Long non-coding RNAs in Huntington's disease neurodegeneration

Comparison of Poly-A+ Selection and rRNA Depletion in Detection of lncRNA in Two Equine Tissues Using RNA-seq

Long non-coding RNAs (lncRNAs) are untranslated regulatory transcripts longer than 200 nucleotides that can play a role in transcriptional, post-translational, and epigenetic regulation. Traditionally, RNA-sequencing (RNA-seq) libraries have been created by isolating transcriptomic RNA via poly-A+ selection. In the past 10 years, methods to perform ribosomal RNA (rRNA) depletion of total RNA have been developed as an alternative, aiming for better coverage of whole transcriptomic RNA, both polyadenylated and non-polyadenylated transcripts. The purpose of this study was to determine which library preparation method is optimal for lncRNA investigations in the horse. Using liver and cerebral parietal lobe tissues from two healthy Thoroughbred mares, RNA-seq libraries were prepared using standard poly-A+ selection and rRNA-depletion methods. Averaging the two biologic replicates, poly-A+ selection yielded 327 and 773 more unique lncRNA transcripts for liver and parietal lobe, respectively. More lncRNA were found to be unique to poly-A+ selected libraries, and rRNA-depletion identified small nucleolar RNA (snoRNA) to have a higher relative expression than in the poly-A+ selected libraries. Overall, poly-A+ selection provides a more thorough identification of total lncRNA in equine tissues while rRNA-depletion may allow for easier detection of snoRNAs.

Circulating nucleic acids in the plasma and serum as potential biomarkers in neurological disorders

Neurological diseases are responsible for approximately 6.8 million deaths every year. They affect up to 1 billion people worldwide and cause significant disability and reduced quality of life. In most neurological disorders, the diagnosis can be challenging; it frequently requires long-term investigation. Thus, the discovery of better diagnostic methods to help in the accurate and fast diagnosis of neurological disorders is crucial. Circulating nucleic acids (CNAs) are defined as any type of DNA or RNA that is present in body biofluids. They can be found within extracellular vesicles or as cell-free DNA and RNA. Currently, CNAs are being explored as potential biomarkers for diseases because they can be obtained using non-invasive methods and may reflect unique characteristics of the biological processes involved in several diseases. CNAs can be especially useful as biomarkers for conditions that involve organs or structures that are difficult to assess, such as the central nervous system. This review presents a critical assessment of the most current literature about the use of plasma and serum CNAs as biomarkers for several aspects of neurological disorders: defining a diagnosis, establishing a prognosis, and monitoring the disease progression and response to therapy. We explored the biological origin, types, and general mechanisms involved in the generation of CNAs in physiological and pathological processes, with specific attention to neurological disorders. In addition, we present some of the future applications of CNAs as non-invasive biomarkers for these diseases.

LncR2metasta: a manually curated database for experimentally supported lncRNAs during various cancer metastatic events

Mounting evidence has shown the involvement of long non-coding RNAs (lncRNAs) during various cancer metastatic events (abbreviated as CMEs, e.g. cancer cell invasion, intravasation, extravasation, proliferation, etc.) that may cooperatively facilitate malignant tumor spread and cause massive patient deaths. The study of lncRNA-CME associations might help understand lncRNA functions in metastasis and present reliable biomarkers for early dissemination detection and optimized treatment. Therefore, we developed a database named 'lncR2metasta' by manually compiling experimentally supported lncRNAs during various CMEs from existing studies. LncR2metasta documents 1238 associations between 304 lncRNAs and 39 CMEs across 54 human cancer subtypes. Each entry of lncR2metasta contains detailed information on a lncRNA-CME association, including lncRNA symbol, a specific CME, brief description of the association, lncRNA category, lncRNA Entrez or Ensembl ID, lncRNA genomic location and strand, lncRNA experiment, lncRNA expression pattern, detection method, target gene (or pathway) of lncRNA, lncRNA regulatory role on a CME, cancer name and the literature reference. An easy-to-use web interface was deployed in lncR2metasta for its users to easily browse, search and download as well as to submit novel lncRNA-CME associations. LncR2metasta will be a useful resource in cancer research community. It is freely available at http://lncR2metasta.wchoda.com.

Large-Scale Analysis Reveals the Specific Clinical and Immune Features of DGCR5 in Glioma

Purpose

Long non-coding RNA DGCR5 plays different roles in different types of cancer. The purpose of this study was to investigate the clinicopathological features, potential biological functions and prognostic significance of DGCR5 in glioma in a large-scale study.

Materials and Methods

A total of 697 RNA-seq data from The Cancer Genome Atlas (TCGA) and 301 mRNA microarray data from Chinese Glioma Genome Atlas (CGGA) were enrolled in this study. R language was used as the main tool for statistical analysis and graphical work.

Results

DGCR5 showed a negative correlation with the WHO grade of malignancy in glioma. Specifically, DGCR5 expression was significantly decreased in GBM and IDH wild-type glioma. Gene ontology analysis showed that DGCR5 was predominantly enriched in immune-related biological processes. Additionally, DGCR5 showed a significant correlation with stromal and immune cell populations, inflammatory activities and immune checkpoints. Clinically, patients with low-expression level of DGCR5 exhibited a worse overall survival.

Conclusion

DGCR5 expression is downregulated in glioma, and low DGCR5 independently predicts worse prognosis in glioma patients. Moreover, DGCR5 is significantly associated with immune response and immune infiltration. These findings suggest that DGCR5 is a promising immunotherapy target and a novel prognostic biomarker for glioma.

RNA-seq analysis of the key long noncoding RNAs and mRNAs related to cognitive impairment after cardiac arrest and cardiopulmonary resuscitation

Cardiac arrest (CA) is the leading cause of death around the world. Survivors after CA and cardiopulmonary resuscitation (CPR) develop moderate to severe cognitive impairment up to 60% at 3 months. Accumulating evidence demonstrated that long non-coding RNAs (lncRNAs) played a pivotal role in ischemic brain injury. This study aimed to identify potential key lncRNAs associated with early cognitive deficits after CA/CPR. LncRNA and mRNA expression profiles of the hippocampus in CA/CPR or sham group were analyzed via high-throughput RNA sequencing, which exhibited 1920 lncRNAs and 1162 mRNAs were differentially expressed. These differentially expressed genes were confirmed to be primarily associated with inflammatory or apoptotic signaling pathways through GO and KEGG pathway enrichment analysis and coding-noncoding co-expression network analysis. Among which, five key pairs of lncRNA-mRNA were further analyzed by qRT-PCR and western blot. We found that the lncRNANONMMUT113601.1 and mRNA Shc1, an inflammation and apoptosis-associated gene, exhibited the most significant changes in hippocampus of CA/CPR mice. Furthermore, we found that the correlations between this lncRNA and mRNA mainly happened in neurons of hippocampus by in situ hybridization. These results suggested that the critical pairs of lncRNA-mRNA may act as essential regulators in early cognitive deficits after resuscitation.

LncRNA NEAT1 in Paraspeckles: A Structural Scaffold for Cellular DNA Damage Response Systems?

Nuclear paraspeckle assembly transcript 1 (NEAT1) is a long non-coding RNA (lncRNA) reported to be frequently deregulated in various types of cancers and neurodegenerative processes. NEAT1 is an indispensable structural component of paraspeckles (PSs), which are dynamic and membraneless nuclear bodies that affect different cellular functions, including stress response. Furthermore, increasing evidence supports the crucial role of NEAT1 and essential structural proteins of PSs (PSPs) in the regulation of the DNA damage repair (DDR) system. This review aims to provide an overview of the current knowledge on the involvement of NEAT1 and PSPs in DDR, which might strengthen the rationale underlying future NEAT1-based therapeutic options in tumor and neurodegenerative diseases.

A novel graph attention adversarial network for predicting disease-related associations

Identifying complex human diseases at molecular level is very helpful, especially in diseases diagnosis, therapy, prognosis and monitoring. Accumulating evidences demonstrated that RNAs are playing important roles in identifying various complex human diseases. However, the amount of verified disease-related RNAs is still little while many of their biological experiments are very time-consuming and labor-intensive. Therefore, researchers have instead been seeking to develop effective computational algorithms to predict associations between diseases and RNAs. In this paper, we propose a novel model called Graph Attention Adversarial Network (GAAN) for the potential disease-RNA association prediction. To our best knowledge, we are among the pioneers to integrate successfully both the state-of-the-art graph convolutional networks (GCNs) and attention mechanism in our model for the prediction of disease-RNA associations. Comparing to other disease-RNA association prediction methods, GAAN is novel in conducting the computations from the aspect of global structure of disease-RNA network with graph embedding while integrating features of local neighborhoods with the attention mechanism. Moreover, GAAN uses adversarial regularization to further discover feature representation distribution of the latent nodes in disease-RNA networks. GAAN also benefits from the efficiency of deep model for the computation of big associations networks. To evaluate the performance of GAAN, we conduct experiments on networks of diseases associating with two different RNAs: MicroRNAs (miRNAs) and Long non-coding RNAs (lncRNAs). Comparisons of GAAN with several popular baseline methods on disease-RNA networks show that our novel model outperforms others by a wide margin in predicting potential disease-RNAs associations.

Role of Long Noncoding RNAs in Parkinson's Disease: Putative Biomarkers and Therapeutic Targets

Parkinson's disease (PD) is a neurodegenerative disease characterized by bradykinesia, rigidity, and tremor. Age is the main risk factor. Long noncoding RNAs (lncRNAs) are novel RNA molecules of more than 200 nucleotides in length. They may be involved in the regulation of many pathological processes of PD. PD has a variety of pathophysiological mechanisms, including alpha-synuclein aggregate, mitochondrial dysfunction, oxidative stress, calcium homeostasis, axonal transport, and neuroinflammation. Among these, the impacts of lncRNAs on the pathogenesis and progression of PD need to be highlighted. lncRNAs may serve as putative biomarkers and therapeutic targets for the early diagnosis of PD. This study aimed to investigate the role of lncRNAs in various pathological processes of PD and the specific lncRNAs that might be used as putative diagnostic biomarkers and therapeutic targets of PD.

MLCDForest: multi-label classification with deep forest in disease prediction for long non-coding RNAs

The long non-coding RNAs (lncRNAs) are subject of intensive recent studies due to its association with various human diseases. It is desirable to build the artificial intelligence-based models for prediction of diseases or tissues based on the lncRNAs data, which will be useful in disease diagnosis and therapy. The accuracy and robustness of existing models based on the machine learning techniques are subject to further improvement. In this study, we propose a deep learning model, called Multi-Label Classifications with Deep Forest, termed MLCDForest, to address multi-label classification on tissue prediction for a given lncRNA, which can be regarded as an implementation of the deep forest model in multi-label classification. The MLCDForest is a sequential multi-label-grained scanning method, which distinguishes from the standard deep forest model. It is proposed to train in sequential of multi-labels with label correlation considered. A systematic comparison using the lncRNA-disease association datasets demonstrates that our method consistently shows superior performance over the state-of-the-art methods in disease prediction. Considering label correlation in the sequential multi-label-grained scanning, our model provides a powerful tool to make multi-label classification and tissue prediction based on given lncRNAs.

Long noncoding RNA NEAT1 knockdown inhibits MPP+-induced apoptosis, inflammation and cytotoxicity in SK-N-SH cells by regulating miR-212-5p/RAB3IP axis

BACKGROUND

Some long non-coding RNAs (lncRNAs) have been suggested to play critical roles in Parkinson's disease (PD) pathogenesis, including nuclear enriched abundant transcript 1 (NEAT1). The purpose of this study was to further elucidate the molecular mechanism of NEAT1 in PD.

METHODS

The expression levels of NEAT1, miR-212-5p and RAB3A-interacting protein (RAB3IP) were determined by quantitative real-time polymerase chain reaction (qRT-PCR). Cell viability and apoptosis were evaluated by Cell Counting Kit-8 (CCK-8) assay and flow cytometry analysis, respectively. Western blot analysis was applied to detect the protein expression of IL-1β, TNF-α and RAB3IP. The LDH activity, ROS generation and SOD activity were measured by Lactate LDH activity assay kit, ROS assay kit, and SOD activity assay kit, respectively. Dual-luciferase reporter assay and RNA immunoprecipitation (RIP) assay were performed to verify the relationship between miR-212-5p and NEAT1 or mRNA of RAB3IP. 1-methyl-4-phenylpyridinium ion (MPP⁺)-treated SK-N-SH cells were used as an in vitro model of PD.

RESULTS

NEAT1 and RAB3IP were upregulated while miR-212-5p was downregulated in SK-N-SH cells treated with MPP⁺. NEAT1 knockdown or miR-212-5p overexpression inhibited MPP⁺-induced apoptosis, inflammation and cytotoxicity in SK-N-SH cells. Moreover, miR-212-5p was a direct target of NEAT1 and its downregulation reversed the eff ;ects caused by NEAT1 knockdown in MPP⁺-induced SK-N-SH cells. Furthermore, RAB3IP was a downstream target of miR-212-5p and its overexpression attenuated the effects of miR-212-5p restoration in MPP⁺-induced SK-N-SH cells. Besides, NEAT1 acted as a molecular sponge of miR-212-5p to regulate RAB3IP expression.

CONCLUSION

NEAT1 knockdown suppressed MPP⁺-induced apoptosis, inflammation and cytotoxicity in SK-N-SH cells through regulating miR-212-5p and RAB3IP expression, providing a possible therapeutic strategy for PD patients.

LncRNA2Target v2.0: a comprehensive database for target genes of lncRNAs in human and mouse

Long non-coding RNAs (lncRNAs) play crucial roles in regulating gene expression, and a growing number of researchers have focused on the identification of target genes of lncRNAs. However, no online repository is available to collect the information on target genes regulated by lncRNAs. To make it convenient for researchers to know what genes are regulated by a lncRNA of interest, we developed a database named lncRNA2Target to provide a comprehensive resource of lncRNA target genes in 2015. To update the database this year, we retrieved all new lncRNA-target relationships from papers published from 1 August 2014 to 30 April 2018 and RNA-seq datasets before and after knockdown or overexpression of a specific lncRNA. LncRNA2Target database v2.0 provides a web interface through which its users can search for the targets of a particular lncRNA or for the lncRNAs that target a particular gene, and is freely accessible at http://123.59.132.21/lncrna2target.

Downregulated Long Noncoding RNA DGCR5 Acts as a New Promising Biomarker for the Diagnosis and Prognosis of Ovarian Cancer

Emerging evidence have indicated that dysregulated long noncoding ribonucleic acids act as a novel diagnostic and therapeutic target in the progression of ovarian cancer. Long noncoding RNA DiGeorge syndrome critical region gene 5 has been reported to participate in some types of human cancer progresses, but its clinical roles in ovarian cancer had been rarely reported. This study aimed to explore the expression, clinicopathological features, diagnostic, and prognostic values of DiGeorge syndrome critical region gene 5 in ovarian cancer. The total levels of DiGeorge syndrome critical region gene 5 transcript variant 1 (NR_002733.2) and 2 (NR_045121.1) in patients with ovarian cancer were determined by quantitative reverse transcription polymerase chain reaction. The correlation of DiGeorge syndrome critical region gene 5 expression with clinicopathological factors was statistically analyzed by χ² test. Overall survival analysis was carried out with the Kaplan–Meier curves with the log-rank test. Univariate and multivariate Cox regression analyses were performed to identify the prognostic significance of DiGeorge syndrome critical region gene 5 expression. Receiver operating characteristic curves were constructed to estimate the diagnostic and prognostic usefulness of DiGeorge syndrome critical region gene 5 in ovarian cancer. Results showed that relative DiGeorge syndrome critical region gene 5 expression was reduced by 36.81% and 65.79% in ovarian cancer tissues of patients and Gene Expression Omnibus DataSets (GSE119056) in contrast to normal tissues, respectively. Patients with lymph node metastasis and distant metastasis exhibited lower levels of DiGeorge syndrome critical region gene 5 in contrast to those patients with non-lymph node metastasis and non-distant metastasis, respectively. Low expression of DiGeorge syndrome critical region gene 5 was significantly associated with large tumor size, more lymph node metastasis, present distant metastasis, advanced clinical stage, and short overall survival in patients with ovarian cancer. Low expression of DiGeorge syndrome critical region gene 5 was an independent unfavorable prognostic factor for overall survival in patients with ovarian cancer. Receiver operating characteristics curves for prognosis yielded significant area under curves for lymph node metastasis, clinical stage, and overall survival. In conclusion, our study demonstrated that downregulated DiGeorge syndrome critical region gene 5 may be a new promising biomarker for predicting clinical progression and prognosis in patients with ovarian cancer.

Epigenetic changes during ageing and their underlying mechanisms

As life expectancy increases worldwide, ageing and age-related diseases arise as a major issue for societies around the globe. Understanding the biological mechanisms underlying the ageing process is thus instrumental for the development of efficient interventions aimed to prevent and treat age-related conditions. Current knowledge in the biogerontology field points to epigenetics as a critical component of the ageing process, not only by serving as a bona-fide marker of biological age but also by controlling and conferring inheritability to cellular and organismal ageing. This is reflected by a myriad of evidences demonstrating the relationship between DNA methylation, histone modifications, chromatin remodeling and small non-coding RNAs and several age-related phenotypes. Given the reversibility of epigenetic alterations, epigenetic reprogramming may also be envisioned as a potential approach to treat age-related disorders. Here we review how different types of epigenetic mechanisms are involved in the ageing process. In addition, we highlight how interventions modulate epigenetics and thus promote health- and lifespan.

Functional roles and networks of non-coding RNAs in the pathogenesis of neurodegenerative diseases

Recent transcriptome analyses have revealed that noncoding RNAs (ncRNAs) are broadly expressed in mammalian cells and abundant in the CNS, with tissue and cell type-specific expression patterns. Moreover, ncRNAs have been found to intricately and dynamically regulate various signaling pathways in neurodegeneration. As such, some antisense transcripts and microRNAs are known to directly affect neurodegeneration in disease contexts. The functions of ncRNAs in pathogenesis are unique for each disorder, as are the pertinent networks of ncRNA/miRNA/mRNA that mediate these functions. Thus, further understanding of ncRNA biogenesis and effects might aid the discovery of diagnostic biomarkers or development of effective therapeutics for neurodegenerative disorders. Here, we review the ncRNAs that have so far been identified in major neurodegenerative disease etiology and the mechanisms that link ncRNAs with disease-specific phenotypes, such as HTT aggregation in HD, α-synuclein in PD, and Aβ plaques and hyperphosphorylated Tau in AD. We also summarize the known lncRNA/miRNA/mRNA networks that participate in neurodegenerative diseases, and we discuss ncRNA-related treatments shown to delay disease onset and prolong lifespan in rodent models.

Expedition to the missing link: Long noncoding RNAs in cardiovascular diseases

With the advances in deep sequencing-based transcriptome profiling technology, it is now known that human genome is transcribed more pervasively than previously thought. Up to 90% of the human DNA is transcribed, and a large proportion of the human genome is transcribed as long noncoding RNAs (lncRNAs), a heterogenous group of non-coding transcripts longer than 200 nucleotides. Emerging evidence suggests that lncRNAs are functional and contribute to the complex regulatory networks involved in cardiovascular development and diseases. In this article, we will review recent evidence on the roles of lncRNAs in the biological processes of cardiovascular development and disorders. The potential applications of lncRNAs as biomarkers and targets for therapeutics are also discussed.

Long non-coding RNAs in development and disease: conservation to mechanisms

Our genomes contain the blueprint of what makes us human and many indications as to why we develop disease. Until the last 10 years, most studies had focussed on protein-coding genes, more specifically DNA sequences coding for proteins. However, this represents less than 5% of our genomes. The other 95% is referred to as the 'dark matter' of our genomes, our understanding of which is extremely limited. Part of this 'dark matter' includes regions that give rise to RNAs that do not code for proteins. A subset of these non-coding RNAs are long non-coding RNAs (lncRNAs), which in particular are beginning to be dissected and their importance to human health revealed. To improve our understanding and treatment of disease it is vital that we understand the molecular and cellular function of lncRNAs, and how their misregulation can contribute to disease. It is not yet clear what proportion of lncRNAs is actually functional; conservation during evolution is being used to understand the biological importance of lncRNA. Here, we present key themes within the field of lncRNAs, emphasising the importance of their roles in both the nucleus and the cytoplasm of cells, as well as patterns in their modes of action. We discuss their potential functions in development and disease using examples where we have the greatest understanding. Finally, we emphasise why lncRNAs can serve as biomarkers and discuss their emerging potential for therapy. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

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.

A learning based framework for diverse biomolecule relationship prediction in molecular association network

Abundant life activities are maintained by various biomolecule relationships in human cells. However, many previous computational models only focus on isolated objects, without considering that cell is a complete entity with ample functions. Inspired by holism, we constructed a Molecular Associations Network (MAN) including 9 kinds of relationships among 5 types of biomolecules, and a prediction model called MAN-GF. More specifically, biomolecules can be represented as vectors by the algorithm called biomarker2vec which combines 2 kinds of information involved the attribute learned by k-mer, etc and the behavior learned by Graph Factorization (GF). Then, Random Forest classifier is applied for training, validation and test. MAN-GF obtained a substantial performance with AUC of 0.9647 and AUPR of 0.9521 under 5-fold Cross-validation. The results imply that MAN-GF with an overall perspective can act as ancillary for practice. Besides, it holds great hope to provide a new insight to elucidate the regulatory mechanisms.

Guo et al. construct a large scale Molecular Associations Network (MAN) including 9 kinds of associations among 5 types of biomolecules, namely protein, miRNA, lncRNA, disease and drug. They further propose a computational model, MAN-GF, that can predict links between these biomolecules and displays a substantial performance under 5-fold cross validation.

Locate-R: Subcellular localization of long non-coding RNAs using nucleotide compositions

Knowledge of the sub-cellular localization of the most diverse class of transcribed RNA, long non-coding RNAs (lncRNAs) will lead us to identify different types of cancers and other diseases as lncRNAs play key role in related cellular functions. In recent days with the exponential growth of known records, it becomes essential to establish new machine learning based techniques to identify the new one due to faster and cheaper solutions provided compared to laboratory methods. In this paper, we propose Locate-R, a novel method for predicting the sub-cellular location of lncRNAs. We have used only n-gapped l-mer composition and l-mer composition as features and select best 655 features to build the model. This model is based locally deep support vector machines which significantly enhance the prediction accuracy with respect to exiting state-of-the-art methods. Our predictor is readily available for use as a stand-alone web application from: http://locate-r.azurewebsites.net/.

Long non-coding RNAs: How to regulate the metastasis of non-small-cell lung cancer

Non–small‐cell lung cancer (NSCLC) has become the most lethal human cancer because of the high rate of metastasis. Hence, clarifying the molecular mechanism underlying NSCLC metastasis is very important to improve the prognosis of patients with NSCLC. Long non‐coding RNAs (LncRNAs) are a class of RNA molecules longer than 200 nucleotides, which can participate in diverse biological processes. About 18% of human LncRNAs were recently found to be associated with tumours. Many studies indicated that aberrant expression of LncRNAs played key roles in the progression and metastasis of NSCLC. According to the function in tumours, LncRNAs can be divided into two classes: oncogenic LncRNAs and tumour‐suppressor LncRNAs. In this review, we summarized the main molecular mechanism of LncRNAs regulating NSCLC metastasis, including three aspects: (a) LncRNAs interact with miRNAs as ceRNAs; (b) LncRNAs bind with target proteins; and (c) LncRNAs participate in the transduction of different signal pathways. Then, LncRNAs can exert their function to regulate the metastasis of NSCLC through influencing the progression of epithelial‐mesenchymal transition (EMT) and the properties of cancer stem cell (CSC). But, it is necessary to do some further research to demonstrate the LncRNAs particular regulatory mechanism of inhibiting the metastasis of NSCLC and explore new drugs targeting LncRNAs.

Identification of N6-methyladenosine-related lncRNAs for patients with primary glioblastoma

To investigate the m⁶a-related long non-coding RNAs (lncRNAs) that may be exploited as potential biomarkers in primary glioblastoma (pGBM), a cohort of 268 glioma samples from GSE16011 dataset was included for discovery. The Chinese Glioma Genome Atlas (CGGA) microarray and RNA sequencing databases were used for validation. Bioinformatic analyses were performed using the R software. The m⁶a-lncRNA co-expression networks were constructed, and four m⁶a-related lncRNAs (MIR9-3HG, LINC00900, MIR155HG, and LINC00515) were identified in pGBM patients on the univariate Cox regression analysis. Patients in the low-risk group had longer overall survival (OS) and progression-free survival (PFS) than those in the high-risk group (P = 0.0025, P = 0.0070). Moreover, the high-risk group displayed older age, isocitrate dehydrogenase (IDH) wild-type, classical and mesenchymal TCGA subtype, and G3 CGGA subtype. Distinct m⁶a status was identified according to histologic grade and two groups (low-risk and high-risk). Functional annotation showed that differentially expressed genes between the two groups were enriched in immune response, apoptosis, cell adhesion, negative regulation of transcription, negative regulation of RNA metabolic process, and regulation of RNA metabolic process. We profiled the m⁶a status in glioma and identified four m⁶a-related prognostic lncRNAs for pGBMs.

Increased level of NEAT1 long non-coding RNA is detectable in peripheral blood cells of patients with Parkinson's disease

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder that poses serious burden to individuals and society as well. Although a number of PD associated genetic factors have been identified, the molecular mechanism of the disease so far has not been completely elucidated. Involvement of long non-coding RNAs (lncRNAs) in the pathology of neurodegenerative disorders is attracting increased interest because of the diverse mechanisms lncRNAs affect gene expression and cellular homeostasis at different levels. We aimed to test the feasibility of detecting alterations in lncRNA levels in easily accessible samples of PD patients by routine laboratory technique. By narrowing the number of selected lncRNAs implicated in neurodegeneration and increasing the number of PD samples included, we found one out of 41 lncRNAs readily detectable in increased level in peripheral blood of PD patients. We detected NEAT1 to be significantly up-regulated in PD patients in multiple comparisons. NEAT1 is the core element of nuclear paraspeckles and it plays role in regulation of transcription, mRNA and miRNA levels, mitochondrial and cellular homeostasis. Our finding is in accord with recent data demonstrating changes in the level of NEAT1 in neurons of PD patients and in several models of the disease. However, to our knowledge this is the first study to report NEAT1 up-regulation in blood of PD patients. Identification of altered expression of this lncRNA in the periphery might help to a better understanding of the mechanisms underlying PD, and can contribute to the identification of new therapeutic targets and disease markers.

Computational models for lncRNA function prediction and functional similarity calculation

From transcriptional noise to dark matter of biology, the rapidly changing view of long non-coding RNA (lncRNA) leads to deep understanding of human complex diseases induced by abnormal expression of lncRNAs. There is urgent need to discern potential functional roles of lncRNAs for further study of pathology, diagnosis, therapy, prognosis, prevention of human complex disease and disease biomarker detection at lncRNA level. Computational models are anticipated to be an effective way to combine current related databases for predicting most potential lncRNA functions and calculating lncRNA functional similarity on the large scale. In this review, we firstly illustrated the biological function of lncRNAs from five biological processes and briefly depicted the relationship between mutations or dysfunctions of lncRNAs and human complex diseases involving cancers, nervous system disorders and others. Then, 17 publicly available lncRNA function-related databases containing four types of functional information content were introduced. Based on these databases, dozens of developed computational models are emerging to help characterize the functional roles of lncRNAs. We therefore systematically described and classified both 16 lncRNA function prediction models and 9 lncRNA functional similarity calculation models into 8 types for highlighting their core algorithm and process. Finally, we concluded with discussions about the advantages and limitations of these computational models and future directions of lncRNA function prediction and functional similarity calculation. We believe that constructing systematic functional annotation systems is essential to strengthen the prediction accuracy of computational models, which will accelerate the identification process of novel lncRNA functions in the future.

Long Noncoding RNAs in Development and Regeneration of the Neural Lineage

Long noncoding RNAs (lncRNAs) are gathering increasing attention toward their roles in different biological systems. In mammals, the richest repertoires of lncRNAs are expressed in the brain and in the testis, and the diversity of lncRNAs in the nervous system is thought to be related to the diversity and the complexity of its cell types. Supporting this notion, many lncRNAs are differentially expressed between different regions of the brain or in particular cell types, and many lncRNAs are dynamically expressed during embryonic or postnatal neurogenesis. Less is known about the functions of these genes, if any, but they are increasingly implicated in diverse processes in health and disease. Here, we review the current knowledge about the roles and importance of lncRNAs in the central and peripheral nervous systems and discuss the specific niches within gene regulatory networks that might be preferentially occupied by lncRNAs.

The long non-coding RNA TUG1-miR-9a-5p axis contributes to ischemic injuries by promoting cardiomyocyte apoptosis via targeting KLF5

Non-coding RNAs participate in many cardiac pathophysiological processes, including myocardial infarction (MI). Here we showed the interplay between long non-coding RNA taurine-upregulated gene 1 (lncR-TUG1), miR-9a-5p (miR-9) and Krüppel-like factor 5 (KLF5). LncR-TUG1 was upregulated in ischemic heart and in cultured cardiomyocytes exposed to H₂O₂. Knockdown of lncR-TUG1 markedly ameliorated impaired cardiac function of MI mice. Further study showed that lncR-TUG1 acted as a competitive endogenous RNA of miR-9, and silencing of lncR-TUG1 inhibited cardiomyocyte apoptosis by upregulating miR-9 expression. Furthermore, the miR-9 overexpression obviously prevented ischemia injury and significantly inhibited H₂O₂-induced cardiomyocyte apoptosis via inhibition of mitochondrial apoptotic pathway. KLF5, as a target gene of miR-9 by dual-luciferase reporter assay, was involved in the process of miR-9 in regulating cardiomyocyte apoptosis. Our data identified the KLF5 was downregulated by miR-9 overexpression and knockdown of KLF5 inhibited cardiomyocyte apoptosis induced by H₂O₂. MiR-9 exerts anti-cardiomyocyte apoptotic affects by targeting KLF5. Collectively, our data identify a novel function of lncR-TUG1/miR-9/KLF5 axis in regulating cardiomyocyte apoptosis that affects myocardial infarction progression.

iLoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC

Motivation

Long non-coding RNAs (lncRNAs) are a class of RNA molecules with more than 200 nucleotides. They have important functions in cell development and metabolism, such as genetic markers, genome rearrangements, chromatin modifications, cell cycle regulation, transcription and translation. Their functions are generally closely related to their localization in the cell. Therefore, knowledge about their subcellular locations can provide very useful clues or preliminary insight into their biological functions. Although biochemical experiments could determine the localization of lncRNAs in a cell, they are both time-consuming and expensive. Therefore, it is highly desirable to develop bioinformatics tools for fast and effective identification of their subcellular locations.

Results

We developed a sequence-based bioinformatics tool called 'iLoc-lncRNA' to predict the subcellular locations of LncRNAs by incorporating the 8-tuple nucleotide features into the general PseKNC (Pseudo K-tuple Nucleotide Composition) via the binomial distribution approach. Rigorous jackknife tests have shown that the overall accuracy achieved by the new predictor on a stringent benchmark dataset is 86.72%, which is over 20% higher than that by the existing state-of-the-art predictor evaluated on the same tests.

Availability and implementation

A user-friendly webserver has been established at http://lin-group.cn/server/iLoc-LncRNA, by which users can easily obtain their desired results.

Supplementary information

Supplementary data are available at Bioinformatics online.

Emerging Roles of Long Non-coding RNAs in Chronic Neuropathic Pain

Chronic neuropathic pain, a type of chronic and potentially disabling pain caused by a disease or injury of the somatosensory nervous system, spinal cord injury, or various chronic conditions, such as viral infections (e.g., post-herpetic neuralgia), autoimmune diseases, cancers, and metabolic disorders (e.g., diabetes mellitus), is one of the most intense types of chronic pain, which incurs a major socio-economic burden and is a serious public health issue, with an estimated prevalence of 7–10% in adults throughout the world. Presently, the available drug treatments (e.g., anticonvulsants acting at calcium channels, serotonin-noradrenaline reuptake inhibitors, tricyclic antidepressants, opioids, topical lidocaine, etc.) for chronic neuropathic pain patients are still rare and have disappointing efficacy, which makes it difficult to relieve the patients’ painful symptoms, and, at best, they only try to reduce the patients’ ability to tolerate pain. Long non-coding RNAs (lncRNAs), a type of transcript of more than 200 nucleotides with no protein-coding or limited capacity, were identified to be abnormally expressed in the spinal cord, dorsal root ganglion, hippocampus, and prefrontal cortex under chronic neuropathic pain conditions. Moreover, a rapidly growing body of data has clearly pointed out that nearly 40% of lncRNAs exist specifically in the nervous system. Hence, it was speculated that these dysregulated lncRNAs might participate in the occurrence, development, and progression of chronic neuropathic pain. In other words, if we deeply delve into the potential roles of lncRNAs in the pathogenesis of chronic neuropathic pain, this may open up new strategies and directions for the development of novel targeted drugs to cure this refractory disorder. In this article, we primarily review the status of chronic neuropathic pain and provide a general overview of lncRNAs, the detailed roles of lncRNAs in the nervous system and its related diseases, and the abnormal expression of lncRNAs and their potential clinical applications in chronic neuropathic pain. We hope that through the above description, readers can gain a better understanding of the emerging roles of lncRNAs in chronic neuropathic pain.

Long non‑coding RNA NR_002794 is upregulated in pre‑eclampsia and regulates the proliferation, apoptosis and invasion of trophoblast cells

Pre-eclampsia is a common complication during pregnancy, characterized by hypertension and proteinuria. The pathogenesis of pre-eclampsia is not fully understood. Studies on the maternal spiral artery have led scientists to consider that the ineffective infiltration of placental trophoblast cells may be a primary cause of pre-eclampsia. The present study aimed to investigate the differences in the profiles of long non-coding RNAs (lncRNAs) between the placentas of patients with pre-eclampsia and those of healthy pregnant women. The involvement of the differentially expressed lncRNAs in the biological activity of trophoblast cells was also assessed. A total of 26 differentially expressed lncRNAs were identified between the pre-eclampsia and healthy groups. Upregulation of NR_002794 was found in tissues from patients with pre-eclampsia. In SWAN71 trophoblast cells, NR_002794 had suppressive effects on proliferation and migration, and resulted in an increased rate of apoptosis. Furthermore, lncRNA NR_002794 had no effect on the phagocytosis of trophoblast cells. The present study suggested that abnormal levels of NR_002794 may lead to atypical conditions in trophoblast cells, which may be associated with the failure of maternal spiral artery remodeling during pregnancy and, consequently, with the development of pre-eclampsia.

Graph Convolutional Network and Convolutional Neural Network Based Method for Predicting lncRNA-Disease Associations

Aberrant expressions of long non-coding RNAs (lncRNAs) are often associated with diseases and identification of disease-related lncRNAs is helpful for elucidating complex pathogenesis. Recent methods for predicting associations between lncRNAs and diseases integrate their pertinent heterogeneous data. However, they failed to deeply integrate topological information of heterogeneous network comprising lncRNAs, diseases, and miRNAs. We proposed a novel method based on the graph convolutional network and convolutional neural network, referred to as GCNLDA, to infer disease-related lncRNA candidates. The heterogeneous network containing the lncRNA, disease, and miRNA nodes, is constructed firstly. The embedding matrix of a lncRNA-disease node pair was constructed according to various biological premises about lncRNAs, diseases, and miRNAs. A new framework based on a graph convolutional network and a convolutional neural network was developed to learn network and local representations of the lncRNA-disease pair. On the left side of the framework, the autoencoder based on graph convolution deeply integrated topological information within the heterogeneous lncRNA-disease-miRNA network. Moreover, as different node features have discriminative contributions to the association prediction, an attention mechanism at node feature level is constructed. The left side learnt the network representation of the lncRNA-disease pair. The convolutional neural networks on the right side of the framework learnt the local representation of the lncRNA-disease pair by focusing on the similarities, associations, and interactions that are only related to the pair. Compared to several state-of-the-art prediction methods, GCNLDA had superior performance. Case studies on stomach cancer, osteosarcoma, and lung cancer confirmed that GCNLDA effectively discovers the potential lncRNA-disease associations.

A Learning-Based Method for LncRNA-Disease Association Identification Combing Similarity Information and Rotation Forest

Long non-coding RNA (lncRNA) play critical roles in the occurrence and development of various diseases. The determination of the lncRNA-disease associations thus would contribute to provide new insights into the pathogenesis of the disease, the diagnosis, and the gene treatments. Considering that traditional experimental approaches are difficult to detect potential human lncRNA-disease associations from the vast amount of biological data, developing computational method could be of significant value. In this paper, we proposed a novel computational method named LDASR to identify associations between lncRNA and disease by analyzing known lncRNA-disease associations. First, the feature vectors of the lncRNA-disease pairs were obtained by integrating lncRNA Gaussian interaction profile kernel similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity. Second, autoencoder neural network was employed to reduce the feature dimension and get the optimal feature subspace from the original feature set. Finally, Rotating Forest was used to carry out prediction of lncRNA-disease association. The proposed method achieves an excellent preference with 0.9502 AUC in leave-one-out cross-validations (LOOCV) and 0.9428 AUC in 5-fold cross-validation, which significantly outperformed previous methods. Moreover, two kinds of case studies on identifying lncRNAs associated with colorectal cancer and glioma further proves the capability of LDASR in identifying novel lncRNA-disease associations. The promising experimental results show that the LDASR can be an excellent addition to the biomedical research in the future.

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We propose a similarity-based characterization method for RNA-disease associations

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The model automatically captures important association features

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This method determines the prospects of machine learning techniques on such problems

Long noncoding RNAs are involved in multiple immunological pathways in response to vaccination

Understanding the mechanisms of vaccine-elicited protection contributes to the development of new vaccines. The emerging field of systems vaccinology provides detailed information on host responses to vaccination and has been successfully applied to study the molecular mechanisms of several vaccines. Long noncoding RNAs (lncRNAs) are crucially involved in multiple biological processes, but their role in vaccine-induced immunity has not been explored. We performed an analysis of over 2,000 blood transcriptome samples from 17 vaccine cohorts to identify lncRNAs potentially involved with antibody responses to influenza and yellow fever vaccines. We have created an online database where all results from this analysis can be accessed easily. We found that lncRNAs participate in distinct immunological pathways related to vaccine-elicited responses. Among them, we showed that the expression of lncRNA FAM30A was high in B cells and correlates with the expression of immunoglobulin genes located in its genomic vicinity. We also identified altered expression of these lncRNAs in RNA-sequencing (RNA-seq) data from a cohort of children following immunization with intranasal live attenuated influenza vaccine, suggesting a common role across several diverse vaccines. Taken together, these findings provide evidence that lncRNAs have a significant impact on immune responses induced by vaccination.

Down-regulation of taurine-up-regulated gene 1 attenuates inflammation by sponging miR-9-5p via targeting NF-κB1/p50 in multiple sclerosis

AIMS

Multiple sclerosis (MS) is an inflammatory disease of the central nervous system characterized by widespread inflammation. LncRNA taurine-up-regulated gene 1 (TUG1) has been reported to be involved in multiple biological processes and human diseases. The aim of this study was to investigate the role of lncRNA TUG1 in MS and the underlying mechanism.

MAIN METHODS

Experimental autoimmune encephalomyelitis (EAE) was induced in mice by immunization with myelin oligodendrocyte glycoprotein peptide 35-55 (MOG35-55). Lentiviral vectors encoding sh-TUG1 was constructed to silence TUG1 in MOG-EAE mice by intracerebroventricular (ICV) injection. The effect of TUG1 on inflammation in MS was evaluated by real-time PCR, Western blot, ELISA and Hematoxylin-eosin staining. To further study the mechanism of TUG1 in MS, TUG1 knockdown and miR-9-5p overexpression were performed in LPS-induced BV2 cells.

KEY FINDINGS

Down-regulation of TUG1 improved mice behavior, reduced granulocyte-macrophage colony stimulating factor (GM-CSF) level, decreased the levels of pro-inflammatory cytokines including tumor necrosis factor-α (TNF-α), interferon-γ (IFN-γ), interleukin (IL)-6 and IL-17, and increased IL-10 in EAE mice. Notably, TUG1 expression was negatively correlated with miR-9-5p expression, while positively correlated with NF-κB1/p50. Knockdown of TUG1 or enforced expression of miR-9-5p inhibited LPS-induced inflammation in BV2 cells, while these effects were abolished by inhibition of miR-9-5p. We further verified that TUG1 negatively regulated miR-9-5p expression and NF-κB1/p50 is a direct target of miR-9-5p.

SIGNIFICANCE

Down-regulation of TUG1 attenuates MS through inhibition of inflammation by sponging miR-9-5p via targeting NF-κB1/p50, suggesting that TUG1 is a potential therapeutic target for MS treatment.

Long Noncoding RNA Analyses for Osteoporosis Risk in Caucasian Women

INTRODUCTION

Osteoporosis is a prevalent bone metabolic disease characterized by bone fragility. As a key pathophysiological mechanism, the disease is caused by excessive bone resorption (by osteoclasts) over bone formation (by osteoblasts). Peripheral blood monocytes (PBMs) is a major systemic cell model for bone metabolism by serving as progenitors of osteoclasts and producing cytokines important for osteoclastogenesis. Protein-coding genes for osteoporosis have been widely studied by mRNA analyses of PBMs in high versus low hip bone mineral density (BMD) subjects. However, long noncoding RNAs (lncRNAs), which account for a large proportion of human transcriptome, have seldom been studied.

METHODS

In this study, microarray analyses of monocytes were performed using Affymetrix exon 1.0 ST arrays in 73 Caucasian females (age: 47-56). LncRNA profile was generated by re-annotating exon array for lncRNAs detection, which yielded 12,007 lncRNAs mapped to the human genome.

RESULTS

575 lncRNAs were differentially expressed between the two groups. In the high BMD subjects, 309 lncRNAs were upregulated and 266 lncRNAs were downregulated (nominally significant, raw p-value < 0.05). To investigate the relationship between mRNAs and lncRNAs, we used two approaches to predict the target genes of lncRNAs and found that 26 candidate lncRNAs might regulate mRNA expression. The majority of these lncRNAs were further validated to be potentially correlated with BMD by GWAS analysis.

CONCLUSION

Overall, our findings for the first time reported the lncRNAs profiles for osteoporosis and suggested the potential regulatory mechanism of lncRNAs on protein-coding genes in bone metabolism.

Identification of the Potential Key Long Non-coding RNAs in Aged Mice With Postoperative Cognitive Dysfunction

Postoperative cognitive dysfunction (POCD) is a significant complication of surgery, particularly in elderly patients. Emerging researches showed that long non-coding RNA (lncRNA) may play a vital role in the pathogenesis of POCD. Here we aimed to identify potential key lncRNAs involved in the development of POCD. LncRNA and mRNA expression profiles in hippocampal tissues from POCD and control mice were analyzed by microarray assay. Gene ontology (GO) and KEGG pathway enrichment analyses were conducted to probe the functions of dysregulated genes. Then, important factors of the mainly affected biological processes were measured in the hippocampus. Correlated coding–non-coding co-expression (CNC) networks were constructed. Finally, the potential key pairs of lncRNA and target mRNA implicated in POCD were probed. Our data showed that 868 differentially expressed lncRNAs and 690 differentially expressed mRNAs were identified in total. GO and KEGG analyses indicated that the differentially expressed genes were mainly associated with inflammatory and apoptotic signaling pathways. Surgery-induced inflammatory cytokines and apoptosis were significantly increased in hippocampal tissues of aged mice. In CNC network analysis, we found that LncRNA uc009qbj.1 was positively correlated with apoptosis-associated gene Vrk2 level. LncRNA ENSMUST00000174338 correlated positively with expression of the inflammation and apoptosis-associated gene Smad7. LncRNA NONMMUT00000123687 mediated gene expression by binding the inflammation-regulated transcription factor Meis2. Our results suggested that these potential key lncRNAs and mRNAs may play a crucial role in the development of POCD through mediating neuronal inflammation or apoptosis.

Predicting lncRNA-disease associations using network topological similarity based on deep mining heterogeneous networks

A kind of noncoding RNA with length more than 200 nucleotides named long noncoding RNA (lncRNA) has gained considerable attention in recent decades. Many studies have confirmed that human genome contains many thousands of lncRNAs. LncRNAs play significant roles in many important biological processes, including complex disease diagnosis, prognosis, prevention and treatment. For some important diseases such as cancer, lncRNAs have been novel candidate biomarkers. However, the role of lncRNAs in human diseases is still in its infancy, and only a small part of lncRNA-disease associations have been experimentally verified. Predicting lncRNA-disease association is an important way to understand the mechanism and function of lncRNA involved in diseases to enrich the annotations of lncRNA. Therefore, it is urgent to prioritize lncRNAs potentially associated with diseases. Biological system is a highly complex heterogenous network involved different molecules. Therefore, the algorithms based on network methods have been extensively applied in information fields which can provide a quantifiable characterization for the networks characterizing multifarious biological systems. A heterogeneous network topology possessing abundant interactions between biomedical entities is rarely utilized in similarity-based methods for predicting lncRNA-disease associations based on the array of varying features of lncRNAs and diseases. DeepWalk, encoding the relations of nodes in a continuous vector space, is an extension of language model and unsupervised learning from sequence-based word to network. In this article, we present a novel lncRNA-disease association prediction method based on DeepWalk, which enhances the existing association discovery methods through a topology-based similarity measure. We integrate the heterogeneous data to construct a Linked Tripartite Network which is a heterogeneous network containing three types od nodes which generated from bioinformatics linked datasets and use DeepWalk method to extract topological structure features of the nodes in the linked tripartite network for calculating similarities. Our proposed method can be separated into the following steps: Firstly, we integrate heterogeneous data to construct a Linked Tripartite Network: containing the topological interactions of known lncRNA-disease, lncRNA-microRNA and microRNA-disease. Secondly, the topological structure features of the nodes are extracted based on DeepWalk. Thirdly, similarity scores of disease-disease pairs and lncRNA-lncRNA pairs are computed based on the topology of this network. Finally, new lncRNA and disease associations are discovered by rule-based inference method with lncRNA-lncRNA similarities. Our proposed method shows superior predictive performance for prediction of lncRNA-disease associations based on topological similarity from heterogenous network. The AUC value is used to show the performance of our method. The similarity measurement using network topology based on DeepWalk provide a novel perspective which is different from the similarity derived from sequence or structure information. Availability: All the data and codes are freely availability at: https://github.com/Pengeace/lncRNA-disease-link.

LncRNA: Shedding light on mechanisms and opportunities in fibrosis and aging

Fibrosis is universally observed in multiple aging-related diseases and progressions and is characterized by excess accumulation of the extracellular matrix. Fibrosis occurs in various human organs and eventually results in organ failure. Noncoding RNAs (ncRNAs) have emerged as essential regulators of cellular signaling and relevant human diseases. In particular, the enigmatic class of long noncoding RNAs (lncRNAs) is a kind of noncoding RNA that is longer than 200 nucleotides and does not possess protein coding ability. LncRNAs have been identified to exert both promotive and inhibitory effects on the multifaceted processes of fibrosis. A growing body of studies has revealed that lncRNAs are involved in fibrosis in various organs, including the liver, heart, lung, and kidney. As lncRNAs have been increasingly identified, they have become promising targets for anti-fibrosis therapies. This review systematically highlights the recent advances regarding the roles of lncRNAs in fibrosis and sheds light on the use of lncRNAs as a potential treatment for fibrosis.

Role of Long Noncoding RNAs and Circular RNAs in Nerve Regeneration

Nerve injuries may cause severe disability and affect the quality of life. It is of great importance to get a full understanding of the biological processes and molecular mechanisms underlying nerve injuries to find and target specific molecules for nerve regeneration. Numerous studies have shown that noncoding RNAs (ncRNAs) participate in diverse biological processes and diseases. Long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) are two major groups of ncRNAs, which attract growing attention. The altered expression patterns of lncRNAs and circRNAs following nerve injury suggest that these ncRNAs might be associated with nerve regeneration. This review will give a brief introduction of lncRNAs and circRNAs. We then summarize the current studies on lncRNAs and circRNAs following peripheral nerve injury and spinal cord injury (SCI). Typical lncRNAs and circRNAs are introduced to illustrate the diverse molecular mechanisms for nerve regeneration. In addition, we also discuss some issues to be addressed in future investigations on lncRNAs and circRNAs.

Upregulation of the lncRNA MEG3 improves cognitive impairment, alleviates neuronal damage, and inhibits activation of astrocytes in hippocampus tissues in Alzheimer's disease through inactivating the PI3K/Akt signaling pathway

OBJECTIVE

The purpose of this study was to elucidate the expression of the long noncoding RNA (lncRNA) maternally expressed gene 3 (MEG3) in rats with Alzheimer's disease (AD) and to explore its potential mechanisms.

METHODS

An AD rat model was induced by microinjection of Aβ_25-35 . On the first day after successful modeling, pcDNA3.1 plasmid and pcDNA3.1-MEG3 plasmid were continuously infused into the third ventricle through a micro-osmotic pump to interfere with the expression level of MEG3. The spatial learning ability and memory ability, the histopathological changes of hippocampus tissues, the ultrastructure of hippocampal neurons, astrocyte activation as well as the survival and apoptosis of hippocampal neurons in each group was observed. The expression of apoptosis, PI3/Akt signaling pathway-related proteins, glial fibrillary acidic protein, inflammatory factors, malondialdehyde, glutathione-peroxidase, and superoxide dismutase levels were determined. The deposition of amyloid beta (Aβ) in the hippocampus of rats by was observed by Aβ immunohistochemical staining.

RESULTS

Downregulated MEG3 was detected in the tissues of AD rats. In addition, upregulation of MEG3 contributed to an improvement of spatial learning ability and memory ability, inhibited the pathological injury and its apoptosis of hippocampal neurons, decreased Aβ positive expression, inhibited oxidative stress injury and inflammatory injury as well as the activated astrocytes in AD rats via inactivation of the PI3/Akt pathway.

CONCLUSION

Our study highlights that upregulation of the lncRNA MEG3 improves cognitive impairment, alleviates neuronal damage, and inhibits activation of astrocytes in hippocampus tissues in AD through inhibiting the PI3K/Akt signaling pathway.

Long Noncoding RNA in Preeclampsia: Transcriptional Noise or Innovative Indicators?

Preeclampsia (PE) is termed as an obstetric issue that is characterized by hypertension (≧140/90 mm Hg), together with proteinuria following 20 weeks of pregnancy. Until today, PE still constitutes a severe threat to the lives of both the mothers and fetuses. In the past, long noncoding RNAs (lncRNAs) were considered as the transcriptional noise. However, some investigations have indicated that lncRNAs could be used as innovative indicators in PE. The current review aims to discuss the relationship between lncRNAs and PE in recent years. According to the retrieved data, we concluded that lncRNAs can exert an impact on both the occurrence and development of PE through the changes in the biological functions of trophoblasts, immune regulation, epigenetic regulation, decidualization, and energy metabolism. The mechanisms of lncRNAs in PE will help us to better understand the pathogenesis of PE and help us to find targets for predicting and diagnosing PE in the future.

A Novel Approach Based on Point Cut Set to Predict Associations of Diseases and LncRNAs

Background:

In recent years, more evidence have progressively indicated that Long non-coding RNAs (lncRNAs) play vital roles in wide-ranging human diseases, which can serve as potential biomarkers and drug targets. Comparing with vast lncRNAs being found, the relationships between lncRNAs and diseases remain largely unknown.

Objective:

The prediction of novel and potential associations between lncRNAs and diseases would contribute to dissect the complex mechanisms of disease pathogenesis. associations while known disease-lncRNA associations are required only.

Method:

In this paper, a new computational method based on Point Cut Set is proposed to predict LncRNA-Disease Associations (PCSLDA) based on known lncRNA-disease associations. Compared with the existing state-of-the-art methods, the major novelty of PCSLDA lies in the incorporation of distance difference matrix and point cut set to set the distance correlation coefficient of nodes in the lncRNA-disease interaction network. Hence, PCSLDA can be applied to forecast potential lncRNAdisease associations while known disease-lncRNA associations are required only.

Results:

Simulation results show that PCSLDA can significantly outperform previous state-of-the-art methods with reliable AUC of 0.8902 in the leave-one-out cross-validation and AUCs of 0.7634 and 0.8317 in 5-fold cross-validation and 10-fold cross-validation respectively. And additionally, 70% of top 10 predicted cancer-lncRNA associations can be confirmed.

Conclusion:

It is anticipated that our proposed model can be a great addition to the biomedical research field.

A Novel Model for Predicting LncRNA-disease Associations based on the LncRNA-MiRNA-Disease Interactive Network

Background:

Accumulating experimental studies have manifested that long-non-coding RNAs (lncRNAs) play an important part in various biological process. It has been shown that their alterations and dysregulations are closely related to many critical complex diseases.

Objective:

It is of great importance to develop effective computational models for predicting potential lncRNA-disease associations.

Method:

Based on the hypothesis that there would be potential associations between a lncRNA and a disease if both of them have associations with the same group of microRNAs, and similar diseases tend to be in close association with functionally similar lncRNAs. A novel method for calculating similarities of both lncRNAs and diseases is proposed, and then a novel prediction model LDLMD for inferring potential lncRNA-disease associations is proposed.

Results:

LDLMD can achieve an AUC of 0.8925 in the Leave-One-Out Cross Validation (LOOCV), which demonstrated that the newly proposed model LDLMD significantly outperforms previous state-of-the-art methods and could be a great addition to the biomedical research field.

Conclusion:

Here, we present a new method for predicting lncRNA-disease associations, moreover, the method of our present decrease the time and cost of biological experiments.

Expression Pattern of Long Non-coding RNAs in Schizophrenic Patients

The role of long non-coding RNAs (lncRNAs) in the pathogenesis of neurological disorders including schizophrenia has been highlighted by independent studies. In the present study, we compared peripheral blood expression of seven lncRNAs between schizophrenic patients and sex- and age-matched controls using quantitative real-time PCR technique. FAS-AS1, PVT1 and TUG1 were significantly down-regulated in schizophrenic patients compared with healthy individuals (P = 0.007, 0.003 and 0.001, respectively). The association between FAS-AS1 expression and schizophrenia was significant in male subjects aged more than 50 but not in other subgroups. GAS5, NEAT1 and OIP5-AS1 expressions were not significantly different between patients and controls (P = 0.523, 0.739 and 0.267, respectively). The associations between GAS5, NEAT1 and OIP5-AS1 expressions and schizophrenia were significant in female subjects but not in male subjects. THRIL was up-regulated in schizophrenic patients compared with healthy subjects. Based on the results of bootstraped median regression, and after controlling for the effects of age and sex, the difference in its expression between cases and controls was significant (P = 0.014), while the interaction between group and sex was not significant. The expression of lncRNAs was not correlated with age in any study subgroups. In addition, we found sex-based pairwise correlations between PVT1 expression and expression levels of OIP5-AS1, THRIL and NEAT1. We also demonstrated high sensitivity and specificity of GAS5 for diagnosis of schizophrenia in female patients. The current study provides further evidence for the participation of lncRNAs in the pathogenesis of schizophrenia. Future studies are needed to confirm the suitability of lncRNAs as peripheral biomarkers for this psychiatric disorder.

The Implications of the Long Non-Coding RNA NEAT1 in Non-Cancerous Diseases

Long non-coding RNAs (lncRNAs) are involved in a variety of biological and cellular processes as well as in physiologic and pathophysiologic events. This review summarizes recent literature about the role of the lncRNA nuclear enriched abundant transcript 1 (NEAT1) in non-cancerous diseases with a special focus on viral infections and neurodegenerative diseases. In contrast to its role as competing endogenous RNA (ceRNA) in carcinogenesis, NEAT1's function in non-cancerous diseases predominantly focuses on paraspeckle-mediated effects on gene expression. This involves processes such as nuclear retention of mRNAs or sequestration of paraspeckle proteins from specific promoters, resulting in transcriptional induction or repression of genes involved in regulating the immune system or neurodegenerative processes. NEAT1 expression is aberrantly-mostly upregulated-in non-cancerous pathological conditions, indicating that it could serve as potential prognostic biomarker. Additional studies are needed to elucidate NEAT1's capability to be a therapeutic target for non-cancerous diseases.

Long Non-Coding RNAs in Multiple Myeloma

Multiple myeloma (MM) is the second most common hematooncological disease of malignant plasma cells in the bone marrow. While new treatment brought unprecedented increase of survival of patients, MM pathogenesis is yet to be clarified. Increasing evidence of expression of long non-coding RNA molecules (lncRNA) linked to development and progression of many tumors suggested their important role in tumorigenesis. To date, over 15,000 lncRNA molecules characterized by diversity of function and specificity of cell distribution were identified in the human genome. Due to their involvement in proliferation, apoptosis, metabolism, and differentiation, they have a key role in the biological processes and pathogenesis of many diseases, including MM. This review summarizes current knowledge of non-coding RNAs (ncRNA), especially lncRNAs, and their role in MM pathogenesis. Undeniable involvement of lncRNAs in MM development suggests their potential as biomarkers.

Long Non-coding RNA in Neuronal Development and Neurological Disorders

Long non-coding RNAs (lncRNAs) are transcripts which are usually more than 200 nt in length, and which do not have the protein-coding capacity. LncRNAs can be categorized based on their generation from distinct DNA elements, or derived from specific RNA processing pathways. During the past several decades, dramatic progress has been made in understanding the regulatory functions of lncRNAs in diverse biological processes, including RNA processing and editing, cell fate determination, dosage compensation, genomic imprinting and development etc. Dysregulation of lncRNAs is involved in multiple human diseases, especially neurological disorders. In this review, we summarize the recent progress made with regards to the function of lncRNAs and associated molecular mechanisms, focusing on neuronal development and neurological disorders.

Mechanisms of Antisense Transcription Initiation with Implications in Gene Expression, Genomic Integrity and Disease Pathogenesis

Non-coding antisense transcripts arise from the strand opposite the sense strand. Over 70% of the human genome generates non-coding antisense transcripts while less than 2% of the genome codes for proteins. Antisense transcripts and/or the act of antisense transcription regulate gene expression and genome integrity by interfering with sense transcription and modulating histone modifications or DNA methylation. Hence, they have significant pathological and physiological relevance. Indeed, antisense transcripts were found to be associated with various diseases including cancer, diabetes, cardiac and neurodegenerative disorders, and, thus, have promising potentials for prognostic and diagnostic markers and therapeutic development. However, it is not clearly understood how antisense transcription is initiated and epigenetically regulated. Such knowledge would provide new insights into the regulation of antisense transcription, and hence disease pathogenesis with therapeutic development. The recent studies on antisense transcription initiation and its epigenetic regulation, which are limited, are discussed here. Furthermore, we concisely describe how antisense transcription/transcripts regulate gene expression and genome integrity with implications in disease pathogenesis and therapeutic development.