significantly increased in patients with CAD, compared to non-CAD patients. Fluorescent labeling and quantitative real-time PCR of sEVs demonstrated that functional PUNISHER was transported into the recipient cells. Mechanistically, the RNA-binding protein, heterogeneous nuclear ribonucleoprotein K (hnRNPK), interacts with PUNISHER, regulating its loading into sEVs. Knockdown of PUNISHER abrogated the EV-mediated effects on endothelial cell (EC) migration, proliferation, tube formation, and sprouting. Angiogenesis-related gene profiling showed that the expression of vascular endothelial growth factor A (VEGFA) RNA was significantly increased in EV recipient cells. Protein stability and RNA immunoprecipitation indicated that the PUNISHER-hnRNPK axis regulates the stability and binding of VEGFA mRNA to hnRNPK. Loss of PUNISHER in EVs abolished the EV-mediated promotion of VEGFA gene and protein expression. Intercellular transfer of EV-incorporated PUNISHER promotes a pro-angiogenic phenotype via a VEGFA-dependent mechanism.

In silico interaction of HOX cluster-embedded microRNAs and long non-coding RNAs in oral cancer

The essential role HOX-associated non-coding RNAs play in chromatin dynamics and gene regulation has been well documented. The potential roles of these microRNAs and long non-coding RNAs in oral cancer development, with their attendant involvement in various cellular processes including proliferation, invasion, migration, epithelial-mesenchymal transition and metastasis is gaining credence. An interaction network of HOX-embedded non-coding RNAs was constructed to identify the RNA interaction landscape using the arena-Idb platform and visualized using Cytoscape. The miR-10a was shown to interact with HOXA1, miR-10b with HOXD10, miR-196a1 with HOXA5, HOXA7, HOXB8, HOXC8, HOXD8, and miR-196a2 with HOXA5. The lncRNAs, HOTAIR interacted with HOXC11, HOTAIRM1 with HOXA1 and HOXA4, HOTTIP with HOXA13, HOXA-AS2 with HOXA3, HOXA11-AS with HOXA11 and HOXD-AS1 with HOXB8. Changes in the HOX cluster-embedded non-coding RNAs have implications for prognosis and overall disease survival. Our review aims to analyze the functional significance and clinical relevance of non-coding RNAs within the HOX cluster in the context of oral carcinogenesis. Elucidating these interactions between the non-coding RNAs and HOX genes in oral cancer development and progression could pave the way for the identification of reliable biomarkers and potential therapeutic targets.

An overview of graph databases and their applications in the biomedical domain

Over the past couple of decades, the explosion of densely interconnected data has stimulated the research, development and adoption of graph database technologies. From early graph models to more recent native graph databases, the landscape of implementations has evolved to cover enterprise-ready requirements. Because of the interconnected nature of its data, the biomedical domain has been one of the early adopters of graph databases, enabling more natural representation models and better data integration workflows, exploration and analysis facilities. In this work, we survey the literature to explore the evolution, performance and how the most recent graph database solutions are applied in the biomedical domain, compiling a great variety of use cases. With this evidence, we conclude that the available graph database management systems are fit to support data-intensive, integrative applications, targeted at both basic research and exploratory tasks closer to the clinic.

GRAPES-DD: exploiting decision diagrams for index-driven search in biological graph databases

Background

Graphs are mathematical structures widely used for expressing relationships among elements when representing biomedical and biological information. On top of these representations, several analyses are performed. A common task is the search of one substructure within one graph, called target. The problem is referred to as one-to-one subgraph search, and it is known to be NP-complete. Heuristics and indexing techniques can be applied to facilitate the search. Indexing techniques are also exploited in the context of searching in a collection of target graphs, referred to as one-to-many subgraph problem. Filter-and-verification methods that use indexing approaches provide a fast pruning of target graphs or parts of them that do not contain the query. The expensive verification phase is then performed only on the subset of promising targets. Indexing strategies extract graph features at a sufficient granularity level for performing a powerful filtering step. Features are memorized in data structures allowing an efficient access. Indexing size, querying time and filtering power are key points for the development of efficient subgraph searching solutions.

Results

An existing approach, GRAPES, has been shown to have good performance in terms of speed-up for both one-to-one and one-to-many cases. However, it suffers in the size of the built index. For this reason, we propose GRAPES-DD, a modified version of GRAPES in which the indexing structure has been replaced with a Decision Diagram. Decision Diagrams are a broad class of data structures widely used to encode and manipulate functions efficiently. Experiments on biomedical structures and synthetic graphs have confirmed our expectation showing that GRAPES-DD has substantially reduced the memory utilization compared to GRAPES without worsening the searching time.

Conclusion

The use of Decision Diagrams for searching in biochemical and biological graphs is completely new and potentially promising thanks to their ability to encode compactly sets by exploiting their structure and regularity, and to manipulate entire sets of elements at once, instead of exploring each single element explicitly. Search strategies based on Decision Diagram makes the indexing for biochemical graphs, and not only, more affordable allowing us to potentially deal with huge and ever growing collections of biochemical and biological structures.

The bioinformatics toolbox for circRNA discovery and analysis

Circular RNAs (circRNAs) are a unique class of RNA molecule identified more than 40 years ago which are produced by a covalent linkage via back-splicing of linear RNA. Recent advances in sequencing technologies and bioinformatics tools have led directly to an ever-expanding field of types and biological functions of circRNAs. In parallel with technological developments, practical applications of circRNAs have arisen including their utilization as biomarkers of human disease. Currently, circRNA-associated bioinformatics tools can support projects including circRNA annotation, circRNA identification and network analysis of competing endogenous RNA (ceRNA). In this review, we collected about 100 circRNA-associated bioinformatics tools and summarized their current attributes and capabilities. We also performed network analysis and text mining on circRNA tool publications in order to reveal trends in their ongoing development.

miRNet 2.0: network-based visual analytics for miRNA functional analysis and systems biology

miRNet is an easy-to-use, web-based platform designed to help elucidate microRNA (miRNA) functions by integrating users' data with existing knowledge via network-based visual analytics. Since its first release in 2016, miRNet has been accessed by >20 000 researchers worldwide, with ∼100 users on a daily basis. While version 1.0 was focused primarily on miRNA-target gene interactions, it has become clear that in order to obtain a global view of miRNA functions, it is necessary to bring other important players into the context during analysis. Driven by this concept, in miRNet version 2.0, we have (i) added support for transcription factors (TFs) and single nucleotide polymorphisms (SNPs) that affect miRNAs, miRNA-binding sites or target genes, whilst also greatly increased (>5-fold) the underlying knowledgebases of miRNAs, ncRNAs and disease associations; (ii) implemented new functions to allow creation and visual exploration of multipartite networks, with enhanced support for in situ functional analysis and (iii) revamped the web interface, optimized the workflow, and introduced microservices and web application programming interface (API) to sustain high-performance, real-time data analysis. The underlying R package is also released in tandem with version 2.0 to allow more flexible data analysis for R programmers. The miRNet 2.0 website is freely available at https://www.mirnet.ca.

A review of Cloud computing technologies for comprehensive microRNA analyses

Cloud computing revolutionized many fields that require ample computational power. Cloud platforms may also provide huge support for microRNA analysis mainly through disclosing scalable resources of different types. In Clouds, these resources are available as services, which simplifies their allocation and releasing. This feature is especially useful during the analysis of large volumes of data, like the one produced by next generation sequencing experiments, which require not only extended storage space but also a distributed computing environment. In this paper, we show which of the Cloud properties and service models can be especially beneficial for microRNA analysis. We also explain the most useful services of the Cloud (including storage space, computational power, web application hosting, machine learning models, and Big Data frameworks) that can be used for microRNA analysis. At the same time, we review several solutions for microRNA and show that the utilization of the Cloud in this field is still weak, but can increase in the future when the awareness of their applicability grows.

Long Non-Coding RNAs As Epigenetic Regulators in Cancer

Long noncoding RNAs (lncRNAs) constitute large portions of the mammalian transcriptome which appeared as a fundamental player, regulating various cellular mechanisms. LncRNAs do not encode proteins, have mRNA-like transcripts and frequently processed similar to the mRNAs. Many investigations have determined that lncRNAs interact with DNA, RNA molecules or proteins and play a significant regulatory function in several biological processes, such as genomic imprinting, epigenetic regulation, cell cycle regulation, apoptosis, and differentiation. LncRNAs can modulate gene expression on three levels: chromatin remodeling, transcription, and post-transcriptional processing. The majority of the identified lncRNAs seem to be transcribed by the RNA polymerase II. Recent evidence has illustrated that dysregulation of lncRNAs can lead to many human diseases, in particular, cancer. The aberrant expression of lncRNAs in malignancies contributes to the dysregulation of proliferation and differentiation process. Consequently, lncRNAs can be useful to the diagnosis, treatment, and prognosis, and have been characterized as potential cancer markers as well. In this review, we highlighted the role and molecular mechanisms of lncRNAs and their correlation with some of the cancers.

Prediction of new associations between ncRNAs and diseases exploiting multi-type hierarchical clustering

BACKGROUND

The study of functional associations between ncRNAs and human diseases is a pivotal task of modern research to develop new and more effective therapeutic approaches. Nevertheless, it is not a trivial task since it involves entities of different types, such as microRNAs, lncRNAs or target genes whose expression also depends on endogenous or exogenous factors. Such a complexity can be faced by representing the involved biological entities and their relationships as a network and by exploiting network-based computational approaches able to identify new associations. However, existing methods are limited to homogeneous networks (i.e., consisting of only one type of objects and relationships) or can exploit only a small subset of the features of biological entities, such as the presence of a particular binding domain, enzymatic properties or their involvement in specific diseases.

RESULTS

To overcome the limitations of existing approaches, we propose the system LP-HCLUS, which exploits a multi-type hierarchical clustering method to predict possibly unknown ncRNA-disease relationships. In particular, LP-HCLUS analyzes heterogeneous networks consisting of several types of objects and relationships, each possibly described by a set of features, and extracts multi-type clusters that are subsequently exploited to predict new ncRNA-disease associations. The extracted clusters are overlapping, hierarchically organized, involve entities of different types, and allow LP-HCLUS to catch multiple roles of ncRNAs in diseases at different levels of granularity. Our experimental evaluation, performed on heterogeneous attributed networks consisting of microRNAs, lncRNAs, diseases, genes and their known relationships, shows that LP-HCLUS is able to obtain better results with respect to existing approaches. The biological relevance of the obtained results was evaluated according to both quantitative (i.e., TPR@k, Areas Under the TPR@k, ROC and Precision-Recall curves) and qualitative (i.e., according to the consultation of the existing literature) criteria.

CONCLUSIONS

The obtained results prove the utility of LP-HCLUS to conduct robust predictive studies on the biological role of ncRNAs in human diseases. The produced predictions can therefore be reliably considered as new, previously unknown, relationships among ncRNAs and diseases.

A Guide to Conquer the Biological Network Era Using Graph Theory

Networks are one of the most common ways to represent biological systems as complex sets of binary interactions or relations between different bioentities. In this article, we discuss the basic graph theory concepts and the various graph types, as well as the available data structures for storing and reading graphs. In addition, we describe several network properties and we highlight some of the widely used network topological features. We briefly mention the network patterns, motifs and models, and we further comment on the types of biological and biomedical networks along with their corresponding computer- and human-readable file formats. Finally, we discuss a variety of algorithms and metrics for network analyses regarding graph drawing, clustering, visualization, link prediction, perturbation, and network alignment as well as the current state-of-the-art tools. We expect this review to reach a very broad spectrum of readers varying from experts to beginners while encouraging them to enhance the field further.

BITS 2017: the annual meeting of the Italian Society of Bioinformatics

This preface introduces the content of the BioMed Central journal Supplement related to the 14th annual meeting of the Bioinformatics Italian Society, held in Cagliari, Italy, from the 5th to the 7th of July, 2017.