AMPFLDAP: Adaptive Message Passing and Feature Fusion on Heterogeneous Network for LncRNA-Disease Associations Prediction

Exploration of the intricate connections between long noncoding RNA (lncRNA) and diseases, referred to as lncRNA-disease associations (LDAs), plays a pivotal and indispensable role in unraveling the underlying molecular mechanisms of diseases and devising practical treatment approaches. It is imperative to employ computational methods for predicting lncRNA-disease associations to circumvent the need for superfluous experimental endeavors. Graph-based learning models have gained substantial popularity in predicting these associations, primarily because of their capacity to leverage node attributes and relationships within the network. Nevertheless, there remains much room for enhancing the performance of these techniques by incorporating and harmonizing the node attributes more effectively. In this context, we introduce a novel model, i.e., Adaptive Message Passing and Feature Fusion (AMPFLDAP), for forecasting lncRNA-disease associations within a heterogeneous network. Firstly, we constructed a heterogeneous network involving lncRNA, microRNA (miRNA), and diseases based on established associations and employing Gaussian interaction profile kernel similarity as a measure. Then, an adaptive topological message passing mechanism is suggested to address the information aggregation for heterogeneous networks. The topological features of nodes in the heterogeneous network were extracted based on the adaptive topological message passing mechanism. Moreover, an attention mechanism is applied to integrate both topological and semantic information to achieve the multimodal features of biomolecules, which are further used to predict potential LDAs. The experimental results demonstrated that the performance of the proposed AMPFLDAP is superior to seven state-of-the-art methods. Furthermore, to validate its efficacy in practical scenarios, we conducted detailed case studies involving three distinct diseases, which conclusively demonstrated AMPFLDAP's effectiveness in the prediction of LDAs.

Epigenetic modifications in Parkinson's disease: A critical review

Parkinson's Disease (PD) is a progressive neurodegenerative disorder expected to increase by over 50% by 2030 due to increasing life expectancy. The disease's hallmarks include slow movement, tremors, and postural instability. Impaired protein processing is a major factor in the pathophysiology of PD, leading to the buildup of aberrant protein aggregates, particularly misfolded α-synuclein, also known as Lewy bodies. These Lewy bodies lead to inflammation and further death of dopaminergic neurons, leading to imbalances in excitatory and inhibitory neurotransmitters, causing excessive uncontrollable movements called dyskinesias. It was previously suggested that a complex interplay involving hereditary and environmental variables causes the specific death of neurons in PD; however, the exact mechanism of the association involving the two primary modifiers is yet unknown. An increasing amount of research points to the involvement of epigenetics in the onset and course of several neurological conditions, such as PD. DNA methylation, post-modifications of histones, and non-coding RNAs are the primary examples of epigenetic alterations, that is defined as alterations to the expression of genes and functioning without modifications in DNA sequence. Epigenetic modifications play a significant role in the development of PD, with genes such as Parkin, PTEN-induced kinase 1 (PINK1), DJ1, Leucine-Rich Repeat Kinase 2 (LRRK2), and alpha-synuclein associated with the disease. The aberrant epigenetic changes implicated in the pathophysiology of PD and their impact on the design of novel therapeutic approaches are the primary focus of this review.

LncRNA-protein interaction prediction with reweighted feature selection

LncRNA-protein interactions are ubiquitous in organisms and play a crucial role in a variety of biological processes and complex diseases. Many computational methods have been reported for lncRNA-protein interaction prediction. However, the experimental techniques to detect lncRNA-protein interactions are laborious and time-consuming. Therefore, to address this challenge, this paper proposes a reweighting boosting feature selection (RBFS) method model to select key features. Specially, a reweighted apporach can adjust the contribution of each observational samples to learning model fitting; let higher weights are given more influence samples than those with lower weights. Feature selection with boosting can efficiently rank to iterate over important features to obtain the optimal feature subset. Besides, in the experiments, the RBFS method is applied to the prediction of lncRNA-protein interactions. The experimental results demonstrate that our method achieves higher accuracy and less redundancy with fewer features.

A glimpse of research cores and frontiers on the relationship between long noncoding RNAs (lncRNAs) and colorectal cancer (CRC) using the VOSviewer tool

As lncRNAs are essential participants in colorectal carcinogenesis. This study aimed to use the VOSviewer tool to access the research cores and frontiers on the relationship between lncRNAs and CRC. Our findings showed that the mechanism of lncRNA in the occurrence and development of CRC was the core theme of the field. (1) Immunotherapy and immune microenvironment of CRC and lncRNAs, (2) CRC and lncRNAs in exosomes and (3) CRC and lncRNA-targeted therapy might represent three research frontiers. A comprehensive understanding of their existing mechanisms and the search for new regulatory paradigms are the core topics of future research. This knowledge will also help us select appropriate targeting methods and select appropriate preclinical models to promote clinical translation and ultimately achieve precise treatment of CRC.

Comprehensive landscape and future perspectives of long noncoding RNAs (lncRNAs) in colorectal cancer (CRC): Based on a bibliometric analysis

This review aimed to use bibliometric analysis to sort out, analyze and summarize the knowledge foundation and hot topics in the field of long noncoding RNAs (lncRNAs) in colorectal cancer (CRC), and point out future trends to inspire related research and innovation. We used CiteSpace to analyze publication outputs, countries, institutions, authors, journals, references, and keywords. Knowledge foundations, hotspots, and future trends were then depicted. The overall research showed the trend of biomedical-oriented multidisciplinary. Much evidence indicates that lncRNA plays the role of oncogene or tumor suppressor in the occurrence and development of CRC. Besides, many lncRNAs have multiple mechanisms. lncRNAs and metastasis of CRC, lncRNAs and drug resistance of CRC, and the clinical application of lncRNAs in CRC are current research hotspots. Through insight into the development trend of lncRNAs in CRC, this study will help researchers extract hidden valuable information for further research.

Regulatory Non-Coding RNAs in Familial Hypercholesterolemia, Theranostic Applications

Familial hypercholesterolemia (FH) is a common monogenic disease which is associated with high serum levels of low-density lipoprotein cholesterol (LDL-C) and leads to atherosclerosis and cardiovascular disease (CVD). Early diagnosis and effective treatment strategy can significantly improve prognosis. Recently, non-coding RNAs (ncRNAs) have emerged as novel biomarkers for the diagnosis and innovative targets for therapeutics. Non-coding RNAs have essential roles in the regulation of LDL-C homeostasis, suggesting that manipulation and regulating ncRNAs could be a promising theranostic approach to ameliorate clinical complications of FH, particularly cardiovascular disease. In this review, we briefly discussed the mechanisms and pathophysiology of FH and novel therapeutic strategies for the treatment of FH. Moreover, the theranostic effects of different non-coding RNAs for the treatment and diagnosis of FH were highlighted. Finally, the advantages and disadvantages of ncRNA-based therapies vs. conventional therapies were discussed.

Long Non-coding RNAs: Mechanisms, Experimental, and Computational Approaches in Identification, Characterization, and Their Biomarker Potential in Cancer

Long non-coding RNAs are diverse class of non-coding RNA molecules >200 base pairs of length having various functions like gene regulation, dosage compensation, epigenetic regulation. Dysregulation and genomic variations of several lncRNAs have been implicated in several diseases. Their tissue and developmental specific expression are contributing factors for them to be viable indicators of physiological states of the cells. Here we present an comprehensive review the molecular mechanisms and functions, state of the art experimental and computational pipelines and challenges involved in the identification and functional annotation of lncRNAs and their prospects as biomarkers. We also illustrate the application of co-expression networks on the TCGA-LIHC dataset for putative functional predictions of lncRNAs having a therapeutic potential in Hepatocellular carcinoma (HCC).

Full-Length Transcriptome Sequencing Provides Insights into Flavonoid Biosynthesis in Fritillaria hupehensis

One of the most commonly utilized medicinal plants in China is Fritillaria hupehensis (Hsiao et K.C. Hsia). However, due to a lack of genomic resources, little is known about the biosynthesis of relevant compounds, particularly the flavonoid biosynthesis pathway. A PacBio RS II sequencing generated a total of 342,044 reads from the bulb, leaf, root, and stem, of which 316,438 were full-length (FL) non-redundant reads with an average length of 1365 bp and a N50 of 1888 bp. There were also 38,607 long non-coding RNAs and 7914 simple sequence repeats detected. To improve our understanding of processes implicated in regulating secondary metabolite biosynthesis in F. hupehensis tissues, we evaluated potential metabolic pathways. Overall, this study provides a repertoire of FL transcripts in F. hupehensis for the first time, and it will be a valuable resource for marker-assisted breeding and research into bioactive compounds for medicinal and pharmacological applications.

A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations

Background

Numerous studies have demonstrated that long non-coding RNAs are related to plenty of human diseases. Therefore, it is crucial to predict potential lncRNA-disease associations for disease prognosis, diagnosis and therapy. Dozens of machine learning and deep learning algorithms have been adopted to this problem, yet it is still challenging to learn efficient low-dimensional representations from high-dimensional features of lncRNAs and diseases to predict unknown lncRNA-disease associations accurately.

Results

We proposed an end-to-end model, VGAELDA, which integrates variational inference and graph autoencoders for lncRNA-disease associations prediction. VGAELDA contains two kinds of graph autoencoders. Variational graph autoencoders (VGAE) infer representations from features of lncRNAs and diseases respectively, while graph autoencoders propagate labels via known lncRNA-disease associations. These two kinds of autoencoders are trained alternately by adopting variational expectation maximization algorithm. The integration of both the VGAE for graph representation learning, and the alternate training via variational inference, strengthens the capability of VGAELDA to capture efficient low-dimensional representations from high-dimensional features, and hence promotes the robustness and preciseness for predicting unknown lncRNA-disease associations. Further analysis illuminates that the designed co-training framework of lncRNA and disease for VGAELDA solves a geometric matrix completion problem for capturing efficient low-dimensional representations via a deep learning approach.

Conclusion

Cross validations and numerical experiments illustrate that VGAELDA outperforms the current state-of-the-art methods in lncRNA-disease association prediction. Case studies indicate that VGAELDA is capable of detecting potential lncRNA-disease associations. The source code and data are available at https://github.com/zhanglabNKU/VGAELDA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04073-z.

Novel lncRNAs Co-Expression Networks Identifies LINC00504 with Oncogenic Role in Luminal A Breast Cancer Cells

Long non-coding RNAs (lncRNAs) are functional transcripts with more than 200 nucleotides. These molecules exhibit great regulatory capacity and may act at different levels of gene expression regulation. Despite this regulatory versatility, the biology of these molecules is still poorly understood. Computational approaches are being increasingly used to elucidate biological mechanisms in which these lncRNAs may be involved. Co-expression networks can serve as great allies in elucidating the possible regulatory contexts in which these molecules are involved. Herein, we propose the use of the pipeline deposited in the RTN package to build lncRNAs co-expression networks using TCGA breast cancer (BC) cohort data. Worldwide, BC is the most common cancer in women and has great molecular heterogeneity. We identified an enriched co-expression network for the validation of relevant cell processes in the context of BC, including LINC00504. This lncRNA has increased expression in luminal subtype A samples, and is associated with prognosis in basal-like subtype. Silencing this lncRNA in luminal A cell lines resulted in decreased cell viability and colony formation. These results highlight the relevance of the proposed method for the identification of lncRNAs in specific biological contexts.

Bioinformatics Approaches for Functional Prediction of Long Noncoding RNAs

There is accumulating evidence that long noncoding RNAs (lncRNAs) play crucial roles in biological processes and diseases. In recent years, computational models have been widely used to predict potential lncRNA-disease relations. In this chapter, we systematically describe various computational algorithms and prediction tools that have been developed to elucidate the roles of lncRNAs in diseases, coding potential/functional characterization, or ascertaining their involvement in critical biological processes as well as provide a comprehensive summary of these applications.

Genome-Wide Computational Analysis and Validation of Potential Long Noncoding RNA-Mediated DNA-DNA-RNA Triplexes in the Human Genome

Long noncoding RNAs are well studied for their regulatory actions through interaction with DNA regulating biological roles of DNA, RNA, or protein. However, direct binding of lncRNA with DNA is rarely demonstrated in experiments. The present protocol explains genome wide computational strategies to choose lncRNAs that can bind directly to the chromatin by forming highly stable DNA-DNA-RNA triplexes. The chapter also focuses on biophysical methods that can be used to validate the computationally derived lncRNA-gene targets in vitro.

Prediction of a competing endogenous RNA co-expression network as a prognostic marker in glioblastoma

Due to its high proliferation capacity and rapid intracranial spread, glioblastoma (GBM) has become one of the least curable malignant cancers. Recently, the competing endogenous RNAs (ceRNAs) hypothesis has become a focus in the researches of molecular biological mechanisms of cancer occurrence and progression. However, there is a lack of correlation studies on GBM, as well as a lack of comprehensive analyses of GBM molecular mechanisms based on high‐throughput sequencing and large‐scale sample sizes. We obtained RNA‐seq data from The Cancer Genome Atlas (TCGA) and Genotype‐Tissue Expression (GTEx) databases. Further, differentially expressed mRNAs were identified from normal brain tissue and GBM tissue. The similarities between the mRNA modules with clinical traits were subjected to weighted correlation network analysis (WGCNA). With the mRNAs from clinical‐related modules, a survival model was constructed by univariate and multivariate Cox proportional hazard regression analyses. Thereafter, we carried out Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Finally, we predicted interactions between lncRNAs, miRNAs and mRNAs by TargetScan, miRDB, miRTarBase and starBase. We identified 2 lncRNAs (NORAD, XIST), 5 miRNAs (hsa‐miR‐3613, hsa‐miR‐371, hsa‐miR‐373, hsa‐miR‐32, hsa‐miR‐92) and 2 mRNAs (LYZ, PIK3AP1) for the construction of a ceRNA network, which might act as a prognostic biomarker of GBM. Combined with previous studies and our enrichment analysis results, we hypothesized that this ceRNA network affects immune activities and tumour microenvironment variations. Our research provides novel aspects to study GBM development and treatment.

Non-Coding RNA Databases in Cardiovascular Research

Cardiovascular diseases (CVDs) are of multifactorial origin and can be attributed to several genetic and environmental components. CVDs are the leading cause of mortality worldwide and they primarily damage the heart and the vascular system. Non-coding RNA (ncRNA) refers to functional RNA molecules, which have been transcribed into DNA but do not further get translated into proteins. Recent transcriptomic studies have identified the presence of thousands of ncRNA molecules across species. In humans, less than 2% of the total genome represents the protein-coding genes. While the role of many ncRNAs is yet to be ascertained, some long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) have been associated with disease progression, serving as useful diagnostic and prognostic biomarkers. A plethora of data repositories specialized in ncRNAs have been developed over the years using publicly available high-throughput data from next-generation sequencing and other approaches, that cover various facets of ncRNA research like basic and functional annotation, expressional profile, structural and molecular changes, and interaction with other biomolecules. Here, we provide a compendium of the current ncRNA databases relevant to cardiovascular research.

LUAD transcriptomic profile analysis of d-limonene and potential lncRNA chemopreventive target

d-Limonene, a type of natural extract obtained from citrus oils, was reported to have anti-cancer effects and be well-tolerated by cancer patients. Despite arousing interest as a cancer chemopreventive substance, the transcriptomic profile of d-limonene in humans is poorly understood. Based on the results of the transcriptomic profiling, a lncRNA named protein disulfide isomerase family A member three pseudogene (PDIA3P1) was found to be regulated by d-limonene. PDIA3P1 is an oncogene verified by three lung adenocarcinoma (LUAD) datasets. The knockdown of PDIA3P1 with siRNA decreased the viability, invasion, migration, and proliferation of LUAD cells. Based on The Cancer Genome Atlas (TCGA) LUAD datasets, PDIA3P1 regulates functions and pathways mainly including lipid metabolism, immunity, and the change of the chromosome structure. This study comprehensively performs the transcriptomic analysis of the d-limonene regulation on LUAD, and reveals that PDIA3P1 may be the mediator in helping d-limonene to prevent and suppress LUAD via lipid metabolism, immunity pathway, and the change in the chromosome structure.

LncRNA IGBP1-AS1/miR-24-1/ZIC3 loop regulates the proliferation and invasion ability in breast cancer

Background

Breast cancer (BC) is one of the malignant solid tumors with the highest morbidity in the world. Currently, the therapeutic outcome of different types of treatment can be unsatisfactory. Novel lncRNA biomarkers in BC remains to be further explored.

Methods

Different expression of lncRNAs among BC tissues and adjacent normal tissues were identified with microarray analyses. A series of in vivo and in vitro gain-of-function laboratory procedures were conducted to study the biological functions of IGBP1-AS1. The prognostic effects on IGBP1-AS1 survival were evaluated by using in situ hybridization and survival analysis. In addition, other experiments including RNA pull down analysis, RNA immunoprecipitation, luciferase reporter assays, and chromatin immunoprecipitation as well as validating assays conducted in vivo were applied to identify the target and regulatory mechanisms of IGBP1-AS1.

Results

Significant down-regulation of IGBP1-AS1 was discovered in the cell lines and tissues of BC. With respect to its biological function, overexpression of IGBP1-AS1 had inhibitory effects on the invasion and proliferation of BC cells in vivo as well as in vitro. Analysis of the samples obtained from BC patients indicated a positive effect of IGBP1-AS1 on survival outcomes. LncRNA IGBP1-AS1/miR-24-1/ZIC3 axis as a loop can regulate the proliferation and invasion of BC cells.

Conclusions

IGBP1-AS1 could have inhibitory impact on the invasion and proliferation of BC and may serve as a promising biomarker for BC.

YY1-induced upregulation of FOXP4-AS1 and FOXP4 promote the proliferation of esophageal squamous cell carcinoma cells

Esophageal squamous cell carcinoma (ESCC) belongs to one of the most common malignant tumors worldwide and possesses high mortality. Long non-coding RNAs (lncRNAs) have been demonstrated to be essential biological participants in the progression of ESCC. On the basis of bio-informatics prediction, forkhead box P4 antisense RNA 1 (FOXP4-AS1) and forkhead box P4 (FOXP4) were upregulated in esophageal carcinoma samples and were positively correlated with each other. The present study aimed to explore the function of FOXP4-AS1 and FOXP4 in ESCC cells. Function assays disclosed that knockdown of FOXP4-AS1 or FOXP4 efficiently suppressed cell proliferation and induced cell apoptosis. Moreover, FOXP4-AS1 positively regulated FOXP4 by interacting with insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) to stabilize FOXP4 messenger RNA. In addition, FOXP4-AS1 could upregulate the expression of FOXP4 by sponging miR-3184-5p. Finally, we found that Yin Yang 1 (YY1) is a transcription factor that can transcriptionally activate both FOXP4-AS1 and FOXP4 in ESCC cells. In a word, YY1-induced upregulation of FOXP4-AS1 and FOXP4 promote the proliferation of ESCC cells.

Predicting functional long non-coding RNAs validated by low throughput experiments

High-throughput techniques have uncovered hundreds and thousands of long non-coding RNAs (lncRNAs). Among them, only a tiny fraction has experimentally validated functions (EVlncRNAs) by low-throughput methods. What fraction of lncRNAs from high-throughput experiments (HTlncRNAs) is truly functional is an active subject of debate. Here, we developed the first method to distinguish EVlncRNAs from HTlncRNAs and mRNAs by using Support Vector Machines and found that EVlncRNAs can be well separated from HTlncRNAs and mRNAs with 0.6 for Matthews correlation coefficient, 64% for sensitivity, and 81% for precision for the independent human test set. The most useful features for classification are related to sequence conservations at RNA (for separating from HTlncRNAs) and protein (for separating from mRNA) levels. The method is found to be robust as the human-RNA-trained model is applicable to independent mouse RNAs with similar accuracy and to a lesser extent to plant RNAs. The method can recover newly discovered EVlncRNAs with high sensitivity. Its application to randomly selected 2000 human HTlncRNAs indicates that the majority of HTlncRNAs is probably non-functional but a large portion (nearly 30%) are likely functional. In other words, there is an ample number of lncRNAs whose specific biological roles are yet to be discovered. The method developed here is expected to speed up and reduce the cost of the discovery by prioritizing potentially functional lncRNAs prior to experimental validation. EVlncRNA-pred is available as a web server at http://biophy.dzu.edu.cn/lncrnapred/index.html . All datasets used in this study can be obtained from the same website.

Long non-coding RNA XIST expression as a prognostic factor in human cancers: A meta-analysis

A large number of literature has shown that high expression of X inactive-specific transcript (XIST) is associated with poor prognosis and metastasis of cancer in patients. However, most of this literature is limited by the small sample sizes and discrete outcomes. Therefore, a meta-analysis was performed to investigate the relation between XIST expression and tumor node metastasis (TNM) stage, lymph node metastasis, distant metastasis, and overall survival of cancer patients. We searched for literature in PubMed, Embase, and Web of Science. The pooled hazard ratios (HRs) or odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to evaluate the association of XIST expression with prognosis and clinicopathological characteristics of cancer patients. Finally, a total of 14 articles involving 1123 patients were included in this meta-analysis. The results suggested that high expression of XIST has a significant relationship with a relatively poor overall survival for patients with malignant tumors (HR 1.82; 95% CI 1.32, 2.52; P = 0.0003). Moreover, high expression of XIST was significantly associated with poor TNM stage (OR 3.64; 95% CI 2.62, 5.07; P < 0.0001), lymph node metastasis (OR 2.39; 95% CI 1.65, 3.46; P < 0.0001) and distant metastasis (OR 2.84; 95% CI 1.90, 4.23; P < 0.0001). In conclusion, high expression of lncRNA XIST may be a predictive factor of poor prognosis in human cancers.

EVLncRNAs: a manually curated database for long non-coding RNAs validated by low-throughput experiments

Long non-coding RNAs (lncRNAs) play important functional roles in various biological processes. Early databases were utilized to deposit all lncRNA candidates produced by high-throughput experimental and/or computational techniques to facilitate classification, assessment and validation. As more lncRNAs are validated by low-throughput experiments, several databases were established for experimentally validated lncRNAs. However, these databases are small in scale (with a few hundreds of lncRNAs only) and specific in their focuses (plants, diseases or interactions). Thus, it is highly desirable to have a comprehensive dataset for experimentally validated lncRNAs as a central repository for all of their structures, functions and phenotypes. Here, we established EVLncRNAs by curating lncRNAs validated by low-throughput experiments (up to 1 May 2016) and integrating specific databases (lncRNAdb, LncRANDisease, Lnc2Cancer and PLNIncRBase) with additional functional and disease-specific information not covered previously. The current version of EVLncRNAs contains 1543 lncRNAs from 77 species that is 2.9 times larger than the current largest database for experimentally validated lncRNAs. Seventy-four percent lncRNA entries are partially or completely new, comparing to all existing experimentally validated databases. The established database allows users to browse, search and download as well as to submit experimentally validated lncRNAs. The database is available at http://biophy.dzu.edu.cn/EVLncRNAs.

Modeling Long ncRNA-Mediated Regulation in the Mammalian Cell Cycle

Long noncoding RNAs (lncRNAs) are transcripts longer than 200 nucleotides that are not translated into proteins. They have recently gained widespread attention due to the finding that tens of thousands of lncRNAs reside in the human genome, and due to an increasing number of lncRNAs that are found to be associated with disease. Some lncRNAs, including disease-associated ones, play different roles in regulating the cell cycle. Mathematical models of the cell cycle have been useful in better understanding this biological system, such as how it could be robust to some perturbations and how the cell cycle checkpoints could act as a switch. Here, we discuss mathematical modeling techniques for studying lncRNA regulation of the mammalian cell cycle. We present examples on how modeling via network analysis and differential equations can provide novel predictions toward understanding cell cycle regulation in response to perturbations such as DNA damage.

Identifying lncRNA-mediated regulatory modules via ChIA-PET network analysis

Background

Although several studies have provided insights into the role of long non-coding RNAs (lncRNAs), the majority of them have unknown function. Recent evidence has shown the importance of both lncRNAs and chromatin interactions in transcriptional regulation. Although network-based methods, mainly exploiting gene-lncRNA co-expression, have been applied to characterize lncRNA of unknown function by means of ’guilt-by-association’, no strategy exists so far which identifies mRNA-lncRNA functional modules based on the 3D chromatin interaction graph.

Results

To better understand the function of chromatin interactions in the context of lncRNA-mediated gene regulation, we have developed a multi-step graph analysis approach to examine the RNA polymerase II ChIA-PET chromatin interaction network in the K562 human cell line. We have annotated the network with gene and lncRNA coordinates, and chromatin states from the ENCODE project. We used centrality measures, as well as an adaptation of our previously developed Markov State Models (MSM) clustering method, to gain a better understanding of lncRNAs in transcriptional regulation. The novelty of our approach resides in the detection of fuzzy regulatory modules based on network properties and their optimization based on co-expression analysis between genes and gene-lncRNA pairs. This results in our method returning more bona fide regulatory modules than other state-of-the art approaches for clustering on graphs.

Conclusions

Interestingly, we find that lncRNA network hubs tend to be significantly enriched in evolutionary conserved lncRNAs and enhancer-like functions. We validated regulatory functions for well known lncRNAs, such as MALAT1 and the enhancer-like lncRNA FALEC. In addition, by investigating the modular structure of bigger components we mine putative regulatory functions for uncharacterized lncRNAs.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2900-8) contains supplementary material, which is available to authorized users.

Long noncoding RNA: a crosslink in biological regulatory network

Long noncoding RNAs (lncRNAs) had been defined as a novel class of functional RNAs longer than 200 nucleotides around a decade ago. It is widely acknowledged that lncRNAs play a significant role in regulation of gene expression, but the biological and molecular mechanisms are diverse and complex, and remain to be determined. Especially, the regulatory network of lncRNAs associated with other biological molecules is still a controversial matter, thus becoming a new frontier of the studies on transcriptome. Recent advance in high-throughput sequencing technologies and bioinformatics approaches may be an accelerator to lift the mysterious veil. In this review, we will outline well-known associations between lncRNAs and other biological molecules, demonstrate the diverse bioinformatics approaches applied in prediction and analysis of lncRNA interaction and perform a case study for lncRNA linc00460 to concretely decipher the lncRNA regulatory network.

Exploring lncRNA-Mediated Regulatory Networks in Endometrial Cancer Cells and the Tumor Microenvironment: Advances and Challenges

Recent studies have revealed both the promise and challenges of targeting long non-coding RNAs (lncRNAs) to diagnose and treat endometrial cancer (EC). LncRNAs are upregulated or downregulated in ECs compared to normal tissues and their dysregulation has been linked to tumor grade, FIGO stage, the depth of myometrial invasion, lymph node metastasis and patient survival. Tumor suppressive lncRNAs (GAS5, MEG3, FER1L4 and LINC00672) and oncogenic lncRNAs (CCAT2, BANCR, NEAT1, MALAT1, H19 and Linc-RoR) have been identified as upstream modulators or downstream effectors of major signaling pathways influencing EC metastasis, including the PTEN/PI3K/AKT/mTOR, RAS/RAF/MEK/ERK, WNT/β-catenin and p53 signaling pathways. TUG1 and TDRG1 stimulate the VEGF-A pathway. PCGEM1 is implicated in activating the JAK/STAT3 pathway. Here, we present an overview of the expression pattern, prognostic value, biological function of lncRNAs in EC cells and their roles within the tumor microenvironment, focusing on the influence of lncRNAs on established EC-relevant pathways. We also describe the emerging classification of EC subtypes based on their lncRNA signature and discuss the clinical implications of lncRNAs as valuable biomarkers for EC diagnosis and potential targets for EC treatment.

Comparison of RNA-Seq and Microarray Gene Expression Platforms for the Toxicogenomic Evaluation of Liver From Short-Term Rat Toxicity Studies

Gene expression profiling is a useful tool to predict and interrogate mechanisms of toxicity. RNA-Seq technology has emerged as an attractive alternative to traditional microarray platforms for conducting transcriptional profiling. The objective of this work was to compare both transcriptomic platforms to determine whether RNA-Seq offered significant advantages over microarrays for toxicogenomic studies. RNA samples from the livers of rats treated for 5 days with five tool hepatotoxicants (α-naphthylisothiocyanate/ANIT, carbon tetrachloride/CCl₄, methylenedianiline/MDA, acetaminophen/APAP, and diclofenac/DCLF) were analyzed with both gene expression platforms (RNA-Seq and microarray). Data were compared to determine any potential added scientific (i.e., better biological or toxicological insight) value offered by RNA-Seq compared to microarrays. RNA-Seq identified more differentially expressed protein-coding genes and provided a wider quantitative range of expression level changes when compared to microarrays. Both platforms identified a larger number of differentially expressed genes (DEGs) in livers of rats treated with ANIT, MDA, and CCl₄ compared to APAP and DCLF, in agreement with the severity of histopathological findings. Approximately 78% of DEGs identified with microarrays overlapped with RNA-Seq data, with a Spearman’s correlation of 0.7 to 0.83. Consistent with the mechanisms of toxicity of ANIT, APAP, MDA and CCl₄, both platforms identified dysregulation of liver relevant pathways such as Nrf2, cholesterol biosynthesis, eiF2, hepatic cholestasis, glutathione and LPS/IL-1 mediated RXR inhibition. RNA-Seq data showed additional DEGs that not only significantly enriched these pathways, but also suggested modulation of additional liver relevant pathways. In addition, RNA-Seq enabled the identification of non-coding DEGs that offer a potential for improved mechanistic clarity. Overall, these results indicate that RNA-Seq is an acceptable alternative platform to microarrays for rat toxicogenomic studies with several advantages. Because of its wider dynamic range as well as its ability to identify a larger number of DEGs, RNA-Seq may generate more insight into mechanisms of toxicity. However, more extensive reference data will be necessary to fully leverage these additional RNA-Seq data, especially for non-coding sequences.

Multivariate Information Fusion With Fast Kernel Learning to Kernel Ridge Regression in Predicting LncRNA-Protein Interactions

Long non-coding RNAs (lncRNAs) constitute a large class of transcribed RNA molecules. They have a characteristic length of more than 200 nucleotides which do not encode proteins. They play an important role in regulating gene expression by interacting with the homologous RNA-binding proteins. Due to the laborious and time-consuming nature of wet experimental methods, more researchers should pay great attention to computational approaches for the prediction of lncRNA-protein interaction (LPI). An in-depth literature review in the state-of-the-art in silico investigations, leads to the conclusion that there is still room for improving the accuracy and velocity. This paper propose a novel method for identifying LPI by employing Kernel Ridge Regression, based on Fast Kernel Learning (LPI-FKLKRR). This approach, uses four distinct similarity measures for lncRNA and protein space, respectively. It is remarkable, that we extract Gene Ontology (GO) with proteins, in order to improve the quality of information in protein space. The process of heterogeneous kernels integration, applies Fast Kernel Learning (FastKL) to deal with weight optimization. The extrapolation model is obtained by gaining the ultimate prediction associations, after using Kernel Ridge Regression (KRR). Experimental outcomes show that the ability of modeling with LPI-FKLKRR has extraordinary performance compared with LPI prediction schemes. On benchmark dataset, it has been observed that the best Area Under Precision Recall Curve (AUPR) of 0.6950 is obtained by our proposed model LPI-FKLKRR, which outperforms the integrated LPLNP (AUPR: 0.4584), RWR (AUPR: 0.2827), CF (AUPR: 0.2357), LPIHN (AUPR: 0.2299), and LPBNI (AUPR: 0.3302). Also, combined with the experimental results of a case study on a novel dataset, it is anticipated that LPI-FKLKRR will be a useful tool for LPI prediction.

LDAP: a web server for lncRNA-disease association prediction

Motivation

Increasing evidences have demonstrated that long noncoding RNAs (lncRNAs) play important roles in many human diseases. Therefore, predicting novel lncRNA-disease associations would contribute to dissect the complex mechanisms of disease pathogenesis. Some computational methods have been developed to infer lncRNA-disease associations. However, most of these methods infer lncRNA-disease associations only based on single data resource.

Results

In this paper, we propose a new computational method to predict lncRNA-disease associations by integrating multiple biological data resources. Then, we implement this method as a web server for lncRNA-disease association prediction (LDAP). The input of the LDAP server is the lncRNA sequence. The LDAP predicts potential lncRNA-disease associations by using a bagging SVM classifier based on lncRNA similarity and disease similarity.

Availability and Implementation

The web server is available at http://bioinformatics.csu.edu.cn/ldap

Contact

jxwang@mail.csu.edu.cn.

Supplimentary Information

Supplementary data are available at Bioinformatics online.

Molecular mechanisms of long noncoding RNAs and their role in disease pathogenesis

LncRNAs are long non-coding regulatory RNAs that are longer than 200 nucleotides. One of the major functions of lncRNAs is the regulation of specific gene expression at multiple steps including, recruitment and expression of basal transcription machinery, post-transcriptional modifications and epigenetics [1]. Emerging evidence suggests that lncRNAs also play a critical role in maintaining tissue homeostasis during physiological and pathological conditions, lipid homeostasis, as well as epithelial and smooth muscle cell homeostasis, a topic that has been elegantly reviewed [2-5]. While aberrant expression of lncRNAs has been implicated in several disease conditions, there is paucity of information about their contribution to the etiology of diseases [6]. Several studies have compared the expression of lncRNAs under normal and cancerous conditions and found differential expression of several lncRNAs, suggesting thereby an involvement of lncRNAs in disease processes [7, 8]. Furthermore, the ability of lncRNAs to influence epigenetic changes also underlies their role in disease pathogenesis since epigenetic regulation is known to play a critical role in many human diseases [1]. LncRNAs thus are not only involved in homeostatic functioning but also play a vital role in the progression of many diseases, thereby underscoring their potential as novel therapeutic targets for the alleviation of a variety of human disease conditions.

Distinct and Modular Organization of Protein Interacting Sites in Long Non-coding RNAs

Background: Long non-coding RNAs (lncRNAs), are being reported to be extensively involved in diverse regulatory roles and have exhibited numerous disease associations. LncRNAs modulate their function through interaction with other biomolecules in the cell including DNA, RNA, and proteins. The availability of genome-scale experimental datasets of RNA binding proteins (RBP) motivated us to understand the role of lncRNAs in terms of its interactions with these proteins. In the current report, we demonstrate a comprehensive study of interactions between RBP and lncRNAs at a transcriptome scale through extensive analysis of the crosslinking and immunoprecipitation (CLIP) experimental datasets available for 70 RNA binding proteins.

Results: Our analysis suggests that density of interaction sites for these proteins was significantly higher for specific sub-classes of lncRNAs when compared to protein-coding transcripts. We also observe a positional preference of these RBPs across lncRNA and protein coding transcripts in addition to a significant co-occurrence of RBPs having similar functions, suggesting a modular organization of these elements across lncRNAs.

Conclusion: The significant enrichment of RBP sites across some lncRNA classes is suggestive that these interactions might be important in understanding the functional role of lncRNA. We observed a significant enrichment of RBPs which are involved in functional roles such as silencing, splicing, mRNA processing, and transport, indicating the potential participation of lncRNAs in such processes.

Long non-coding RNAs and complex diseases: from experimental results to computational models

LncRNAs have attracted lots of attentions from researchers worldwide in recent decades. With the rapid advances in both experimental technology and computational prediction algorithm, thousands of lncRNA have been identified in eukaryotic organisms ranging from nematodes to humans in the past few years. More and more research evidences have indicated that lncRNAs are involved in almost the whole life cycle of cells through different mechanisms and play important roles in many critical biological processes. Therefore, it is not surprising that the mutations and dysregulations of lncRNAs would contribute to the development of various human complex diseases. In this review, we first made a brief introduction about the functions of lncRNAs, five important lncRNA-related diseases, five critical disease-related lncRNAs and some important publicly available lncRNA-related databases about sequence, expression, function, etc. Nowadays, only a limited number of lncRNAs have been experimentally reported to be related to human diseases. Therefore, analyzing available lncRNA–disease associations and predicting potential human lncRNA–disease associations have become important tasks of bioinformatics, which would benefit human complex diseases mechanism understanding at lncRNA level, disease biomarker detection and disease diagnosis, treatment, prognosis and prevention. Furthermore, we introduced some state-of-the-art computational models, which could be effectively used to identify disease-related lncRNAs on a large scale and select the most promising disease-related lncRNAs for experimental validation. We also analyzed the limitations of these models and discussed the future directions of developing computational models for lncRNA research.

The long non-coding RNA Neat1 is an important mediator of the therapeutic effect of bexarotene on traumatic brain injury in mice

OBJECTIVE

Bexarotene treatments exert neuroprotective effects on mice following traumatic brain injury (TBI). The present study aims to investigate the potential roles of the long noncoding RNA Neat1 in the neuroprotective effects of bexarotene.

MATERIALS AND METHODS

Adult male C57BL/6J mice (n=80) and newborn mice (within 24h after birth) (n=20) were used to generate a "controlled cortical impact" (CCI) model and harvest primary cortex neurons, respectively. The HT22 cell line and the BV2 cell line were cultured under "normal" or "oxygen/glucose-deprived" (OGD) conditions. The relationship between RXR-α and the Neat1 promoter was clarified using ChIP-qPCR and dual-luciferase reporter gene assays. The mRNA alterations induced by Neat1 knockdown were measured using next-generation RNA sequencing. Proteins were captured by Neat1, pulled down and subjected to mass spectrometry. The neurological severity score, rotarod test and water maze test were employed to measure the animals' motor and cognitive functions.

RESULTS

Bexarotene prominently up-regulated the Neat1 level in an RXR-α-dependent manner. Neat1 knockdown induced significant changes in mRNA expression, and the altered mRNAs were involved in many biological processes, including synapse formation and axon guidance. In primary neurons, Neat1 knockdown inhibited and Neat1 over-expression prompted axon elongation. Multiple proteins, including Pidd1, were captured by Neat1. Neat1 inhibited cell apoptosis and restricted inflammation by capturing Pidd1. The in vitro anti-apoptotic and anti-inflammatory effects of Neat1 were further confirmed in C57BL/6 mice, which resulted in better motor and cognitive function after TBI.

CONCLUSION

Bexarotene up-regulates the lncRNA Neat1, which inhibits apoptosis and inflammation, thereby resulting in better functional recovery in mice after TBI.

Genome-wide computational analysis of potential long noncoding RNA mediated DNA:DNA:RNA triplexes in the human genome

BACKGROUND

Only a handful of long noncoding RNAs have been functionally characterized. They are known to modulate regulation through interacting with other biomolecules in the cell: DNA, RNA and protein. Though there have been detailed investigations on lncRNA-miRNA and lncRNA-protein interactions, the interaction of lncRNAs with DNA have not been studied extensively. In the present study, we explore whether lncRNAs could modulate genomic regulation by interacting with DNA through the formation of highly stable DNA:DNA:RNA triplexes.

METHODS

We computationally screened 23,898 lncRNA transcripts as annotated by GENCODE, across the human genome for potential triplex forming sequence stretches (PTS). The PTS frequencies were compared across 5'UTR, CDS, 3'UTR, introns, promoter and 1000 bases downstream of the transcription termination sites. These regions were annotated by mapping to experimental regulatory regions, classes of repeat regions and transcription factors. We validated few putative triplex mediated interactions where lncRNA-gene pair interaction is via pyrimidine triplex motif using biophysical methods.

RESULTS

We identified 20,04,034 PTS sites to be enriched in promoter and intronic regions across human genome. Additional analysis of the association of PTS with core promoter elements revealed a systematic paucity of PTS in all regulatory regions, except TF binding sites. A total of 25 transcription factors were found to be associated with PTS. Using an interaction network, we showed that a subset of the triplex forming lncRNAs, have a positive association with gene promoters. We also demonstrated an in vitro interaction of one lncRNA candidate with its predicted gene target promoter regions.

CONCLUSIONS

Our analysis shows that PTS are enriched in gene promoter and largely associated with simple repeats. The current study suggests a major role of a subset of lncRNAs in mediating chromatin organization modulation through CTCF and NSRF proteins.

BRWLDA: bi-random walks for predicting lncRNA-disease associations

Increasing efforts have been done to figure out the association between lncRNAs and complex diseases. Many computational models construct various lncRNA similarity networks, disease similarity networks, along with known lncRNA-disease associations to infer novel associations. However, most of them neglect the structural difference between lncRNAs network and diseases network, hierarchical relationships between diseases and pattern of newly discovered associations. In this study, we developed a model that performs Bi-Random Walks to predict novel LncRNA-Disease Associations (BRWLDA in short). This model utilizes multiple heterogeneous data to construct the lncRNA functional similarity network, and Disease Ontology to construct a disease network. It then constructs a directed bi-relational network based on these two networks and available lncRNAs-disease associations. Next, it applies bi-random walks on the network to predict potential associations. BRWLDA achieves reliable and better performance than other comparing methods not only on experiment verified associations, but also on the simulated experiments with masked associations. Case studies further demonstrate the feasibility of BRWLDA in identifying new lncRNA-disease associations.

Identifying novel fruit-related genes in Arabidopsis thaliana based on the random walk with restart algorithm

Fruit is essential for plant reproduction and is responsible for protection and dispersal of seeds. The development and maturation of fruit is tightly regulated by numerous genetic factors that respond to environmental and internal stimulation. In this study, we attempted to identify novel fruit-related genes in a model organism, Arabidopsis thaliana, using a computational method. Based on validated fruit-related genes, the random walk with restart (RWR) algorithm was applied on a protein-protein interaction (PPI) network using these genes as seeds. The identified genes with high probabilities were filtered by the permutation test and linkage tests. In the permutation test, the genes that were selected due to the structure of the PPI network were discarded. In the linkage tests, the importance of each candidate gene was measured from two aspects: (1) its functional associations with validated genes and (2) its similarity with validated genes on gene ontology (GO) terms and KEGG pathways. Finally, 255 inferred genes were obtained, subsequent extensive analysis of important genes revealed that they mainly contribute to ubiquitination (UBQ9, UBQ8, UBQ11, UBQ10), serine hydroxymethyl transfer (SHM7, SHM5, SHM6) or glycol-metabolism (HXKL2_ARATH, CSY5, GAPCP1), suggesting essential roles during the development and maturation of fruit in Arabidopsis thaliana.

Present Scenario of Long Non-Coding RNAs in Plants

Small non-coding RNAs have been extensively studied in plants over the last decade. In contrast, genome-wide identification of plant long non-coding RNAs (lncRNAs) has recently gained momentum. LncRNAs are now being recognized as important players in gene regulation, and their potent regulatory roles are being studied comprehensively in eukaryotes. LncRNAs were first reported in humans in 1992. Since then, research in animals, particularly in humans, has rapidly progressed, and a vast amount of data has been generated, collected, and organized using computational approaches. Additionally, numerous studies have been conducted to understand the roles of these long RNA species in several diseases. However, the status of lncRNA investigation in plants lags behind that in animals (especially humans). Efforts are being made in this direction using computational tools and high-throughput sequencing technologies, such as the lncRNA microarray technique, RNA-sequencing (RNA-seq), RNA capture sequencing, (RNA CaptureSeq), etc. Given the current scenario, significant amounts of data have been produced regarding plant lncRNAs, and this amount is likely to increase in the subsequent years. In this review we have documented brief information about lncRNAs and their status of research in plants, along with the plant-specific resources/databases for information retrieval on lncRNAs.

AnnoLnc: a web server for systematically annotating novel human lncRNAs

BACKGROUND

Long noncoding RNAs (lncRNAs) have been shown to play essential roles in almost every important biological process through multiple mechanisms. Although the repertoire of human lncRNAs has rapidly expanded, their biological function and regulation remain largely elusive, calling for a systematic and integrative annotation tool.

RESULTS

Here we present AnnoLnc ( http://annolnc.cbi.pku.edu.cn ), a one-stop portal for systematically annotating novel human lncRNAs. Based on more than 700 data sources and various tool chains, AnnoLnc enables a systematic annotation covering genomic location, secondary structure, expression patterns, transcriptional regulation, miRNA interaction, protein interaction, genetic association and evolution. An intuitive web interface is available for interactive analysis through both desktops and mobile devices, and programmers can further integrate AnnoLnc into their pipeline through standard JSON-based Web Service APIs.

CONCLUSIONS

To the best of our knowledge, AnnoLnc is the only web server to provide on-the-fly and systematic annotation for newly identified human lncRNAs. Compared with similar tools, the annotation generated by AnnoLnc covers a much wider spectrum with intuitive visualization. Case studies demonstrate the power of AnnoLnc in not only rediscovering known functions of human lncRNAs but also inspiring novel hypotheses.

Navigating the dynamic landscape of long noncoding RNA and protein-coding gene annotations in GENCODE

Background

Our understanding of the transcriptional potential of the genome and its functional consequences has undergone a significant change in the last decade. This has been largely contributed by the improvements in technology which could annotate and in many cases functionally characterize a number of novel gene loci in the human genome. Keeping pace with advancements in this dynamic environment and being able to systematically annotate a compendium of genes and transcripts is indeed a formidable task. Of the many databases which attempted to systematically annotate the genome, GENCODE has emerged as one of the largest and popular compendium for human genome annotations.

Results

The analysis of various versions of GENCODE revealed that there was a constant upgradation of transcripts for both protein-coding and long noncoding RNA (lncRNAs) leading to conflicting annotations. The GENCODE version 24 accounts for 4.18 % of the human genome to be transcribed which is an increase of 1.58 % from its first version. Out of 2,51,614 transcripts annotated across GENCODE versions, only 21.7 % had consistency. We also examined GENCODE consortia categorized transcripts into 70 biotypes out of which only 17 remained stable throughout.

Conclusions

In this report, we try to review the impact on the dynamicity with respect to gene annotations, specifically (lncRNA) annotations in GENCODE over the years. Our analysis suggests a significant dynamism in gene annotations, reflective of the evolution and consensus in nomenclature of genes. While a progressive change in annotations and timely release of the updates make the resource reliable in the community, the dynamicity with each release poses unique challenges to its users. Taking cues from other experiments with bio-curation, we propose potential avenues and methods to mend the gap.

Electronic supplementary material

The online version of this article (doi:10.1186/s40246-016-0090-2) contains supplementary material, which is available to authorized users.

Long non-coding RNA Databases in Cardiovascular Research

With the rising interest in the regulatory functions of long non-coding RNAs (lncRNAs) in complex human diseases such as cardiovascular diseases, there is an increasing need in public databases offering comprehensive and integrative data for all aspects of these versatile molecules. Recently, a variety of public data repositories that specialized in lncRNAs have been developed, which make use of huge high-throughput data particularly from next-generation sequencing (NGS) approaches. Here, we provide an overview of current lncRNA databases covering basic and functional annotation, lncRNA expression and regulation, interactions with other biomolecules, and genomic variants influencing the structure and function of lncRNAs. The prominent lncRNA antisense noncoding RNA in the INK4 locus (ANRIL), which has been unequivocally associated with coronary artery disease through genome-wide association studies (GWAS), serves as an example to demonstrate the features of each individual database.

Genome-wide identification of long noncoding RNA genes and their potential association with fecundity and virulence in rice brown planthopper, Nilaparvata lugens

Background

The functional repertoire of long noncoding RNA (lncRNA) has been characterized in several model organisms, demonstrating that lncRNA plays important roles in fundamental biological processes. However, they remain largely unidentified in most species. Understanding the characteristics and functions of lncRNA in insects would be useful for insect resources utilization and sustainable pest control.

Methods

A computational pipeline was developed to identify lncRNA genes in the rice brown planthopper, Nilaparvata lugens, a destructive rice pest causing huge yield losses. Strand specific RT-PCR were used to determine the transcription orientation of lncRNAs.

Results

In total, 2,439 lncRNA transcripts corresponding to 1,882 loci were detected from 12 whole transcriptomes (RNA-seq) datasets, including samples from high fecundity (HFP), low fecundity (LFP), I87i and C89i populations, in addition Mudgo and TN1 virulence strains. The identified N. lugens lncRNAs had low sequence similarities with other known lncRNAs. However, their structural features were similar with mammalian counterparts. N. lugens lncRNAs had shorter transcripts than protein-coding genes due to the lower exon number though their exons and introns were longer. Only 19.9% of N. lugens lncRNAs had multiple alternatively spliced isoforms. We observed biases in the genome location of N. lugens lncRNAs. More than 30% of the lncRNAs overlapped with known protein-coding genes. These lncRNAs tend to be co-expressed with their neighboring genes (Pearson correlation, p < 0.01, T-test) and might interact with adjacent protein-coding genes. In total, 19-148 lncRNAs were specifically-expressed in the samples of HFP, LFP, Mudgo, TN1, I87i and C89i populations. Three lncRNAs specifically expressed in HFP and LFP populations overlapped with reproductive-associated genes.

Discussion

The structural features of N. lugens lncRNAs are similar to mammalian counterparts. Coexpression and function analysis suggeste that N. lugens lncRNAs might have important functions in high fecundity and virulence adaptability.

Conclusions

This study provided the first catalog of lncRNA genes in rice brown planthopper. Gene expression and genome location analysis indicated that lncRNAs might play important roles in high fecundity and virulence adaptation in N. lugens.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1953-y) contains supplementary material, which is available to authorized users.

Long noncoding RNAs could be potential key players in the pathophysiology of Sjögren's syndrome

Long noncoding RNAs (lncRNAs) are a recently discovered class of noncoding functional RNAs encoded by metazoan genomes. Recent studies suggest a larger regulatory role for lncRNAs in critical biological and disease processes. Mounting evidence on the role of lncRNAs in regulating key processes of the immune system prompted us to hypothesize the role of lncRNAs as key regulators of the pathophysiology of Sjögren's syndrome (SS). We used two similar approaches based on reanalysis of microarray expression datasets and curation of lncRNA-protein coding gene interactions from literature to derive support for our hypothesis. We also discuss potential caveats to our approach and suggest approaches to validate the hypothesis. Our analysis suggests the potential larger and hitherto unknown role of lncRNA regulatory networks in modulating the expression of key genes involved in the pathogenesis of SS and thereby modulating the pathophysiology of SS.