LncDisease: a sequence based bioinformatics tool for predicting lncRNA-disease associations

Target-triggered assembly of plasmon resonance nanostructures for quantitative detection of lncRNA in liver cancer cells via surface enhanced Raman spectroscopy

Long-stranded non-coding RNAs (lncRNA) have important roles in disease as transcriptional regulators, mRNA processing regulators and protein synthesis factors. However, traditional methods for detecting lncRNA are time-consuming and labor-intensive, and the functions of lncRNA are still being explored. Here, we present a surface enhanced Raman spectroscopy (SERS) based biosensor for the detection of lncRNA associated with liver cancer (LC) as well as in situ cellular imaging. Using the dual SERS probes, quantitative detection of lncRNA (DAPK1-215) can be achieved with an ultra-low detection limit of 952 aM by the target-triggered assembly of core-satellite nanostructures. And the reliability of this assay can be further improved with the R2 value of 0.9923 by an internal standard probe that enables the signal dynamic calibration. Meanwhile, the high expression of DAPK1-215 mainly distributed in the cytoplasm was observed in LC cells compared with the normal ones using the SERS imaging method. Moreover, results of cellular function assays showed that DAPK1-215 promoted the migration and invasion of LC by significantly reducing the expression of the structural domain of death associated protein kinase. The development of this biosensor based on SERS can provide a sensitive and specific method for exploring the expression of lncRNA that would be a potential biomarker for the screening of LC.

Integrative analysis of the lncRNA-miRNA-mRNA interactions in smooth muscle cell phenotypic transitions

Objectives: We previously found that the pluripotency factor OCT4 is reactivated in smooth muscle cells (SMC) in human and mouse atherosclerotic plaques and plays an atheroprotective role. Loss of OCT4 in SMC in vitro was associated with decreases in SMC migration. However, molecular mechanisms responsible for atheroprotective SMC-OCT4-dependent effects remain unknown. Methods: Since studies in embryonic stem cells demonstrated that OCT4 regulates long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), making them candidates for OCT4 effect mediators, we applied an in vitro approach to investigate the interactions between OCT4-regulated lncRNAs, mRNAs, and miRNAs in SMC. We used OCT4 deficient mouse aortic SMC (MASMC) treated with the pro-atherogenic oxidized phospholipid POVPC, which, as we previously demonstrated, suppresses SMC contractile markers and induces SMC migration. Differential expression of lncRNAs, mRNAs, and miRNAs was obtained by lncRNA/mRNA expression array and small-RNA microarray. Long non-coding RNA to mRNA associations were predicted based on their genomic proximity and association with vascular diseases. Given a recently discovered crosstalk between miRNA and lncRNA, we also investigated the association of miRNAs with upregulated/downregulated lncRNA-mRNA pairs. Results: POVPC treatment in SMC resulted in upregulating genes related to the axon guidance and focal adhesion pathways. Knockdown of Oct4 resulted in differential regulation of pathways associated with phagocytosis. Importantly, these results were consistent with our data showing that OCT4 deficiency attenuated POVPC-induced SMC migration and led to increased phagocytosis. Next, we identified several up- or downregulated lncRNA associated with upregulation of the specific mRNA unique for the OCT4 deficient SMC, including upregulation of ENSMUST00000140952-Hoxb5/6 and ENSMUST00000155531-Zfp652 along with downregulation of ENSMUST00000173605-Parp9 and, ENSMUST00000137236-Zmym1. Finally, we found that many of the downregulated miRNAs were associated with cell migration, including miR-196a-1 and miR-10a, targets of upregulated ENSMUST00000140952, and miR-155 and miR-122, targets of upregulated ENSMUST00000155531. Oppositely, the upregulated miRNAs were anti-migratory and pro-phagocytic, such as miR-10a/b and miR-15a/b, targets of downregulated ENSMUST00000173605, and miR-146a/b and miR-15b targets of ENSMUST00000137236. Conclusion: Our integrative analyses of the lncRNA-miRNA-mRNA interactions in SMC indicated novel potential OCT4-dependent mechanisms that may play a role in SMC phenotypic transitions.

Transcriptomics analysis reveals key lncRNAs and genes related to the infection of feline kidney cell line by panleukopenia virus

Feline panleukopenia virus (FPV) can cause a viral disease and is responsible for severe leukopenia, gastroenteritis, and nervous signs with significant economic losses. Biochemically long non-coding RNAs (lncRNAs) can regulate the expression of mRNA in different ways, thereby causing the functional changes in host cells in response to viral infection. However, no attention has been paid until now to investigate the link between FPV pathogenesis and lncRNA. Here, through RNA sequencing, we performed a comprehensive analysis of lncRNA and mRNA in F81 cells after FPV-BJ04 strain infection. Consistent with previous studies, our data showed that lncRNAs have distinct features from mRNA. A total of 291 lncRNAs and 873 mRNAs were differentially expressed in F81 cells after FPV-BJ04 infection. GO and KEGG enrichment analysis showed that the differentially upregulated lncRNAs target genes were mainly involved in the positive regulation of transcription by RNA polymerase II and MAPK signaling pathway. The differentially downregulated lncRNAs target genes were mainly involved in the mRNA splicing and endocytosis. In addition, the differentially expressed immune pathway related genes that are targeted by lncRNA were also screened out to construct a lncRNA-miRNA-mRNA axes as a potential novel biomarkers in regulating the immune response of feline against FPV infection. Our results contribute to understand the basic role of lncRNA in F81 cells during FPV infection and lay the foundation for following research.

Non-Coding RNAs in Tuberculosis Epidemiology: Platforms and Approaches for Investigating the Genome's Dark Matter

A growing amount of information about the different types, functions, and roles played by non-coding RNAs (ncRNAs) is becoming available, as more and more research is done. ncRNAs have been identified as potential therapeutic targets in the treatment of tuberculosis (TB), because they may be essential regulators of the gene network. ncRNA profiling and sequencing has recently revealed significant dysregulation in tuberculosis, primarily due to aberrant processes of ncRNA synthesis, including amplification, deletion, improper epigenetic regulation, or abnormal transcription. Despite the fact that ncRNAs may have a role in TB characteristics, the detailed mechanisms behind these occurrences are still unknown. The dark matter of the genome can only be explored through the development of cutting-edge bioinformatics and molecular technologies. In this review, ncRNAs' synthesis and functions are discussed in detail, with an emphasis on the potential role of ncRNAs in tuberculosis. We also focus on current platforms, experimental strategies, and computational analyses to explore ncRNAs in TB. Finally, a viewpoint is presented on the key challenges and novel techniques for the future and for a wide-ranging therapeutic application of ncRNAs.

LncRNA-miRNA network analysis across the Th17 cell line reveals biomarker potency of lncRNA NEAT1 and KCNQ1OT1 in multiple sclerosis

Differentiation of CD⁴⁺ T cells into Th17 cells is an important factor in the onset and progression of multiple sclerosis (MS) and Th17/Treg imbalance. Little is known about the role of lncRNAs in the differentiation of CD⁴⁺ cells from Th17 cells. This study aimed to analyse the lncRNA‐miRNAs network involved in MS disease and its role in the differentiation of Th17 cells. The lncRNAs in Th17 differentiation were obtained from GSE66261 using the GEO datasets. Differential expression of lncRNAs in Th17 primary cells compared to Th17 effector cells was investigated by RNA‐seq analysis. Next, the most highlighted lncRNAs in autoimmune diseases were downloaded from the lncRNAs disease database, and the most critical miRNA was extracted by literature search. Then, the lncRNA‐miRNA interaction was achieved by the Starbase database, and the ceRNA network was designed by Cytoscape. Finally, using the CytoHubba application, two hub lncRNAs with the most interactions with miRNAs were identified by the MCODE plug‐in. The expression level of genes was measured by qPCR, and the plasma level of cytokines was analysed by ELISA kits. The results showed an increase in the expression of NEAT1, KCNQ1OT1 and RORC and a decrease in the expression of FOXP3. In plasma, an upregulation of IL17 and a downregulation of TGFB inflammatory cytokines were detected. The dysregulated expression of these genes could be attributed to relapsing‐remitting MS (RR‐MS) patients and help us understand MS pathogenesis better.

EnANNDeep: An Ensemble-based lncRNA-protein Interaction Prediction Framework with Adaptive k-Nearest Neighbor Classifier and Deep Models

lncRNA-protein interactions (LPIs) prediction can deepen the understanding of many important biological processes. Artificial intelligence methods have reported many possible LPIs. However, most computational techniques were evaluated mainly on one dataset, which may produce prediction bias. More importantly, they were validated only under cross validation on lncRNA-protein pairs, and did not consider the performance under cross validations on lncRNAs and proteins, thus fail to search related proteins/lncRNAs for a new lncRNA/protein. Under an ensemble learning framework (EnANNDeep) composed of adaptive k-nearest neighbor classifier and Deep models, this study focuses on systematically finding underlying linkages between lncRNAs and proteins. First, five LPI-related datasets are arranged. Second, multiple source features are integrated to depict an lncRNA-protein pair. Third, adaptive k-nearest neighbor classifier, deep neural network, and deep forest are designed to score unknown lncRNA-protein pairs, respectively. Finally, interaction probabilities from the three predictors are integrated based on a soft voting technique. In comparing to five classical LPI identification models (SFPEL, PMDKN, CatBoost, PLIPCOM, and LPI-SKF) under fivefold cross validations on lncRNAs, proteins, and LPIs, EnANNDeep computes the best average AUCs of 0.8660, 0.8775, and 0.9166, respectively, and the best average AUPRs of 0.8545, 0.8595, and 0.9054, respectively, indicating its superior LPI prediction ability. Case study analyses indicate that SNHG10 may have dense linkage with Q15717. In the ensemble framework, adaptive k-nearest neighbor classifier can separately pick the most appropriate k for each query lncRNA-protein pair. More importantly, deep models including deep neural network and deep forest can effectively learn the representative features of lncRNAs and proteins.

Inferring Latent Disease-lncRNA Associations by Label-Propagation Algorithm and Random Projection on a Heterogeneous Network

Long noncoding RNA (lncRNA), a type of more than 200 nucleotides non-coding RNA, is related to various complex diseases. To precisely identify the potential lncRNA–disease association is important to understand the disease pathogenesis, to develop new drugs, and to design individualized diagnosis and treatment methods for different human diseases. Compared with the complexity and high cost of biological experiments, computational methods can quickly and effectively predict potential lncRNA–disease associations. Thus, it is a promising avenue to develop computational methods for lncRNA-disease prediction. However, owing to the low prediction accuracy ofstate of the art methods, it is vastly challenging to accurately and effectively identify lncRNA-disease at present. This article proposed an integrated method called LPARP, which is based on label-propagation algorithm and random projection to address the issue. Specifically, the label-propagation algorithm is initially used to obtain the estimated scores of lncRNA–disease associations, and then random projections are used to accurately predict disease-related lncRNAs.The empirical experiments showed that LAPRP achieved good prediction on three golddatasets, which is superior to existing state-of-the-art prediction methods. It can also be used to predict isolated diseases and new lncRNAs. Case studies of bladder cancer, esophageal squamous-cell carcinoma, and colorectal cancer further prove the reliability of the method. The proposed LPARP algorithm can predict the potential lncRNA–disease interactions stably and effectively with fewer data. LPARP can be used as an effective and reliable tool for biomedical research.

Noncoding RNAs in apoptosis: identification and function

Apoptosis is a vital cellular process that is critical for the maintenance of homeostasis in health and disease. The derailment of apoptotic mechanisms has severe consequences such as abnormal development, cancer, and neurodegenerative diseases. Thus, there exist complex regulatory mechanisms in eukaryotes to preserve the balance between cell growth and cell death. Initially, protein-coding genes were prioritized in the search for such regulatory macromolecules involved in the regulation of apoptosis. However, recent genome annotations and transcriptomics studies have uncovered a plethora of regulatory noncoding RNAs that have the ability to modulate not only apoptosis but also many other biochemical processes in eukaryotes. In this review article, we will cover a brief summary of apoptosis and detection methods followed by an extensive discussion on microRNAs, circular RNAs, and long noncoding RNAs in apoptosis.

iLncRNAdis-FB: Identify lncRNA-Disease Associations by Fusing Biological Feature Blocks Through Deep Neural Network

Identification of lncRNA-disease associations is not only important for exploring the disease mechanism, but will also facilitate the molecular targeting drug discovery. Fusing multiple biological information is able to generate a more comprehensive view of lncRNA-disease association feature. However, the existing fusion strategies in this field fail to remove the noisy and irrelevant information from each data source. As a result, their predictive performance is still too low to be applied to real world applications. In this regard, a novel computational predictor called iLncRNAdis-FB is proposed based on the Convolution Neural Network (CNN) to integrate different data sources by using the feature blocks in a supervised manner. The lncRNA similarity matrix and disease similarity matrix are constructed, based on which the three-dimensional feature blocks are generated. These feature blocks are then fed into CNN to train the model so as to predict unknown lncRNA-disease associations. Experimental results show that iLncRNAdis-FB achieves better performance compared with other state-of-the-art predictors. Furthermore, a web server of iLncRNAdis-FB has been established at http://bliulab.net/iLncRNAdis-FB/, by which users can submit lncRNA sequences to detect their potential associated diseases.

Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review

Technological advances in high-throughput techniques have resulted in tremendous growth of complex biological datasets providing evidence regarding various biomolecular interactions. To cope with this data flood, computational approaches, web services, and databases have been implemented to deal with issues such as data integration, visualization, exploration, organization, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more and more difficult for an end user to know what the scope and focus of each repository is and how redundant the information between them is. Several repositories have a more general scope, while others focus on specialized aspects, such as specific organisms or biological systems. Unfortunately, many of these databases are self-contained or poorly documented and maintained. For a clearer view, in this article we provide a comprehensive categorization, comparison and evaluation of such repositories for different bioentity interaction types. We discuss most of the publicly available services based on their content, sources of information, data representation methods, user-friendliness, scope and interconnectivity, and we comment on their strengths and weaknesses. We aim for this review to reach a broad readership varying from biomedical beginners to experts and serve as a reference article in the field of Network Biology.

The Implications of ncRNAs in the Development of Human Diseases

The mammalian genome comprehends a small minority of genes that encode for proteins (barely 2% of the total genome in humans) and an immense majority of genes that are transcribed into RNA but not encoded for proteins (ncRNAs). These non-coding genes are intimately related to the expression regulation of protein-coding genes. The ncRNAs subtypes differ in their size, so there are long non-coding genes (lncRNAs) and other smaller ones, like microRNAs (miRNAs) and piwi-interacting RNAs (piRNAs). Due to their important role in the maintenance of cellular functioning, any deregulation of the expression profiles of these ncRNAs can dissemble in the development of different types of diseases. Among them, we can highlight some of high incidence in the population, such as cancer, neurodegenerative, or cardiovascular disorders. In addition, thanks to the enormous advances in the field of medical genomics, these same ncRNAs are starting to be used as possible drugs, approved by the FDA, as an effective treatment for diseases.

MNDR v3.0: mammal ncRNA-disease repository with increased coverage and annotation

Many studies have indicated that non-coding RNA (ncRNA) dysfunction is closely related to numerous diseases. Recently, accumulated ncRNA-disease associations have made related databases insufficient to meet the demands of biomedical research. The constant updating of ncRNA-disease resources has become essential. Here, we have updated the mammal ncRNA-disease repository (MNDR, http://www.rna-society.org/mndr/) to version 3.0, containing more than one million entries, four-fold increment in data compared to the previous version. Experimental and predicted circRNA-disease associations have been integrated, increasing the number of categories of ncRNAs to five, and the number of mammalian species to 11. Moreover, ncRNA-disease related drug annotations and associations, as well as ncRNA subcellular localizations and interactions, were added. In addition, three ncRNA-disease (miRNA/lncRNA/circRNA) prediction tools were provided, and the website was also optimized, making it more practical and user-friendly. In summary, MNDR v3.0 will be a valuable resource for the investigation of disease mechanisms and clinical treatment strategies.

ATACdb: a comprehensive human chromatin accessibility database

Accessible chromatin is a highly informative structural feature for identifying regulatory elements, which provides a large amount of information about transcriptional activity and gene regulatory mechanisms. Human ATAC-seq datasets are accumulating rapidly, prompting an urgent need to comprehensively collect and effectively process these data. We developed a comprehensive human chromatin accessibility database (ATACdb, http://www.licpathway.net/ATACdb), with the aim of providing a large amount of publicly available resources on human chromatin accessibility data, and to annotate and illustrate potential roles in a tissue/cell type-specific manner. The current version of ATACdb documented a total of 52 078 883 regions from over 1400 ATAC-seq samples. These samples have been manually curated from over 2200 chromatin accessibility samples from NCBI GEO/SRA. To make these datasets more accessible to the research community, ATACdb provides a quality assurance process including four quality control (QC) metrics. ATACdb provides detailed (epi)genetic annotations in chromatin accessibility regions, including super-enhancers, typical enhancers, transcription factors (TFs), common single-nucleotide polymorphisms (SNPs), risk SNPs, eQTLs, LD SNPs, methylations, chromatin interactions and TADs. Especially, ATACdb provides accurate inference of TF footprints within chromatin accessibility regions. ATACdb is a powerful platform that provides the most comprehensive accessible chromatin data, QC, TF footprint and various other annotations.

ICLRBBN: a tool for accurate prediction of potential lncRNA disease associations

Growing evidence has elucidated that long non-coding RNAs (lncRNAs) are involved in a variety of complex diseases in human bodies. In recent years, it has become a hot topic to develop effective computational models to identify potential lncRNA-disease associations. In this article, a novel method called ICLRBBN (Internal Confidence-Based Local Radial Basis Biological Network) is proposed to detect potential lncRNA-disease associations by adopting an internal confidence-based radial basis biological network. In ICLRBBN, a novel internal confidence-based collaborative filtering recommendation algorithm was designed first to mine hidden features between lncRNAs and diseases, which guarantees that ICLRBBN can be more effectively applied to predict new diseases. Then, a unique three-layer local radial basis function network consisting of diseases and lncRNAs was constructed, based on which the association probability between diseases and lncRNAs was calculated by combining different characteristics of lncRNAs with local information of diseases. Finally, we compared ICLRBBN with 6 state-of-the-art methods based on two different validation frameworks. Simulation results showed that area under the receiver operating characteristic curve (AUC) values achieved by ICLRBBN outperformed all competing methods. Furthermore, case studies illustrated that ICLRBBN has a promising future as a powerful tool in the practical application of lncRNA-disease association prediction. A web service for prediction of potential lncRNA-disease associations is available at http://leelab2997.cn/.

Micronucleus Evaluation in Exfoliated Human Buccal Epithelium Cells among E-Waste Workers in Payatas, the Philippines

BACKGROUND

The improper recycling of electronic waste (e-waste) by informal recyclers often leads to contamination of the environment. E-waste contains organic and inorganic compounds along with heavy metals and trace elements. These pollutants can have a negative effect on humans. Biomonitoring can provide information on the sources, amount, geographical distribution, and adverse health effects of contaminants.

OBJECTIVES

The present study aimed to assess risks to the health of informal e-waste recyclers in Payatas, the Philippines due to their exposure to e-waste toxicity by examining the presence of micronuclei in buccal epithelium cells.

METHODOLOGY

Frequencies of binucleated cells (BNc) and abnormal cells were obtained from the buccal epithelium of the study population composed of e-waste exposed recyclers (n=40) and a control group (n=52). Descriptive statistics and regression analysis were employed for the data analysis.

RESULTS

Participants' gender, occupation, smoking status, alcohol consumption, and the number of karyolitic cells of both groups were significantly associated. Only occupation in e-waste recycling and length of e-waste exposure were significantly associated in terms of the number of abnormal cells and micronuclei. Similar trends were found in the linear regression analysis drawn from participants' length of e-waste exposure with a significance of R2= 7346, indicating that as the length of e-waste exposure increased, the number of micronuclei found in the participants' buccal epithelium cells increased as well.

CONCLUSIONS

Longer exposure to e-waste materials may induce genotoxic damage in human cells which is a serious concern, leading to adverse effects to human health.

COMPETING INTERESTS

The authors declare no competing financial interests.

Pathobiology of pulmonary artery hypertension: role of long non-coding RNAs

Pulmonary arterial hypertension (PAH) is a disease with complex pathobiology, significant morbidity and mortality, and remains without a cure. It is characterized by vascular remodelling associated with uncontrolled proliferation of pulmonary artery smooth muscle cells, endothelial cell proliferation and dysfunction, and endothelial-to-mesenchymal transition, leading to narrowing of the vascular lumen, increased vascular resistance and pulmonary arterial pressure, which inevitably results in right heart failure and death. There are multiple molecules and signalling pathways that are involved in the vascular remodelling, including non-coding RNAs, i.e. microRNAs and long non-coding RNAs (lncRNAs). It is only in recent years that the role of lncRNAs in the pathobiology of pulmonary vascular remodelling and right ventricular dysfunction is being vigorously investigated. In this review, we have summarized the current state of knowledge about the role of lncRNAs as key drivers and gatekeepers in regulating major cellular and molecular trafficking involved in the pathogenesis of PAH. In addition, we have discussed the limitations and challenges in translating lncRNA research in vivo and in therapeutic applications of lncRNAs in PAH.

The Interplay between Long Noncoding RNAs and Proteins of the Epigenetic Machinery in Ovarian Cancer

Comprehensive large-scale sequencing and bioinformatics analyses have uncovered a myriad of cancer-associated long noncoding RNAs (lncRNAs). Aberrant expression of lncRNAs is associated with epigenetic reprogramming during tumor development and progression, mainly due to their ability to interact with DNA, RNA, or proteins to regulate gene expression. LncRNAs participate in the control of gene expression patterns during development and cell differentiation and can be cell and cancer type specific. In this review, we described the potential of lncRNAs for clinical applications in ovarian cancer (OC). OC is a complex and heterogeneous disease characterized by relapse, chemoresistance, and high mortality rates. Despite advances in diagnosis and treatment, no significant improvements in long-term survival were observed in OC patients. A set of lncRNAs was associated with survival and response to therapy in this malignancy. We manually curated databases and used bioinformatics tools to identify lncRNAs implicated in the epigenetic regulation, along with examples of direct interactions between the lncRNAs and proteins of the epigenetic machinery in OC. The resources and mechanisms presented herein can improve the understanding of OC biology and provide the basis for further investigations regarding the selection of novel biomarkers and therapeutic targets.

ncRPheno: a comprehensive database platform for identification and validation of disease related noncoding RNAs

Noncoding RNAs (ncRNAs) play critical roles in many critical biological processes and have become a novel class of potential targets and bio-markers for disease diagnosis, therapy, and prognosis. Annotating and analysing ncRNA-disease association data are essential but challenging. Current computational resources lack comprehensive database platforms to consistently interpret and prioritize ncRNA-disease association data for biomedical investigation and application. Here, we present the ncRPheno database platform (http://lilab2.sysu.edu.cn/ncrpheno), which comprehensively integrates and annotates ncRNA-disease association data and provides novel searches, visualizations, and utilities for association identification and validation. ncRPheno contains 482,751 non-redundant associations between 14,494 ncRNAs and 3,210 disease phenotypes across 11 species with supporting evidence in the literature. A scoring model was refined to prioritize the associations based on evidential metrics. Moreover, ncRPheno provides user-friendly web interfaces, novel visualizations, and programmatic access to enable easy exploration, analysis, and utilization of the association data. A case study through ncRPheno demonstrated a comprehensive landscape of ncRNAs dysregulation associated with 22 cancers and uncovered 821 cancer-associated common ncRNAs. As a unique database platform, ncRPheno outperforms the existing similar databases in terms of data coverage and utilities, and it will assist studies in encoding ncRNAs associated with phenotypes ranging from genetic disorders to complex diseases.

ABBREVIATIONS

APIs: application programming interfaces; circRNA: circular RNA; ECO: Evidence & Conclusion Ontology; EFO: Experimental Factor Ontology; FDR: false discovery rate; GO: Gene Ontology; GWAS: genome wide association studies; HPO: Human Phenotype Ontology; ICGC: International Cancer Genome Consortium; lncRNA: long noncoding RNA; miRNA: micro RNA; ncRNA: noncoding RNA; NGS: next generation sequencing; OMIM: Online Mendelian Inheritance in Man; piRNA: piwi-interacting RNA; snoRNA: small nucleolar RNA; TCGA: The Cancer Genome Atlas.

Targeting epigenetics in cancer: therapeutic potential of flavonoids

Irrespective of sex and age, cancer is the leading cause of mortality around the globe. Therapeutic incompliance, unwanted effects, and economic burdens imparted by cancer treatments, are primary health challenges. The heritable features in gene expression that are propagated through cell division and contribute to cellular identity without a change in DNA sequence are considered epigenetic characteristics and agents that could interfere with these features and are regarded as potential therapeutic targets. The genetic modification accounts for the recurrence and uncontrolled changes in the physiology of cancer cells. This review focuses on plant-derived flavonoids as a therapeutic tool for cancer, attributed to their ability for epigenetic regulation of cancer pathogenesis. The epigenetic mechanisms of various classes of flavonoids including flavonols, flavones, isoflavones, flavanones, flavan-3-ols, and anthocyanidins, such as cyanidin, delphinidin, and pelargonidin, are discussed. The outstanding results of preclinical studies encourage researchers to design several clinical trials on various flavonoids to ascertain their clinical strength in the treatment of different cancers. The results of such studies will define the clinical fate of these agents in future.

LncRNADisease 2.0: an updated database of long non-coding RNA-associated diseases

Mounting evidence suggested that dysfunction of long non-coding RNAs (lncRNAs) is involved in a wide variety of diseases. A knowledgebase with systematic collection and curation of lncRNA-disease associations is critically important for further examining their underlying molecular mechanisms. In 2013, we presented the first release of LncRNADisease, representing a database for collection of experimental supported lncRNA-disease associations. Here, we describe an update of the database. The new developments in LncRNADisease 2.0 include (i) an over 40-fold lncRNA-disease association enhancement compared with the previous version; (ii) providing the transcriptional regulatory relationships among lncRNA, mRNA and miRNA; (iii) providing a confidence score for each lncRNA-disease association; (iv) integrating experimentally supported circular RNA disease associations. LncRNADisease 2.0 documents more than 200 000 lncRNA-disease associations. We expect that this database will continue to serve as a valuable source for potential clinical application related to lncRNAs. LncRNADisease 2.0 is freely available at http://www.rnanut.net/lncrnadisease/.

iPiDi-PUL: identifying Piwi-interacting RNA-disease associations based on positive unlabeled learning

Accumulated researches have revealed that Piwi-interacting RNAs (piRNAs) are regulating the development of germ and stem cells, and they are closely associated with the progression of many diseases. As the number of the detected piRNAs is increasing rapidly, it is important to computationally identify new piRNA-disease associations with low cost and provide candidate piRNA targets for disease treatment. However, it is a challenging problem to learn effective association patterns from the positive piRNA-disease associations and the large amount of unknown piRNA-disease pairs. In this study, we proposed a computational predictor called iPiDi-PUL to identify the piRNA-disease associations. iPiDi-PUL extracted the features of piRNA-disease associations from three biological data sources, including piRNA sequence information, disease semantic terms and the available piRNA-disease association network. Principal component analysis (PCA) was then performed on these features to extract the key features. The training datasets were constructed based on known positive associations and the negative associations selected from the unknown pairs. Various random forest classifiers trained with these different training sets were merged to give the predictive results via an ensemble learning approach. Finally, the web server of iPiDi-PUL was established at http://bliulab.net/iPiDi-PUL to help the researchers to explore the associated diseases for newly discovered piRNAs.

A random forest based computational model for predicting novel lncRNA-disease associations

BACKGROUND

Accumulated evidence shows that the abnormal regulation of long non-coding RNA (lncRNA) is associated with various human diseases. Accurately identifying disease-associated lncRNAs is helpful to study the mechanism of lncRNAs in diseases and explore new therapies of diseases. Many lncRNA-disease association (LDA) prediction models have been implemented by integrating multiple kinds of data resources. However, most of the existing models ignore the interference of noisy and redundancy information among these data resources.

RESULTS

To improve the ability of LDA prediction models, we implemented a random forest and feature selection based LDA prediction model (RFLDA in short). First, the RFLDA integrates the experiment-supported miRNA-disease associations (MDAs) and LDAs, the disease semantic similarity (DSS), the lncRNA functional similarity (LFS) and the lncRNA-miRNA interactions (LMI) as input features. Then, the RFLDA chooses the most useful features to train prediction model by feature selection based on the random forest variable importance score that takes into account not only the effect of individual feature on prediction results but also the joint effects of multiple features on prediction results. Finally, a random forest regression model is trained to score potential lncRNA-disease associations. In terms of the area under the receiver operating characteristic curve (AUC) of 0.976 and the area under the precision-recall curve (AUPR) of 0.779 under 5-fold cross-validation, the performance of the RFLDA is better than several state-of-the-art LDA prediction models. Moreover, case studies on three cancers demonstrate that 43 of the 45 lncRNAs predicted by the RFLDA are validated by experimental data, and the other two predicted lncRNAs are supported by other LDA prediction models.

CONCLUSIONS

Cross-validation and case studies indicate that the RFLDA has excellent ability to identify potential disease-associated lncRNAs.

GAS5, a FoxO1-actived long noncoding RNA, promotes propofol-induced oral squamous cell carcinoma apoptosis by regulating the miR-1297-GSK3β axis

Propofol, an intravenous anaesthetic agent, has been found to exhibit antitumour effects in various kinds of cancer cells. However, the potential roles and regulatory mechanisms of propofol in oral squamous cell carcinoma (OSCC) remain unknown. Herein, we found that propofol inhibits OSCC cell growth and promotes cell apoptosis in a dose- and time-dependent manner. Further mechanistic studies revealed that the long noncoding RNA GAS5 is induced by propofol in OSCC cells. Elevated GAS5 acts as a competing endogenous RNA for miR-1297 and attenuates its inhibitory effect on GSK3β, leading to GSK3β increase and Mcl1 decrease. Additionally, we found that FoxO1 binds to the promoter of GAS5, facilitating its transcription in response to propofol treatment. Thus, these results suggest that propofol exhibits antitumour effects in OSCC cells and that the FoxO1-GAS5-miR-1297-GSK3β axis plays an important role in propofol-induced OSCC cell apoptosis.

TRlnc: a comprehensive database for human transcriptional regulatory information of lncRNAs

Long noncoding RNAs (lncRNAs) have been proven to play important roles in transcriptional processes and biological functions. With the increasing study of human diseases and biological processes, information in human H3K27ac ChIP-seq, ATAC-seq and DNase-seq datasets is accumulating rapidly, resulting in an urgent need to collect and process data to identify transcriptional regulatory regions of lncRNAs. We therefore developed a comprehensive database for human regulatory information of lncRNAs (TRlnc, http://bio.licpathway.net/TRlnc), which aimed to collect available resources of transcriptional regulatory regions of lncRNAs and to annotate and illustrate their potential roles in the regulation of lncRNAs in a cell type-specific manner. The current version of TRlnc contains 8 683 028 typical enhancers/super-enhancers and 32 348 244 chromatin accessibility regions associated with 91 906 human lncRNAs. These regions are identified from over 900 human H3K27ac ChIP-seq, ATAC-seq and DNase-seq samples. Furthermore, TRlnc provides the detailed genetic and epigenetic annotation information within transcriptional regulatory regions (promoter, enhancer/super-enhancer and chromatin accessibility regions) of lncRNAs, including common SNPs, risk SNPs, eQTLs, linkage disequilibrium SNPs, transcription factors, methylation sites, histone modifications and 3D chromatin interactions. It is anticipated that the use of TRlnc will help users to gain in-depth and useful insights into the transcriptional regulatory mechanisms of lncRNAs.

Probing lncRNA-Protein Interactions: Data Repositories, Models, and Algorithms

Identifying lncRNA-protein interactions (LPIs) is vital to understanding various key biological processes. Wet experiments found a few LPIs, but experimental methods are costly and time-consuming. Therefore, computational methods are increasingly exploited to capture LPI candidates. We introduced relevant data repositories, focused on two types of LPI prediction models: network-based methods and machine learning-based methods. Machine learning-based methods contain matrix factorization-based techniques and ensemble learning-based techniques. To detect the performance of computational methods, we compared parts of LPI prediction models on Leave-One-Out cross-validation (LOOCV) and fivefold cross-validation. The results show that SFPEL-LPI obtained the best performance of AUC. Although computational models have efficiently unraveled some LPI candidates, there are many limitations involved. We discussed future directions to further boost LPI predictive performance.

A hidden human proteome encoded by 'non-coding' genes

It has been a long debate whether the 98% ‘non-coding’ fraction of human genome can encode functional proteins besides short peptides. With full-length translating mRNA sequencing and ribosome profiling, we found that up to 3330 long non-coding RNAs (lncRNAs) were bound to ribosomes with active translation elongation. With shotgun proteomics, 308 lncRNA-encoded new proteins were detected. A total of 207 unique peptides of these new proteins were verified by multiple reaction monitoring (MRM) and/or parallel reaction monitoring (PRM); and 10 new proteins were verified by immunoblotting. We found that these new proteins deviated from the canonical proteins with various physical and chemical properties, and emerged mostly in primates during evolution. We further deduced the protein functions by the assays of translation efficiency, RNA folding and intracellular localizations. As the new protein UBAP1-AST6 is localized in the nucleoli and is preferentially expressed by lung cancer cell lines, we biologically verified that it has a function associated with cell proliferation. In sum, we experimentally evidenced a hidden human functional proteome encoded by purported lncRNAs, suggesting a resource for annotating new human proteins.

Computational resources associating diseases with genotypes, phenotypes and exposures

The causes of a disease and its therapies are not only related to genotypes, but also associated with other factors, including phenotypes, environmental exposures, drugs and chemical molecules. Distinguishing disease-related factors from many neutral factors is critical as well as difficult. Over the past two decades, bioinformaticians have developed many computational resources to integrate the omics data and discover associations among these factors. However, researchers and clinicians are experiencing difficulties in choosing appropriate resources from hundreds of relevant databases and software tools. Here, in order to assist the researchers and clinicians, we systematically review the public computational resources of human diseases related to genotypes, phenotypes, environment factors, drugs and chemical exposures. We briefly describe the development history of these computational resources, followed by the details of the relevant databases and software tools. We finally conclude with a discussion of current challenges and future opportunities as well as prospects on this topic.

Potential Biomarker and Therapeutic LncRNAs in Multiple Sclerosis Through Targeting Memory B Cells

Multiple sclerosis (MS) is a chronic autoimmune disease that degenerates the central nervous system (CNS). B cells exacerbate the progression of CNS lesions in MS by producing auto-antibodies, pro-inflammatory cytokines, and presenting auto-antigens to activated T cells. Long non-coding RNAs (lncRNAs) play a crucial role in complex biological processes and their stability in body fluids combined with their tissue specificity make these biomolecules promising biomarker candidates for MS diagnosis. In the current study, we investigated memory B cell-specific lncRNAs located, on average, less than 50 kb from differentially expressed protein-coding genes in MS patients compared to healthy individuals. Moreover, we included in our selection criteria lncRNA transcripts predicted to interact with microRNAs with established involvement in MS. To assess the expression levels of lncRNAs and their adjacent protein-coding genes, quantitative reverse transcription PCR was performed on peripheral blood mononuclear cells samples of 50 MS patients compared to 25 controls. Our results showed that in relapsing MS patients, compared to remitting MS patients and healthy controls, lncRNA RP11-530C5.1 was up-regulated while AL928742.12 was down-regulated. Pearson's correlation tests showed positive correlations between the expression levels of RP11-530C5.1 and AL928742.12 with PAWR and IGHA2, respectively. The results of the ROC curve test demonstrated the potential biomarker roles of AL928742.12 and RP11-530C5.1. We conclude that these lncRNAs are potential markers for detection of relapsing MS patients.

Long noncoding RNA LINC00342 promotes growth of infantile hemangioma by sponging miR-3619-5p from HDGF

Infantile hemangiomas (IH) are a type of benign vascular neoplasm that may cause permanent scarring. Hemangioma-derived endothelial cells (HemECs) are commonly used as an in vitro model to study IH. Long noncoding RNA is a type of RNA transcript longer than 200 nucleotides that does not encode any protein. LINC00342 was discovered to regulate proliferation and apoptosis in nonsmall cell lung cancer. However, the role of LINC00342 in IH has never been reported before. Expressions of LINC00342 and miR-3619-5p were detected in proliferating versus normal skin tissues. Colony formation and Cell-Couting Kit 8 assays were carried out to study the effects on cell proliferation after knockdown and overexpression of LINC00342, respectively. Meanwhile caspase-3 activity and nucleosomal fragmentation assay were applied to detect cell apoptosis. Micro-RNA binding sites on LINC00342 and hepatoma-derived growth factor (HDGF) were predicted and confirmed via dual-luciferase reporter assay. Biotin RNA pulldown assay was used to verify the direct binding between RNA molecules. LINC00342 enhanced proliferation and inhibited apoptosis in HemECs. MiR-3619-5p targeted both LINC00342 and HDGF, where LINC00342 sponged miR-3619-5p and positively regulated HDGF. HDGF knockdown rescued the effects of LINC00342 on HemECs. The LINC00342-miR-3619-5p-HDGF signaling pathway could regulate cell proliferation and apoptosis in HemECs.NEW & NOTEWORTHY The role of LINC00342 in infantile hemangiomas has not yet been elucidated. This paper highlights the regulatory role of LINC00342 in cell proliferation and apoptosis in hemangioma-derived endothelial cells and the underlying molecular mechanisms. The findings would provide potential target for treatment of infantile hemangiomas.

D-lnc: a comprehensive database and analytical platform to dissect the modification of drugs on lncRNA expression

Long non-coding RNAs (lncRNAs) have been proven to be implicated in the pathogenesis of various diseases. Multiple studies have demonstrated that small molecule drugs can modify lncRNA expression, which suggests a promising therapy for human diseases. Here, we constructed a comprehensive query and analytical platform D-lnc to dissect the influence of drugs on lncRNA expression. Firstly, we manually curated the experimentally validated regulations of drugs on lncRNA expression and recorded 7,825 entries between 59 drugs and 7,538 lncRNAs across five species from nearly 1,000 published papers. Secondly, we comprehensively screened the Connectivity Map (cMap) and the Gene Expression Omnibus (GEO) databases to obtain the drug-perturbed gene expression profiles. Through probe re-annotation of microarray data, we identified 19,946 putative associations between 1,279 drugs and 129 lncRNAs in cMap and 36,210 entries between 115 drugs and 2,360 lncRNAs in GEO. Finally, we developed an online analytical platform to predict the potential acting drugs or modified lncRNAs based on user input lncRNA sequence or drug structure through computing the similarities of lncRNA sequences or drug structures. In a word, D-lnc provides a comprehensive platform to detect the modification of drugs on lncRNA expression, which would facilitate the development of lncRNA-targeted therapeutics. D-lnc is freely available at http://www.jianglab.cn/D-lnc/ .

lncRNASNP2: an updated database of functional SNPs and mutations in human and mouse lncRNAs

Long non-coding RNAs (lncRNAs) are emerging as important regulators in different biological processes through various ways. Because the related data, especially mutations in cancers, increased sharply, we updated the lncRNASNP to version 2 (http://bioinfo.life.hust.edu.cn/lncRNASNP2). lncRNASNP2 provides comprehensive information of SNPs and mutations in lncRNAs, as well as their impacts on lncRNA structure and function. lncRNASNP2 contains 7260238 SNPs on 141353 human lncRNA transcripts and 3921448 SNPs on 117405 mouse lncRNA transcripts. Besides the SNP information in the first version, the following new features were developed to improve the lncRNASNP2. (i) noncoding variants from COSMIC cancer data (859534) in lncRNAs and their effects on lncRNA structure and function; (ii) TCGA cancer mutations (315234) in lncRNAs and their impacts; (iii) lncRNA expression profiling of 20 cancer types in both tumor and its adjacent samples; (iv) expanded lncRNA-associated diseases; (v) optimized the results about lncRNAs structure change induced by variants; (vi) reduced false positives in miRNA and lncRNA interaction results. Furthermore, we developed online tools for users to analyze new variants in lncRNA. We aim to maintain the lncRNASNP as a useful resource for lncRNAs and their variants.

DNA co-methylation analysis of lincRNAs across nine cancer types reveals novel potential epigenetic biomarkers in cancer

Aim: The potential functions and prognostic value of lincRNAs with co-methylation events are explored in 9 cancer types. Materials & methods: Here, we evaluated the co-methylation events in promoter and gene-body regions between two lincRNAs across 9 cancer types by constructing a systematic biological framework. Results: The co-methylation events in both promoter and gene-body regions tended to be highly cancer specific. Patient samples could be separated by tumor and normal types according to the eigengenes of universal co-methylation clusters. Functional enrichment results revealed the lincRNAs that brought promoter and gene-body co-methylation events that affected cancer progress through participating in different pathways and could serve as potential prognostic biomarkers. Conclusion: The study provides new insight into the epigenetic regulation in cancer and leads to a potential new direction for epigenetic biomarker discovery.

Systematic review regulatory principles of non-coding RNAs in cardiovascular diseases

Cardiovascular diseases (CVDs) continue to be a major cause of morbidity and mortality, and non-coding RNAs (ncRNAs) play critical roles in CVDs. With the recent emergence of high-throughput technologies, including small RNA sequencing, investigations of CVDs have been transformed from candidate-based studies into genome-wide undertakings, and a number of ncRNAs in CVDs were discovered in various studies. A comprehensive review of these ncRNAs would be highly valuable for researchers to get a complete picture of the ncRNAs in CVD. To address these knowledge gaps and clinical needs, in this review, we first discussed dysregulated ncRNAs and their critical roles in cardiovascular development and related diseases. Moreover, we reviewed >28 561 published papers and documented the ncRNA-CVD association benchmarking data sets to summarize the principles of ncRNA regulation in CVDs. This data set included 13 249 curated relationships between 9503 ncRNAs and 139 CVDs in 12 species. Based on this comprehensive resource, we summarized the regulatory principles of dysregulated ncRNAs in CVDs, including the complex associations between ncRNA and CVDs, tissue specificity and ncRNA synergistic regulation. The highlighted principles are that CVD microRNAs (miRNAs) are highly expressed in heart tissue and that they play central roles in miRNA-miRNA functional synergistic network. In addition, CVD-related miRNAs are close to one another in the functional network, indicating the modular characteristic features of CVD miRNAs. We believe that the regulatory principles summarized here will further contribute to our understanding of ncRNA function and dysregulation mechanisms in CVDs.

Analysis of lncRNA-miRNA-mRNA Interactions in Hyper-proliferative Human Pulmonary Arterial Smooth Muscle Cells

We previously reported enhanced proliferation of smooth muscle cells on the combined exposure of HIV proteins and cocaine leading to the development of HIV-pulmonary arterial hypertension. Here, we attempt to comprehensively understand the interactions between long noncoding RNAs (lncRNAs), mRNAs and micro-RNAs (miRNAs) to determine their role in smooth muscle hyperplasia. Differential expression of lncRNAs, mRNAs and miRNAs were obtained by microarray and small-RNA sequencing from HPASMCs treated with and without cocaine and/or HIV-Tat. LncRNA to mRNA associations were conjectured by analyzing their genomic proximity and by interrogating their association to vascular diseases and cancer co-expression patterns reported in the relevant databases. Neuro-active ligand receptor signaling, Ras signaling and PI3-Akt pathway were among the top pathways enriched in either differentially expressed mRNAs or mRNAs associated to lncRNAs. HPASMC with combined exposure to cocaine and Tat (C + T) vs control identified the following top lncRNA-mRNA pairs, ENST00000495536-HOXB13, T216482-CBL, ENST00000602736-GDF7, and, TCONS_00020413-RND1. Many of the down-regulated miRNAs in the HPASMCs treated with C + T were found to be anti-proliferative and targets of up-regulated lncRNAs targeting up-regulated mRNAs, including down-regulation of miR-185, -491 and up-regulation of corresponding ENST00000585387. Specific knock down of the selected lncRNAs highlighted the importance of non-coding RNAs in smooth muscle hyperplasia.

Network Diffusion Approach to Predict LncRNA Disease Associations Using Multi-Type Biological Networks: LION

Recently, long-non-coding RNAs (lncRNAs) have attracted attention because of their emerging role in many important biological mechanisms. The accumulating evidence indicates that the dysregulation of lncRNAs is associated with complex diseases. However, only a few lncRNA-disease associations have been experimentally validated and therefore, predicting potential lncRNAs that are associated with diseases become an important task. Current computational approaches often use known lncRNA-disease associations to predict potential lncRNA-disease links. In this work, we exploited the topology of multi-level networks to propose the LncRNA rankIng by NetwOrk DiffusioN (LION) approach to identify lncRNA-disease associations. The multi-level complex network consisted of lncRNA-protein, protein–protein interactions, and protein-disease associations. We applied the network diffusion algorithm of LION to predict the lncRNA-disease associations within the multi-level network. LION achieved an AUC value of 96.8% for cardiovascular diseases, 91.9% for cancer, and 90.2% for neurological diseases by using experimentally verified lncRNAs associated with diseases. Furthermore, compared to a similar approach (TPGLDA), LION performed better for cardiovascular diseases and cancer. Given the versatile role played by lncRNAs in different biological mechanisms that are perturbed in diseases, LION’s accurate prediction of lncRNA-disease associations helps in ranking lncRNAs that could function as potential biomarkers and potential drug targets.

Targeting epigenetics and non-coding RNAs in atherosclerosis: from mechanisms to therapeutics

Atherosclerosis, the principal cause of cardiovascular death worldwide, is a pathological disease characterized by fibro-proliferation, chronic inflammation, lipid accumulation, and immune disorder in the vessel wall. As the atheromatous plaques develop into advanced stage, the vulnerable plaques are prone to rupture, which causes acute cardiovascular events, including ischemic stroke and myocardial infarction. Emerging evidence has suggested that atherosclerosis is also an epigenetic disease with the interplay of multiple epigenetic mechanisms. The epigenetic basis of atherosclerosis has transformed our knowledge of epigenetics from an important biological phenomenon to a burgeoning field in cardiovascular research. Here, we provide a systematic and up-to-date overview of the current knowledge of three distinct but interrelated epigenetic processes (including DNA methylation, histone methylation/acetylation, and non-coding RNAs), in atherosclerotic plaque development and instability. Mechanistic and conceptual advances in understanding the biological roles of various epigenetic modifiers in regulating gene expression and functions of endothelial cells (vascular homeostasis, leukocyte adhesion, endothelial-mesenchymal transition, angiogenesis, and mechanotransduction), smooth muscle cells (proliferation, migration, inflammation, hypertrophy, and phenotypic switch), and macrophages (differentiation, inflammation, foam cell formation, and polarization) are discussed. The inherently dynamic nature and reversibility of epigenetic regulation, enables the possibility of epigenetic therapy by targeting epigenetic "writers", "readers", and "erasers". Several Food Drug Administration-approved small-molecule epigenetic drugs show promise in pre-clinical studies for the treatment of atherosclerosis. Finally, we discuss potential therapeutic implications and challenges for future research involving cardiovascular epigenetics, with an aim to provide a translational perspective for identifying novel biomarkers of atherosclerosis, and transforming precision cardiovascular research and disease therapy in modern era of epigenetics.

Prediction of lncRNA-disease associations by integrating diverse heterogeneous information sources with RWR algorithm and positive pointwise mutual information

BACKGROUND

Long non-coding RNAs play an important role in human complex diseases. Identification of lncRNA-disease associations will gain insight into disease-related lncRNAs and benefit disease diagnoses and treatment. However, using experiments to explore the lncRNA-disease associations is expensive and time consuming.

RESULTS

In this study, we developed a novel method to identify potential lncRNA-disease associations by Integrating Diverse Heterogeneous Information sources with positive pointwise Mutual Information and Random Walk with restart algorithm (namely IDHI-MIRW). IDHI-MIRW first constructs multiple lncRNA similarity networks and disease similarity networks from diverse lncRNA-related and disease-related datasets, then implements the random walk with restart algorithm on these similarity networks for extracting the topological similarities which are fused with positive pointwise mutual information to build a large-scale lncRNA-disease heterogeneous network. Finally, IDHI-MIRW implemented random walk with restart algorithm on the lncRNA-disease heterogeneous network to infer potential lncRNA-disease associations.

CONCLUSIONS

Compared with other state-of-the-art methods, IDHI-MIRW achieves the best prediction performance. In case studies of breast cancer, stomach cancer, and colorectal cancer, 36/45 (80%) novel lncRNA-disease associations predicted by IDHI-MIRW are supported by recent literatures. Furthermore, we found lncRNA LINC01816 is associated with the survival of colorectal cancer patients. IDHI-MIRW is freely available at https://github.com/NWPU-903PR/IDHI-MIRW .

Angiotensin II Signal Transduction: An Update on Mechanisms of Physiology and Pathophysiology

The renin-angiotensin-aldosterone system plays crucial roles in cardiovascular physiology and pathophysiology. However, many of the signaling mechanisms have been unclear. The angiotensin II (ANG II) type 1 receptor (AT₁R) is believed to mediate most functions of ANG II in the system. AT₁R utilizes various signal transduction cascades causing hypertension, cardiovascular remodeling, and end organ damage. Moreover, functional cross-talk between AT₁R signaling pathways and other signaling pathways have been recognized. Accumulating evidence reveals the complexity of ANG II signal transduction in pathophysiology of the vasculature, heart, kidney, and brain, as well as several pathophysiological features, including inflammation, metabolic dysfunction, and aging. In this review, we provide a comprehensive update of the ANG II receptor signaling events and their functional significances for potential translation into therapeutic strategies. AT₁R remains central to the system in mediating physiological and pathophysiological functions of ANG II, and participation of specific signaling pathways becomes much clearer. There are still certain limitations and many controversies, and several noteworthy new concepts require further support. However, it is expected that rigorous translational research of the ANG II signaling pathways including those in large animals and humans will contribute to establishing effective new therapies against various diseases.

Microarray analysis of lncRNA expression in rabies virus infected human neuroblastoma cells

Rabies, caused by the rabies virus (RABV), is the oldest known zoonotic infectious disease. Although the molecular mechanisms of RABV pathogenesis have been investigated extensively, the interactions between host and RABV are not clearly understood. It is now known that long non-coding RNAs (lncRNAs) participate in various physiological and pathological processes, but their possible roles in the host response to RABV infection remain to be elucidated. To better understand the pathogenesis of RABV, RNAs from RABV-infected and uninfected human neuroblastoma cells (SK-N-SH) were analyzed using human lncRNA microarrays. We identified 896 lncRNAs and 579 mRNAs that were differentially expressed after infection, indicating a potential role for lncRNAs in the immune response to RABV. Differentially expressed RNAs were examined using Gene Ontology (GO) analysis and were tentatively assigned to biological pathways using the Kyoto Encyclopedia of Genes and Genomes (KEGG). A lncRNA-mRNA-transcription factor co-expression network was constructed to relate lncRNAs to regulatory factors and pathways that may be important in virus-host interactions. The network analysis suggests that E2F4, TAF7 and several lncRNAs function as transcriptional regulators in various signaling pathways. This study is the first global analysis of lncRNA and mRNA co-expression during RABV infection, provides deeper insight into the mechanism of RABV pathogenesis, and reveals promising candidate for future investigation.

Landscape of SNPs-mediated lncRNA structural variations and their implication in human complex diseases

An increasing number of functional studies shows that long noncoding RNAs (lncRNAs) are involved in many aspects of cellular physiology and fulfills a wide variety of regulatory roles at almost every stage of gene expression. A major feature of lncRNAs is the highly folded modular domains in transcripts. With improved modeling and definition, it is now feasible to explore and gain novel insights into the structural-functional relationship of lncRNAs and their association with complex human diseases. In this study, we utilized an automatic computational pipeline to scan lncRNA architecture at the genome-wide scale and to obtain a landscape of functional domains. An accurate alignment algorithm was performed to identify 40 triple pairs between single-nucleotide polymorphisms (SNPs), lncRNAs and diseases. In order to detect the potential contribution of a lncRNA's modular character, we estimated and evaluated structural rearrangements, which were derived from disease-associated SNPs. In addition, we focused on annotating and comparing the global and local heterogeneity of the wild-type and mutant lncRNAs. Assessing lncRNA architecture has yielded how variations in structured regions impact the molecular mechanisms of lncRNAs and how SNPs disturb binding and recruiting ability. These observations are the first glimpse of the 'lncRNA structurome' and make it possible to robustly explore and assemble intricate space conformation and their stress variation. This result also successfully demonstrates that lncRNA transcripts contain a complex structural landscape and highlights the proposed contribution of lncRNA structure in controlling RNA functions and disease mechanisms.

Integrative Analysis of lncRNAs in Th17 Cell Lineage to Discover New Potential Biomarkers and Therapeutic Targets in Autoimmune Diseases

Th17 cells play a critical role in the pathogenesis of autoimmune diseases, including multiple sclerosis, rheumatoid arthritis, systemic lupus erythematosus, Sjogren's syndrome, and inflammatory bowel disease. Despite the extensive investigation into this T cell lineage, little is understood regarding the role of Th17 lineage-specific lncRNAs (long non-coding RNAs) > 200 nt. lncRNAs may influence disease through a variety of mechanisms; their expression could be regulated by SNPs. lncRNAs can also affect the expression of neighboring genes or complementary miRNAs, and their expression may have lineage-specific patterns. In the system biology study presented here, the effective lncRNAs from different criteria were predicted for each autoimmune disease, and we then evaluated their expression levels in 50 MS patients compared to 25 controls using qRT-PCR. We identified changes in the expression levels of AL450992.2, AC009948.5, and RP11-98D18.3 as potential peripheral blood mononuclear cell (PBMC) biomarkers for MS among our studied lncRNAs in which co-expression analysis of AL450992.2 had the most AUCs, and the relationship to RORC was also assessed. We propose that the recurrently deregulated lncRNAs identified in this report could provide a valuable resource for studies aimed at delineating the relationship between functional lncRNAs and autoimmune disorders.

A Novel Approach for Predicting Disease-lncRNA Associations Based on the Distance Correlation Set and Information of the miRNAs

Recently, accumulating laboratorial studies have indicated that plenty of long noncoding RNAs (lncRNAs) play important roles in various biological processes and are associated with many complex human diseases. Therefore, developing powerful computational models to predict correlation between lncRNAs and diseases based on heterogeneous biological datasets will be important. However, there are few approaches to calculating and analyzing lncRNA-disease associations on the basis of information about miRNAs. In this article, a new computational method based on distance correlation set is developed to predict lncRNA-disease associations (DCSLDA). Comparing with existing state-of-the-art methods, we found that the major novelty of DCSLDA lies in the introduction of lncRNA-miRNA-disease network and distance correlation set; thus DCSLDA can be applied to predict potential lncRNA-disease associations without requiring any known disease-lncRNA associations. Simulation results show that DCSLDA can significantly improve previous existing models with reliable AUC of 0.8517 in the leave-one-out cross-validation. Furthermore, while implementing DCSLDA to prioritize candidate lncRNAs for three important cancers, in the first 0.5% of forecast results, 17 predicted associations are verified by other independent studies and biological experimental studies. Hence, it is anticipated that DCSLDA could be a great addition to the biomedical research field.

lncRNA TNXA-PS1 Modulates Schwann Cells by Functioning As a Competing Endogenous RNA Following Nerve Injury

As the major glia in PNS, Schwann cells play a critical role in peripheral nerve injury repair. Finding an efficient approach to promote Schwann cell activation might facilitate peripheral nerve repair. Long noncoding RNAs (lncRNAs) have been shown to regulate gene expression and take part in many biological processes. However, the role of lncRNAs in peripheral nerve regeneration is not fully understood. In this study, we obtained a global lncRNA portrayal following sciatic nerve injury in male rats using microarray and further investigated one of these dys-regulated lncRNAs, TNXA-PS1, confirming its vital role in regulating Schwann cells. Silencing TNAX-PS1 could promote Schwann cell migration and mechanism analyses showed that TNXA-PS1 might exert its regulatory role by sponging miR-24-3p/miR-152-3p and affecting dual specificity phosphatase 1 (Dusp1) expression. Systematic lncRNA expression profiling of sciatic nerve segments following nerve injury in rats suggested lncRNA TNXA-PS1 as a key regulator of Schwann cell migration, providing a potential therapeutic target for nerve injury repair.SIGNIFICANCE STATEMENT The PNS has an intrinsic regeneration capacity after injury in which Schwann cells play a crucial role. Therefore, further exploration of functional molecules in the Schwann cell phenotype modulation is of great importance. We have identified a set of dys-regulated long noncoding RNAs (lncRNAs) in rats following sciatic nerve injury and found that the expression of TNXA-PS1 was significantly downregulated. Mechanically analyses showed that TNXA-PS1 might act as a competing endogenous RNA to affect dual specificity phosphatase 1 (Dusp1) expression, regulating migration of Schwann cells. This study provides for the first time a global landscape of lncRNAs following sciatic nerve injury in rats and broadens the known functions of lncRNA during nerve injury. The investigation of TNXA-PS1 might facilitate the development of novel targets for nerve injury therapy.

Profiling of Long Non-coding RNAs and mRNAs by RNA-Sequencing in the Hippocampi of Adult Mice Following Propofol Sedation

Propofol is a frequently used intravenous anesthetic agent. The impairment caused by propofol on the neural system, especially the hippocampus, has been widely reported. However, the molecular mechanism underlying the effects of propofol on learning and memory functions in the hippocampus is still unclear. In the present study we performed lncRNA and mRNA analysis in the hippocampi of adult mice, after propofol sedation, through RNA-Sequencing (RNA-Seq). A total of 146 differentially expressed lncRNAs and 1103 mRNAs were identified. Bioinformatics analysis, including gene ontology (GO) analysis, pathway analysis and network analysis, were done for the identified dysregulated genes. Pathway analysis indicated that the FoxO signaling pathway played an important role in the effects of propofol on the hippocampus. Finally, four lncRNAs and three proteins were selected from the FoxO-related network for further validation. The up-regulation of lncE230001N04Rik and the down-regulation of lncRP23-430H21.1 and lncB230206L02Rik showed the same fold change tendencies but changes in Gm26532 were not statistically significant in the RNA-Seq results, following propofol sedation. The FoxO pathway-related proteins, PI3K and AKT, are up-regulated in propofol-exposed group. FoxO3a is down-regulated at both mRNA and protein levels. Our study reveals that propofol sedation can influence the expression of lncRNAs and mRNAs in the hippocampus, and bioinformatics analysis have identified key biological processes and pathways associated with propofol sedation. Cumulatively, our results provide a framework for further study on the role of lncRNAs in propofol-induced or -related neurotoxicity, particularly with regards to hippocampus-related dysfunction.

Systematical analysis of lncRNA-mRNA competing endogenous RNA network in breast cancer subtypes

BACKGROUND

Breast cancer is one of the most common solid tumors in women involving multiple subtypes. However, the mechanism for subtypes of breast cancer is still complicated and unclear. Recently, several studies indicated that long non-coding RNAs (lncRNAs) could act as sponges to compete miRNAs with mRNAs, participating in various biological processes.

METHODS

We concentrated on the competing interactions between lncRNAs and mRNAs in four subtypes of breast cancer (basal-like, HER2+, luminal A and luminal B), and analyzed the impacts of competing endogenous RNAs (ceRNAs) on each subtype systematically. We constructed four breast cancer subtype-related lncRNA-mRNA ceRNA networks by integrating the miRNA target information and the expression data of lncRNAs, miRNAs and mRNAs.

RESULTS

We constructed the ceRNA network for each breast cancer subtype. Functional analysis revealed that the subtype-related ceRNA networks were enriched in cancer-related pathways in KEGG, such as pathways in cancer, miRNAs in cancer, and PI3k-Akt signaling pathway. In addition, we found three common lncRNAs across the four subtype-related ceRNA networks, NEAT1, OPI5-AS1 and AC008124.1, which played specific roles in each subtype through competing with diverse mRNAs. Finally, the potential drugs for treatment of basal-like subtype could be predicted through reversing the differentially expressed lncRNA in the ceRNA network.

CONCLUSION

This study provided a novel perspective of lncRNA-involved ceRNA network to dissect the molecular mechanism for breast cancer.

MNDR v2.0: an updated resource of ncRNA-disease associations in mammals

Accumulating evidence suggests that diverse non-coding RNAs (ncRNAs) are involved in the progression of a wide variety of diseases. In recent years, abundant ncRNA-disease associations have been found and predicted according to experiments and prediction algorithms. Diverse ncRNA-disease associations are scattered over many resources and mammals, whereas a global view of diverse ncRNA-disease associations is not available for any mammals. Hence, we have updated the MNDR v2.0 database (www.rna-society.org/mndr/) by integrating experimental and prediction associations from manual literature curation and other resources under one common framework. The new developments in MNDR v2.0 include (i) an over 220-fold increase in ncRNA-disease associations enhancement compared with the previous version (including lncRNA, miRNA, piRNA, snoRNA and more than 1400 diseases); (ii) integrating experimental and prediction evidence from 14 resources and prediction algorithms for each ncRNA-disease association; (iii) mapping disease names to the Disease Ontology and Medical Subject Headings (MeSH); (iv) providing a confidence score for each ncRNA-disease association and (v) an increase of species coverage to six mammals. Finally, MNDR v2.0 intends to provide the scientific community with a resource for efficient browsing and extraction of the associations between diverse ncRNAs and diseases, including >260 000 ncRNA-disease associations.

Predicting binary, discrete and continued lncRNA-disease associations via a unified framework based on graph regression

BACKGROUND

In human genomes, long non-coding RNAs (lncRNAs) have attracted more and more attention because their dysfunctions are involved in many diseases. However, the associations between lncRNAs and diseases (LDA) still remain unknown in most cases. While identifying disease-related lncRNAs in vivo is costly, computational approaches are promising to not only accelerate the possible identification of associations but also provide clues on the underlying mechanism of various lncRNA-caused diseases. Former computational approaches usually only focus on predicting new associations between lncRNAs having known associations with diseases and other lncRNA-associated diseases. They also only work on binary lncRNA-disease associations (whether the pair has an association or not), which cannot reflect and reveal other biological facts, such as the number of proteins involved in LDA or how strong the association is (i.e., the intensity of LDA).

RESULTS

To address abovementioned issues, we propose a graph regression-based unified framework (GRUF). In particular, our method can work on lncRNAs, which have no previously known disease association and diseases that have no known association with any lncRNAs. Also, instead of only a binary answer for the association, our method tries to uncover more biological relationship between a pair of lncRNA and disease, which may provide better clues for researchers. We compared GRUF with three state-of-the-art approaches and demonstrated the superiority of GRUF, which achieves 5%~16% improvement in terms of the area under the receiver operating characteristic curve (AUC). GRUF also provides a predicted confidence score for the predicted LDA, which reveals the significant correlation between the score and the number of RNA-Binding Proteins involved in LDAs. Lastly, three out of top-5 LDA candidates generated by GRUF in novel prediction are verified indirectly by medical literature and known biological facts.

CONCLUSIONS

The proposed GRUF has two advantages over existing approaches. Firstly, it can be used to work on lncRNAs that have no known disease association and diseases that have no known association with any lncRNAs. Secondly, instead of providing a binary answer (with or without association), GRUF works for both discrete and continued LDA, which help revealing the pathological implications between lncRNAs and diseases.

Chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes

Disease-related protein-coding genes have been widely studied, but disease-related non-coding genes remain largely unknown. This work introduces a new vector to represent diseases, and applies the newly vectorized data for a positive-unlabeled learning algorithm to predict and rank disease-related long non-coding RNA (lncRNA) genes. This novel vector representation for diseases consists of two sub-vectors, one is composed of 45 elements, characterizing the information entropies of the disease genes distribution over 45 chromosome substructures. This idea is supported by our observation that some substructures (e.g., the chromosome 6 p-arm) are highly preferred by disease-related protein coding genes, while some (e.g., the 21 p-arm) are not favored at all. The second sub-vector is 30-dimensional, characterizing the distribution of disease gene enriched KEGG pathways in comparison with our manually created pathway groups. The second sub-vector complements with the first one to differentiate between various diseases. Our prediction method outperforms the state-of-the-art methods on benchmark datasets for prioritizing disease related lncRNA genes. The method also works well when only the sequence information of an lncRNA gene is known, or even when a given disease has no currently recognized long non-coding genes.