NONCODEv4: Annotation of Noncoding RNAs with Emphasis on Long Noncoding RNAs

Abnormal expression of PRKAG2-AS results in dysfunction of cardiomyocytes through regulating PRKAG2 transcription by interacting with PPARG

The role of PRKAG2 in the maintenance of heart function is well established, but little is known about how PRKAG2 is regulated in cardiomyocytes. In this study, we investigated the role of the lncRNA PRKAG2-AS, which is present at the PRKAG2 promoter, in the regulation of PRKAG2 expression. PRKAG2-AS expression was predominantly nuclear, as determined by RNA nucleoplasmic separation and fluorescence in situ hybridization. Knockdown of PRKAG2-AS in the nucleus, but not the cytoplasm, significantly decreased the expression of PRKAG2b and PRKAG2d. Interestingly, we found that PRKAG2-AS and its target genes, PRKAG2b and PRKAG2d, were reduced in the hearts of patients with ischemic cardiomyopathy, suggesting a potential role for PRKAG2-AS in myocardial ischemia. Indeed, knockdown of PRKAG2-AS in the nucleus resulted in apoptosis of cardiomyocytes. We further elucidated the mechanism by which PRKAG2-AS regulates PRKAG2 transcription by identifying 58 PRKAG2-AS interacting proteins. Among them, PPARG was selected for further investigation based on its correlation and potential interaction with PRKAG2-AS in regulating transcription. Overexpression of PPARG, or its activation with rosiglitazone, led to a significant increase in the expression of PRKAG2b and PRKAG2d in cardiomyocytes, which could be attenuated by PRKAG2-AS knockdown. This finding suggests that PRKAG2-AS mediates, at least partially, the protective effects of rosiglitazone on hypoxia-induced apoptosis. However, given the risk of rosiglitazone in heart failure, we also examined the involvement of PRKAG2-AS in this condition and found that PRKAG2-AS, as well as PRKAG2b and PRKAG2d, was elevated in hearts with dilated cardiomyopathy (DCM) and that overexpression of PRKAG2-AS led to a significant increase in PRKAG2b and PRKAG2d expression, indicating that up-regulation of PRKAG2-AS may contribute to the mechanism of heart failure by promoting transcription of PRKAG2. Consequently, proper expression of PRKAG2-AS is essential for maintaining cardiomyocyte function, and aberrant PRKAG2-AS expression induced by hypoxia or other stimuli may cause cardiac dysfunction.

Long Non-Coding RNAs As Epigenetic Regulators in Cancer

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

A MAFG-lncRNA axis links systemic nutrient abundance to hepatic glucose metabolism

Obesity and type 2 diabetes mellitus are global emergencies and long noncoding RNAs (lncRNAs) are regulatory transcripts with elusive functions in metabolism. Here we show that a high fraction of lncRNAs, but not protein-coding mRNAs, are repressed during diet-induced obesity (DIO) and refeeding, whilst nutrient deprivation induced lncRNAs in mouse liver. Similarly, lncRNAs are lost in diabetic humans. LncRNA promoter analyses, global cistrome and gain-of-function analyses confirm that increased MAFG signaling during DIO curbs lncRNA expression. Silencing Mafg in mouse hepatocytes and obese mice elicits a fasting-like gene expression profile, improves glucose metabolism, de-represses lncRNAs and impairs mammalian target of rapamycin (mTOR) activation. We find that obesity-repressed LincIRS2 is controlled by MAFG and observe that genetic and RNAi-mediated LincIRS2 loss causes elevated blood glucose, insulin resistance and aberrant glucose output in lean mice. Taken together, we identify a MAFG-lncRNA axis controlling hepatic glucose metabolism in health and metabolic disease.

Despite widespread transcription of LncRNA in mammalian systems, their contribution to metabolic homeostasis at the cellular and tissue level remains elusive. Here Pradas-Juni et al. describe a transcription factor–LncRNA pathway that couples hepatocyte nutrient sensing to regulation of glucose metabolism in mice.

Coding and Non-Coding RNA Abnormalities in Bipolar Disorder

The molecular mechanisms underlying bipolar disorder (BPD) have remained largely unknown. Postmortem brain tissue studies comparing BPD patients with healthy controls have produced a heterogeneous array of potentially implicated protein-coding RNAs. We hypothesized that dysregulation of not only coding, but multiple classes of RNA (coding RNA, long non-coding (lnc) RNA, circular (circ) RNA, and/or alternative splicing) underlie the pathogenesis of BPD. Using non-polyadenylated libraries we performed RNA sequencing in postmortem human medial frontal gyrus tissue from BPD patients and healthy controls. Twenty genes, some of which not previously implicated in BPD, were differentially expressed (DE). PCR validation and replication confirmed the implication of these DE genes. Functional in silico analyses identified enrichment of angiogenesis, vascular system development and histone H3-K4 demethylation. In addition, ten lncRNA transcripts were differentially expressed. Furthermore, an overall increased number of alternative splicing events in BPD was detected, as well as an increase in the number of genes carrying alternative splicing events. Finally, a large reservoir of circRNAs populating brain tissue not affected by BPD is described, while in BPD altered levels of two circular transcripts, cNEBL and cEPHA3, are reported. cEPHA3, hitherto unlinked to BPD, is implicated in developmental processes in the central nervous system. Although we did not perform replication analyses of non-coding RNA findings, our findings hint that RNA dysregulation in BPD is not limited to coding regions, opening avenues for future pharmacological investigations and biomarker research.

Recent Advances on the Semi-Supervised Learning for Long Non-Coding RNA-Protein Interactions Prediction: A Review

In recent years, more and more evidence indicates that long non-coding RNA (lncRNA) plays a significant role in the development of complex biological processes, especially in RNA progressing, chromatin modification, and cell differentiation, as well as many other processes. Surprisingly, lncRNA has an inseparable relationship with human diseases such as cancer. Therefore, only by knowing more about the function of lncRNA can we better solve the problems of human diseases. However, lncRNAs need to bind to proteins to perform their biomedical functions. So we can reveal the lncRNA function by studying the relationship between lncRNA and protein. But due to the limitations of traditional experiments, researchers often use computational prediction models to predict lncRNA protein interactions. In this review, we summarize several computational models of the lncRNA protein interactions prediction base on semi-supervised learning during the past two years, and introduce their advantages and shortcomings briefly. Finally, the future research directions of lncRNA protein interaction prediction are pointed out.

A path-based computational model for long non-coding RNA-protein interaction prediction

Recently, lncRNAs have attracted accumulating attentions because more and more experimental researches have shown lncRNA can play critical roles in many biological processes. Predicting potential interactions between lncRNAs and proteins are key to understand the lncRNAs biological functions. But traditional biological experiments are expensive and time-consuming, network similarity methods provide a powerful solution to computationally predict lncRNA-protein interactions. In this work, a novel path-based lncRNA-protein interaction (PBLPI) prediction model is proposed by integrating protein semantic similarity, lncRNA functional similarity, known human lncRNA-protein interactions, and Gaussian interaction profile kernel similarity. PBLPI model utilizes three interlinked sub-graphs to construct a heterogeneous graph, and then infers potential lncRNA-protein interactions through depth-first search algorithm. Consequently, PBLPI achieves reliable performance in the frameworks of 5-fold cross validation (average AUC is 0.9244 and AUPR is 0.6478). In the case study, we use "Mus musculus" data to further validate the reliability of PBLPI method. It is anticipated that PBLPI would become a useful tool to identify potential lncRNA-protein interactions.

Identification, Prediction and Data Analysis of Noncoding RNAs: A Review

BACKGROUND

Noncoding RNAs (ncRNAs) which play an important role in various cellular processes are important in medicine as well as in drug design strategies. Different studies have shown that ncRNAs are dis-regulated in cancer cells and play an important role in human tumorigenesis. Therefore, it is important to identify and predict such molecules by experimental and computational methods, respectively. However, to avoid expensive experimental methods, computational algorithms have been developed for accurately and fast prediction of ncRNAs.

OBJECTIVE

The aim of this review was to introduce the experimental and computational methods to identify and predict ncRNAs structure. Also, we explained the ncRNA's roles in cellular processes and drugs design, briefly.

METHOD

In this survey, we will introduce ncRNAs and their roles in biological and medicinal processes. Then, some important laboratory techniques will be studied to identify ncRNAs. Finally, the state-of-the-art models and algorithms will be introduced along with important tools and databases.

RESULTS

The results showed that the integration of experimental and computational approaches improves to identify ncRNAs. Moreover, the high accurate databases, algorithms and tools were compared to predict the ncRNAs.

CONCLUSION

ncRNAs prediction is an exciting research field, but there are different difficulties. It requires accurate and reliable algorithms and tools. Also, it should be mentioned that computational costs of such algorithm including running time and usage memory are very important. Finally, some suggestions were presented to improve computational methods of ncRNAs gene and structural prediction.

Network-Based Methods and Other Approaches for Predicting lncRNA Functions and Disease Associations

The discovery that a considerable portion of eukaryotic genomes is transcribed and gives rise to long noncoding RNAs (lncRNAs) provides an important new perspective on the transcriptome and raises questions about the centrality of these lncRNAs in gene-regulatory processes and diseases. The rapidly increasing number of mechanistically investigated lncRNAs has provided evidence for distinct functional classes, such as enhancer-like lncRNAs, which modulate gene expression via chromatin looping, and noncoding competing endogenous RNAs (ceRNAs), which act as microRNA decoys. Despite great progress in the last years, the majority of lncRNAs are functionally uncharacterized and their implication for disease biogenesis and progression is unknown. Here, we summarize recent developments in lncRNA function prediction in general and lncRNA-disease associations in particular, with emphasis on in silico methods based on network analysis and on ceRNA function prediction. We believe that such computational techniques provide a valuable aid to prioritize functional lncRNAs or disease-relevant lncRNAs for targeted, experimental follow-up studies.

Rapid Generation of Long Noncoding RNA Knockout Mice Using CRISPR/Cas9 Technology

In recent years, long noncoding RNAs (lncRNAs) have emerged as multifaceted regulators of gene expression, controlling key developmental and disease pathogenesis processes. However, due to the paucity of lncRNA loss-of-function mouse models, key questions regarding the involvement of lncRNAs in organism homeostasis and (patho)-physiology remain difficult to address experimentally in vivo. The clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 platform provides a powerful genome-editing tool and has been successfully applied across model organisms to facilitate targeted genetic mutations, including Caenorhabditis elegans, Drosophila melanogaster, Danio rerio and Mus musculus. However, just a few lncRNA-deficient mouse lines have been created using CRISPR/Cas9-mediated genome engineering, presumably due to the need for lncRNA-specific gene targeting strategies considering the absence of open-reading frames in these loci. Here, we describe a step-wise procedure for the generation and validation of lncRNA loss-of-function mouse models using CRISPR/Cas9-mediated genome engineering. In a proof-of-principle approach, we generated mice deficient for the liver-enriched lncRNA Gm15441, which we found downregulated during development of metabolic disease and induced during the feeding/fasting transition. Further, we discuss guidelines for the selection of lncRNA targets and provide protocols for in vitro single guide RNA (sgRNA) validation, assessment of in vivo gene-targeting efficiency and knockout confirmation. The procedure from target selection to validation of lncRNA knockout mouse lines can be completed in 18–20 weeks, of which <10 days hands-on working time is required.

The Bipartite Network Projection-Recommended Algorithm for Predicting Long Non-coding RNA-Protein Interactions

With the development of science and biotechnology, many evidences show that ncRNAs play an important role in the development of important biological processes, especially in chromatin modification, cell differentiation and proliferation, RNA progressing, human diseases, etc. Moreover, lncRNAs account for the majority of ncRNAs, and the functions of lncRNAs are expressed by the related RNA-binding proteins. It is well known that the experimental verification of lncRNA-protein relationships is a waste of time and expensive. So many time-saving and inexpensive computational methods are proposed to uncover potential lncRNA-protein interactions. In this work, we propose a novel computational method to predict the potential lncRNA-protein interactions with the bipartite network projection recommended algorithm (LPI-BNPRA). Our approach is a semi-supervised method based on the lncRNA similarity matrix, protein similarity matrix, and lncRNA-protein interaction matrix. Compared with three previous methods under the leave-one-out cross-validation, our model has a more high-confidence result with the AUC value of 0.8754 and the AUPR value of 0.6283. We also do case studies by the Mus musculus dataset to further reflect the reliability of our approach. This suggests that LPI-BNPRA will be a reliable computational method to uncover lncRNA-protein interactions in biomedical research.

Short and Long Noncoding RNAs Regulate the Epigenetic Status of Cells

SIGNIFICANCE

The concepts of junk DNA and transcriptional noise are long gone as the existence of noncoding RNAs (ncRNAs) has been tested extensively in recent years. Given that the epigenetic status of cells affects many biological processes, how ncRNAs mechanistically contribute to these processes is of great interest. Recent Advances: Recent studies show that various ncRNAs interact with epigenetic and/or transcription factors to modulate the epigenetic status of cells directly and/or indirectly. There exists growing interest in the field of cardiovascular research to understand the roles of ncRNAs. Due to the large number of ncRNAs in the mammalian genome, only a handful of ncRNAs have been functionally elucidated, which makes it difficult to understand how ncRNAs interact with protein-coding genes and their encoded proteins.

CRITICAL ISSUES

Although the canonical function of microRNAs (miRNAs) to inhibit the translation of protein-coding genes is well established, the number of functionally annotated long noncoding RNAs (lncRNAs) is still small, which is especially true in the heart.

FUTURE DIRECTIONS

Future studies must connect the epigenetic controls of various cellular phenomena by incorporating both miRNAs and lncRNAs. Antioxid. Redox Signal. 29, 832-845.

IRWNRLPI: Integrating Random Walk and Neighborhood Regularized Logistic Matrix Factorization for lncRNA-Protein Interaction Prediction

Long non-coding RNA (lncRNA) plays an important role in many important biological processes and has attracted widespread attention. Although the precise functions and mechanisms for most lncRNAs are still unknown, we are certain that lncRNAs usually perform their functions by interacting with the corresponding RNA- binding proteins. For example, lncRNA-protein interactions play an important role in post transcriptional gene regulation, such as splicing, translation, signaling, and advances in complex diseases. However, experimental verification of lncRNA-protein interactions prediction is time-consuming and laborious. In this work, we propose a computational method, named IRWNRLPI, to find the potential associations between lncRNAs and proteins. IRWNRLPI integrates two algorithms, random walk and neighborhood regularized logistic matrix factorization, which can optimize a lot more than using an algorithm alone. Moreover, the method is semi-supervised and does not require negative samples. Based on the leave-one-out cross validation, we obtain the AUC of 0.9150 and the AUPR of 0.7138, demonstrating its reliable performance. In addition, by means of case study in the “Mus musculus,” many lncRNA-protein interactions which are predicted by our method can be successfully confirmed by experiments. This suggests that IRWNRLPI will be a useful bioinformatics resource in biomedical research.

The emergence of long non-coding RNAs in hepatocellular carcinoma: an update

Hepatocellular carcinoma (HCC) accounting for roughly 90% of all primary liver neoplasms is the sixth most frequent neoplasm and the second prominent reason of tumor fatality worldwide. As regulators of diverse biological processes, long non-coding RNAs (lncRNAs) are involved in onset and development of neoplasms. With the continuous booming of well-featured lncRNAs in HCC from 2016 to now, we reviewed the newly-presented comprehension about the relationship between lncRNAs and HCC in this study. To be specific, we summarized the overview function and study tools of lncRNAs, elaborated the roles of lncRNAs in HCC, and sketched the molecule mechanisms of lncRNAs in HCC. In addition, the application of lncRNAs serving as biomarkers in early diagnosis and outcome prediction of HCC patients was highlighted.

LPI-ETSLP: lncRNA-protein interaction prediction using eigenvalue transformation-based semi-supervised link prediction

RNA-protein interactions are essential for understanding many important cellular processes. In particular, lncRNA-protein interactions play important roles in post-transcriptional gene regulation, such as splicing, translation, signaling and even the progression of complex diseases. However, the experimental validation of lncRNA-protein interactions remains time-consuming and expensive, and only a few theoretical approaches are available for predicting potential lncRNA-protein associations. Here, we presented eigenvalue transformation-based semi-supervised link prediction (LPI-ETSLP) to uncover the relationship between lncRNAs and proteins. Moreover, it is semi-supervised and does not need negative samples. Based on 5-fold cross validation, an AUC of 0.8876 and an AUPR of 0.6438 have demonstrated its reliable performance compared with three other computational models. Furthermore, the case study demonstrated that many lncRNA-protein interactions predicted by our method can be successfully confirmed by experiments. It is indicated that LPI-ETSLP would be a useful bioinformatics resource for biomedical research studies.

A heart-enriched antisense long non-coding RNA regulates the balance between cardiac and skeletal muscle triadin

Non-coding RNAs play major roles in cardiac pathophysiology. Recent studies reported that long non-coding RNAs (lncRNAs) are dysregulated in the failing heart, but how they contribute to heart failure development is unclear. In this study, we aimed to identify heart-enriched lncRNAs and investigate their regulation and function in the failing heart.

RESULTS

Analysis of a RNA-seq dataset of 15 Caucasian tissues allowed the identification of 415 heart-enriched lncRNAs. Fifty-three lncRNAs were located on the genome in close vicinity to protein-coding genes associated with cardiac function and disease. Analysis of a second RNA-seq dataset of 16 failing human hearts highlighted one lncRNA which we arbitrarily named TRDN-AS due to its localisation in the antisense position of the gene encoding triadin (TRDN). Expression of TRDN-AS and cardiac TRDN was up-regulated in biopsies from failing human hearts compared to control hearts. In failing hearts, TRDN-AS was positively correlated with a cardiac isoform of TRDN and negatively correlated with a skeletal muscle isoform of TRDN. A murine homolog of human TRDN-AS was identified and found to be enriched in the heart and localised in the nuclear compartment of cardiomyocytes. Trdn-AS expression as well as the ratio between cardiac and skeletal muscle isoforms were down-regulated after experimental myocardial infarction. In murine cardiomyocytes, activation of Trdn-AS transcription with the CRISPR/dCas9-VPR system enhanced the ratio between cardiac and skeletal isoforms of Trdn.

CONCLUSION

The lncRNA TRDN-AS regulates the balance between cardiac and skeletal isoforms of triadin. This finding may have implications for the treatment of heart failure.

LPI-NRLMF: lncRNA-protein interaction prediction by neighborhood regularized logistic matrix factorization

LncRNA-protein interactions play important roles in many important cellular processes including signaling, transcriptional regulation, and even the generation and progression of complex diseases. However, experimental methods for determining proteins bound by a specific lncRNA remain expensive, difficult and time-consuming, and only a few theoretical approaches are available for predicting potential lncRNA-protein associations. In this study, we developed a novel matrix factorization computational approach to uncover lncRNA-protein relationships, namely lncRNA-protein interactions prediction by neighborhood regularized logistic matrix factorization (LPI-NRLMF). Moreover, it is a semi-supervised and does not need negative samples. As a result, new model obtained reliable performance in the leave-one-out cross validation (the AUC of 0.9025 and AUPR of 0.6924), which significantly improved the prediction performance of previous models. Furthermore, the case study demonstrated that many lncRNA-protein interactions predicted by our method can be successfully confirmed by experiments. It is anticipated that LPI-NRLMF could serve as a useful resource for potential lncRNA-protein association identification.

Regulation of Noncoding Transcriptome in Developing Photoreceptors by Rod Differentiation Factor NRL

Purpose

Transcriptome analysis by next generation sequencing allows qualitative and quantitative profiling of expression patterns associated with development and disease. However, most transcribed sequences do not encode proteins, and little is known about the functional relevance of noncoding (nc) transcriptome in neuronal subtypes. The goal of this study was to perform a comprehensive analysis of long noncoding (lncRNAs) and antisense (asRNAs) RNAs expressed in mouse retinal photoreceptors.

Methods

Transcriptomic profiles were generated at six developmental time points from flow-sorted Nrlp-GFP (rods) and Nrlp-GFP;Nrl-/- (S-cone like) mouse photoreceptors. Bioinformatic analysis was performed to identify novel noncoding transcripts and assess their regulation by rod differentiation factor neural retina leucine zipper (NRL). In situ hybridization (ISH) was used for validation and cellular localization.

Results

NcRNA profiles demonstrated dynamic yet specific expression signature and coexpression clusters during rod development. In addition to currently annotated 586 lncRNAs and 454 asRNAs, we identified 1037 lncRNAs and 243 asRNAs by de novo assembly. Of these, 119 lncRNAs showed altered expression in the absence of NRL and included NRL binding sites in their promoter/enhancer regions. ISH studies validated the expression of 24 lncRNAs (including 12 previously unannotated) and 4 asRNAs in photoreceptors. Coexpression analysis demonstrated 63 functional modules and 209 significant antisense-gene correlations, allowing us to predict possible role of these lncRNAs in rods.

Conclusions

Our studies reveal coregulation of coding and noncoding transcripts in rod photoreceptors by NRL and establish the framework for deciphering the function of ncRNAs during retinal development.

LncRNAs: From Basic Research to Medical Application

This review aimed to summarize the current research contents about long noncoding RNAs (lncRNAs) and some related lncRNAs as molecular biomarkers or therapy strategies in human cancer and cardiovascular diseases. Following the development of various kinds of sequencing technologies, lncRNAs have become one of the most unknown areas that need to be explored. First, the definition and classification of lncRNAs were constantly amended and supplemented because of their complexity and diversity. Second, several methods and strategies have been developed to study the characteristic of lncRNAs, including new species identifications, subcellular localization, gain or loss of function, molecular interaction, and bioinformatics analysis. Third, based on the present results from basic researches, the working mechanisms of lncRNAs were proved to be different forms of interactions involving DNAs, RNAs, and proteins. Fourth, lncRNA can play different important roles during the embryogenesis and organ differentiations. Finally, because of the tissue-specific expression of lncRNAs, they could be used as biomarkers or therapy targets and effectively applied in different kinds of diseases, such as human cancer and cardiovascular diseases.

Long Non-Coding RNAs: A Novel Paradigm for Toxicology

Long non-coding RNAs (lncRNAs) are over 200 nucleotides in length and are transcribed from the mammalian genome in a tissue-specific and developmentally regulated pattern. There is growing recognition that lncRNAs are novel biomarkers and/or key regulators of toxicological responses in humans and animal models. Lacking protein-coding capacity, the numerous types of lncRNAs possess a myriad of transcriptional regulatory functions that include cis and trans gene expression, transcription factor activity, chromatin remodeling, imprinting, and enhancer up-regulation. LncRNAs also influence mRNA processing, post-transcriptional regulation, and protein trafficking. Dysregulation of lncRNAs has been implicated in various human health outcomes such as various cancers, Alzheimer's disease, cardiovascular disease, autoimmune diseases, as well as intermediary metabolism such as glucose, lipid, and bile acid homeostasis. Interestingly, emerging evidence in the literature over the past five years has shown that lncRNA regulation is impacted by exposures to various chemicals such as polycyclic aromatic hydrocarbons, benzene, cadmium, chlorpyrifos-methyl, bisphenol A, phthalates, phenols, and bile acids. Recent technological advancements, including next-generation sequencing technologies and novel computational algorithms, have enabled the profiling and functional characterizations of lncRNAs on a genomic scale. In this review, we summarize the biogenesis and general biological functions of lncRNAs, highlight the important roles of lncRNAs in human diseases and especially during the toxicological responses to various xenobiotics, evaluate current methods for identifying aberrant lncRNA expression and molecular target interactions, and discuss the potential to implement these tools to address fundamental questions in toxicology.

Altered Expression of Long Noncoding RNAs in Blood After Ischemic Stroke and Proximity to Putative Stroke Risk Loci

BACKGROUND AND PURPOSE

Although peripheral blood mRNA and micro-RNA change after ischemic stroke, any role for long noncoding RNA (lncRNA), which comprise most of the genome and have been implicated in various diseases, is unknown. Thus, we hypothesized that lncRNA expression also changes after stroke.

METHODS

lncRNA expression was assessed in 266 whole-blood RNA samples drawn once per individual from patients with ischemic stroke and matched with vascular risk factor controls. Differential lncRNA expression was assessed by ANCOVA (P<0.005; fold change>|1.2|), principal components analysis, and hierarchical clustering on a derivation set (n=176) and confirmed on a validation set (n=90). Poststroke temporal lncRNA expression changes were assessed using ANCOVA with confounding factor correction (P<0.005; partial correlation with time since event >|0.4|). Because sexual dimorphism exists in stroke, analyses were performed for each sex separately.

RESULTS

A total of 299 lncRNAs were differentially expressed between stroke and control males, whereas 97 lncRNAs were differentially expressed between stroke and control females. Significant changes of lncRNA expression with time after stroke were detected for 49 lncRNAs in men and 31 lncRNAs in women. Some differentially expressed lncRNAs mapped close to genomic locations of previously identified putative stroke-risk genes, including lipoprotein, lipoprotein(a)-like 2, ABO (transferase A, α1-3-N-acetylgalactosaminyltransferase; transferase B, α1-3-galactosyltransferase) blood group, prostaglandin 12 synthase, and α-adducins.

CONCLUSIONS

This study provides evidence of altered and sexually dimorphic lncRNA expression in peripheral blood of patients with stroke compared with that of controls and suggests that lncRNAs have potential for stroke biomarker development. Some regulated lncRNA could regulate some previously identified putative stroke-risk genes.