NONCODEv4: exploring the world of long non-coding RNA genes

ARAP1-AS1: a novel long non-coding RNA with a vital regulatory role in human cancer development

Long non-coding RNAs (lncRNAs) have garnered significant attention in biomedical research due to their pivotal roles in gene expression regulation and their association with various human diseases. Among these lncRNAs, ArfGAP With RhoGAP Domain, Ankyrin Repeat, And PH Domain 1 - Antisense RNA 1 (ARAP1-AS1) has recently emerged as an novel oncogenic player. ARAP1-AS1 is prominently overexpressed in numerous solid tumors and wields influence by modulating gene expression and signaling pathways. This regulatory impact is realized through dual mechanisms, involving both competitive interactions with microRNAs and direct protein binding. ARAP1-AS1 assumes an important role in driving tumorigenesis and malignant tumor progression, affecting biological characteristics such as tumor expansion and metastasis. This paper provides a concise review of the regulatory role of ARAP1-AS1 in malignant tumors and discuss its potential clinical applications as a biomarker and therapeutic target. We also address existing knowledge gaps and suggest avenues for future research. ARAP1-AS1 serves as a prototypical example within the burgeoning field of lncRNA studies, offering insights into the broader landscape of non-coding RNA molecules. This investigation enhances our comprehension of the complex mechanisms that govern the progression of cancer.

Circular RNAs in tuberculosis and lung cancer

This review signifies the role of circular RNAs (circRNAs) in tuberculosis (TB) and lung cancer (LC), focusing on pathogenesis, diagnosis, and treatment. CircRNAs, a newly discovered type of non-coding RNA, have emerged as key regulators of gene expression and promising biomarkers in various bodily fluids due to their stability. The current review discusses circRNA biogenesis, highlighting their RNase-R resistance due to their loop forming structure, making them effective biomarkers. It details their roles in gene regulation, including splicing, transcription control, and miRNA interactions, and their impact on cellular processes and diseases. For LC, the review identifies circRNA dysregulation affecting cell growth, motility, and survival, and their potential as therapeutic targets and biomarkers. In TB, it addresses circRNAs' influence on host anti-TB immune responses, proposing their use as early diagnostic markers. The paper also explores the interplay between TB and LC, emphasizing circRNAs as dual biosignatures, and the necessity for differential diagnosis. It concludes that no single circRNA biomarker is universally applicable for both TB and LC. Ultimately, the review highlights the pivotal role of circRNAs in TB and LC, encouraging further research in biomarker identification and therapeutic development concomitant for both diseases.

Fusion of multi-source relationships and topology to infer lncRNA-protein interactions

Long non-coding RNAs (lncRNAs) are important factors involved in biological regulatory networks. Accurately predicting lncRNA-protein interactions (LPIs) is vital for clarifying lncRNA's functions and pathogenic mechanisms. Existing deep learning models have yet to yield satisfactory results in LPI prediction. Recently, graph autoencoders (GAEs) have seen rapid development, excelling in tasks like link prediction and node classification. We employed GAE technology for LPI prediction, devising the FMSRT-LPI model based on path masking and degree regression strategies and thereby achieving satisfactory outcomes. This represents the first known integration of path masking and degree regression strategies into the GAE framework for potential LPI inference. The effectiveness of our FMSRT-LPI model primarily relies on four key aspects. First, within the GAE framework, our model integrates multi-source relationships of lncRNAs and proteins with LPN's topological data. Second, the implemented masking strategy efficiently identifies LPN's key paths, reconstructs the network, and reduces the impact of redundant or incorrect data. Third, the integrated degree decoder balances degree and structural information, enhancing node representation. Fourth, the PolyLoss function we introduced is more appropriate for LPI prediction tasks. The results on multiple public datasets further demonstrate our model's potential in LPI prediction.

PIWI-Interacting RNAs: A Pivotal Regulator in Neurological Development and Disease

PIWI-interacting RNAs (piRNAs), a class of small non-coding RNAs (sncRNAs) with 24-32 nucleotides (nt), were initially identified in the reproductive system. Unlike microRNAs (miRNAs) or small interfering RNAs (siRNAs), piRNAs normally guide P-element-induced wimpy testis protein (PIWI) families to slice extensively complementary transposon transcripts without the seed pairing. Numerous studies have shown that piRNAs are abundantly expressed in the brain, and many of them are aberrantly regulated in central neural system (CNS) disorders. However, the role of piRNAs in the related developmental and pathological processes is unclear. The elucidation of piRNAs/PIWI would greatly improve the understanding of CNS development and ultimately lead to novel strategies to treat neural diseases. In this review, we summarized the relevant structure, properties, and databases of piRNAs and their functional roles in neural development and degenerative disorders. We hope that future studies of these piRNAs will facilitate the development of RNA-based therapeutics for CNS disorders.

The lncRNA TPT1-AS1 promotes the survival of neuroendocrine prostate cancer cells by facilitating autophagy

The lncRNA tumor protein translationally controlled 1-antisense RNA 1 (TPT1-AS1) is known for its oncogenic role in various cancers, but its impact on the pathological progression of prostate cancer remains unclear. Our previous study demonstrated that the RE1-silencing transcription factor (REST) regulates neuroendocrine differentiation (NED) in prostate cancer (PCA) by derepressing specific long non-coding RNAs (lncRNAs), including TPT1-AS1. In this study, we revealed that TPT1-AS1 is overexpressed in LNCaP and C4-2B cells after IL-6 and enzalutamide treatment. By analyzing The Cancer Genome Atlas (TCGA) prostate adenocarcinoma dataset, we detected upregulated TPT1-AS1 expression in neuroendocrine-associated PCA but not in prostate adenocarcinoma. Single-cell RNA sequencing data further confirmed the increased TPT1-AS1 levels in neuroendocrine prostate cancer (NEPC) cells. Surprisingly, functional experiments indicated that TPT1-AS1 overexpression had no stimulatory effect on NED in LNCaP cells and that TPT1-AS1 knockdown did not inhibit IL-6-induced NED. Transcriptomic analysis revealed the essential role of TPT1-AS1 in synaptogenesis and autophagy activation in neuroendocrine differentiated PCA cells induced by IL-6 and enzalutamide treatment. TPT1-AS1 was found to regulate the expression of autophagy-related genes that maintain neuroendocrine cell survival through autophagy activation. In conclusion, our data expand the current knowledge of REST-repressed lncRNAs in NED in PCA and highlight the contribution of TPT1-AS1 to protect neuroendocrine cells from cell death rather than inducing NED. Our study suggested that TPT1-AS1 plays a cytoprotective role in NEPC cells; thus, targeting TPT1-AS1 is a potential therapeutic strategy.

Prediction of LncRNA-Protein Interactions Based on Kernel Combinations and Graph Convolutional Networks

The complexes of long non-coding RNAs bound to proteins can be involved in regulating life activities at various stages of organisms. However, in the face of the growing number of lncRNAs and proteins, verifying LncRNA-Protein Interactions (LPI) based on traditional biological experiments is time-consuming and laborious. Therefore, with the improvement of computing power, predicting LPI has met new development opportunity. In virtue of the state-of-the-art works, a framework called LncRNA-Protein Interactions based on Kernel Combinations and Graph Convolutional Networks (LPI-KCGCN) has been proposed in this article. We first construct kernel matrices by taking advantage of extracting both the lncRNAs and protein concerning the sequence features, sequence similarity features, expression features, and gene ontology. Then reconstruct the existent kernel matrices as the input of the next step. Combined with known LPI interactions, the reconstructed similarity matrices, which can be used as features of the topology map of the LPI network, are exploited in extracting potential representations in the lncRNA and protein space using a two-layer Graph Convolutional Network. The predicted matrix can be finally obtained by training the network to produce scoring matrices w.r.t. lncRNAs and proteins. Different LPI-KCGCN variants are ensemble to derive the final prediction results and testify on balanced and unbalanced datasets. The 5-fold cross-validation shows that the optimal feature information combination on a dataset with 15.5% positive samples has an AUC value of 0.9714 and an AUPR value of 0.9216. On another highly unbalanced dataset with only 5% positive samples, LPI-KCGCN also has outperformed the state-of-the-art works, which achieved an AUC value of 0.9907 and an AUPR value of 0.9267.

LPI-IBWA: Predicting lncRNA-protein interactions based on an improved Bi-Random walk algorithm

Many studies have shown that long-chain noncoding RNAs (lncRNAs) are involved in a variety of biological processes such as post-transcriptional gene regulation, splicing, and translation by combining with corresponding proteins. Predicting lncRNA-protein interactions is an effective approach to infer the functions of lncRNAs. The paper proposes a new computational model named LPI-IBWA. At first, LPI-IBWA uses similarity kernel fusion (SKF) to integrate various types of biological information to construct lncRNA and protein similarity networks. Then, a bounded matrix completion model and a weighted k-nearest known neighbors algorithm are utilized to update the initial sparse lncRNA-protein interaction matrix. Based on the updated lncRNA-protein interaction matrix, the lncRNA similarity network and the protein similarity network are integrated into a heterogeneous network. Finally, an improved Bi-Random walk algorithm is used to predict novel latent lncRNA-protein interactions. 5-fold cross-validation experiments on a benchmark dataset showed that the AUC and AUPR of LPI-IBWA reach 0.920 and 0.736, respectively, which are higher than those of other state-of-the-art methods. Furthermore, the experimental results of case studies on a novel dataset also illustrated that LPI-IBWA could efficiently predict potential lncRNA-protein interactions.

Exploring lncRNAs associated with human pancreatic islet cell death induced by transfer of adoptive lymphocytes in a humanized mouse model

Background

Long noncoding RNA (lncRNA)-mediated posttranscriptional and epigenetic landscapes of gene regulation are associated with numerous human diseases. However, the regulatory mechanisms governing human β-cell function and survival remain unknown. Owing to technical and ethical constraints, studying the direct role of lncRNAs in β-cell function and survival in humans in vivo is difficult. Therefore, we utilized humanized mice with human islets to investigate lncRNA expression using whole transcriptome shotgun sequencing. Our study aimed to characterize lncRNAs that may be crucial for human islet cell function and survival.

Methods

Human β-cell death was induced in humanized mice engrafted with functional human islets. Using these humanized mice harboring human islets with induced β-cell death, we investigated lncRNA expression through whole transcriptome shotgun sequencing. Additionally, we systematically identified, characterized, and explored the regulatory functions of lncRNAs that are potentially important for human pancreatic islet cell function and survival.

Results

Human islet cell death was induced in humanized mice engrafted with functional human islets. RNA sequencing analysis of isolated human islets, islet grafts from humanized mice with and without induced cell death, revealed aberrant expression of a distinct set of lncRNAs that are associated with the deregulated mRNAs important for cellular processes and molecular pathways related to β-cell function and survival. A total of 10 lncRNA isoforms (SCYL1-1:22, POLG2-1:1, CTRB1-1:1, SRPK1-1:1, GTF3C5-1:1, PPY-1:1, CTRB1-1:5, CPA5-1:1, BCAR1-2:1, and CTRB1-1:4) were identified as highly enriched and specific to human islets. These lncRNAs were deregulated in human islets from donors with different BMIs and with type 2 diabetes (T2D), as well as in cultured human islets with glucose stimulation and induced cell death induced by cytokines. Aberrant expression of these lncRNAs was detected in the exosomes from the medium used to culture islets with cytokines.

Conclusion

Islet-enriched and specific human lncRNAs are deregulated in human islet grafts and cultured human islets with induced cell death. These lncRNAs may be crucial for human β-cell function and survival and could have an impact on identifying biomarkers for β-cell loss and discovering novel therapeutic targets to enhance β-cell function and survival.

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.

Fatty acid metabolism decreased while sexual selection increased in brown rats spreading south

For mammals that originate in the cold north, adapting to warmer environments is crucial for southwards invasion. The brown rat (Rattus norvegicus) originated in Northeast China and has become a global pest. R. n. humiliatus (RNH) spread from the northeast, where R. n. caraco (RNC) lives, to North China and diverged to form a subspecies. Genomic analyses revealed that subspecies differentiation was promoted by temperature but impeded by gene flow and that genes related to fatty acid metabolism were under the strongest selection. Transcriptome analyses revealed downregulated hepatic genes related to fatty acid metabolism and upregulated those related to pheromones in RNH vs. RNC. Similar patterns were observed in relation to cold/warm acclimation. RNH preferred mates with stronger pheromone signals intra-populationally and more genetic divergence inter-populationally. We concluded that RNH experienced reduced fat utilization and increased pheromone-mediated sexual selection during its invasion from the cold north to warm south.

REST-repressed lncRNA LINC01801 induces neuroendocrine differentiation in prostate cancer via transcriptional activation of autophagy

The association between REST reduction and the development of neuroendocrine prostate cancer (NEPC), a novel drug-resistant and lethal variant of castration-resistant prostate cancer (CRPC), is well established. To better understand the mechanisms underlying this process, we aimed to identify REST-repressed long noncoding RNAs (lncRNAs) that promote neuroendocrine differentiation (NED), thus facilitating targeted therapy-induced resistance. In this study, we used data from REST knockdown RNA sequencing combined with siRNA screening to determine that LINC01801 was upregulated and played a crucial role in NED in prostate cancer (PCa). Using The Cancer Genome Atlas (TCGA) prostate adenocarcinoma database and CRPC samples collected in our laboratory, we demonstrated that LINC01801 expression is upregulated in NEPC. Functional experiments revealed that overexpression of LINC01801 had a slight stimulatory effect on the NED of LNCaP cells, while downregulation of LINC01801 significantly inhibited the induction of NED. Mechanistically, LINC01801 is transcriptionally repressed by REST, and transcriptomic analysis revealed that LINC01801 preferentially affects the autophagy pathway. LINC01801 was found to function as a competing endogenous RNA (ceRNA) to regulate the expression of autophagy-related genes by sponging hsa-miR-6889-3p in prostate cancer cells. In conclusion, our data expand the current knowledge of REST-induced NED and highlight the contribution of the REST-LINC01801-hsa-miR-6889-3p axis to autophagic induction, which may provide promising avenues for therapeutic opportunities.

Identification of a novel immune-related long noncoding RNA in carp primary macrophages associated with bisphenol A' s immunoregulatory effects

Increasing evidence suggests that long non-coding RNAs (lncRNAs) play pivotal roles in various biological processes. However, current studies on lncRNAs mostly focus on mammalian species, with little research on the functional roles of lncRNAs in teleost fish. Here, we identified a novel intergenic lncRNA (linc-93.2) in the head kidney primary macrophages of common carp (Cyprinus carpio) after exposure to a typical environmental endocrine disrupting chemical, bisphenol A (BPA). As a result, linc-93.2 was more than 3,619 bp in length and predominantly localized to the nucleus of primary macrophages other than cytoplasm, with the highest expression level in spleen followed by head kidney among different organs. Bioinformatic analysis predicted a cis-target gene, dennd1b, and 20 trans-target genes including hsp70, gna13 and rasgap, were potentially regulated by linc-93.2; NFκB and estrogen receptor (ERα) binding sites were located in the promoter region upstream of its transcription start site, which together suggested the involvement of linc-93.2 in immune and neurological functions in fish. Based on that, the expression level of linc-93.2 was determined in macrophages following acute lipopolysaccharide (LPS) and BPA treatments, both of which significantly induced linc-93.2 and IL-1β expression in cells. Moreover, a NF-κB inhibitor PDTC significantly reduced linc-93.2 expression in macrophages, but co-exposure of macrophages to PDTC with BPA or LPS could significantly rescue linc-93.2 expression, consistent with the observation on that LPS or BPA alone significantly induced both linc-93.2 and its target gene expression. Interestingly, linc-93.2 and its target gene expression was significantly suppressed by an ER antagonist ICI 182,780, however, the co-exposure of macrophages to ICI 182,780 with BPA failed to attenuate their declined expression. Overall, the current study demonstrated that linc-93.2, a novel immune-related lncRNA, may participate in the immune processes of common carp macrophages via the NF-κB and ER pathway. The results presented in this study enhance our understanding of the immunotoxin mechanisms of BPA in teleost fish.

Data resources and computational methods for lncRNA-disease association prediction

Increasing interest has been attracted in deciphering the potential disease pathogenesis through lncRNA-disease association (LDA) prediction, regarding to the diverse functional roles of lncRNAs in genome regulation. Whilst, computational models and algorithms benefit systematic biology research, even facilitate the classical biological experimental procedures. In this review, we introduce representative diseases associated with lncRNAs, such as cancers, cardiovascular diseases, and neurological diseases. Current publicly available resources related to lncRNAs and diseases have also been included. Furthermore, all of the 64 computational methods for LDA prediction have been divided into 5 groups, including machine learning-based methods, network propagation-based methods, matrix factorization- and completion-based methods, deep learning-based methods, and graph neural network-based methods. The common evaluation methods and metrics in LDA prediction have also been discussed. Finally, the challenges and future trends in LDA prediction have been discussed. Recent advances in LDA prediction approaches have been summarized in the GitHub repository at https://github.com/sheng-n/lncRNA-disease-methods.

ENCD: a manually curated database of experimentally supported endocrine system disease and lncRNA associations

ENCD (http://www.bio-server.cn/ENCD/) is a manually curated database that provides comprehensive experimentally supported associations among endocrine system diseases (ESDs) and long non-coding ribonucleic acid (lncRNAs). The incidence of ESDs has increased in recent years, often accompanying other chronic diseases, and can lead to disability. A growing body of research suggests that lncRNA plays an important role in the progression and metastasis of ESDs. However, there are no resources focused on collecting and integrating the latest and experimentally supported lncRNA-ESD associations. Hence, we developed an ENCD database that consists of 1379 associations between 35 ESDs and 501 lncRNAs in 12 human tissues curated from literature. By using ENCD, users can explore the genetic data for diseases corresponding to the body parts of interest as well as study the lncRNA regulating mechanism for ESDs. ENCD also provides a flexible tool to visualize a disease- or gene-centric regulatory network. In addition, ENCD offers a submission page for researchers to submit their newly discovered endocrine disorders-genetic data entries online. Collectively, ENCD will provide comprehensive insights for investigating the ESDs associated with lncRNAs. Database URL http://www.bio-server.cn/ENCD.

Folic Acid and Vitamin B12 Prevent Deleterious Effects of Rotenone on Object Novelty Recognition Memory and Kynu Expression in an Animal Model of Parkinson's Disease

Parkinson's disease (PD) is characterized by a range of motor signs, but cognitive dysfunction is also observed. Supplementation with folic acid and vitamin B12 is expected to prevent cognitive impairment. To test this in PD, we promoted a lesion within the substantia nigra pars compacta of rats using the neurotoxin rotenone. In the sequence, the animals were supplemented with folic acid and vitamin B12 for 14 consecutive days and subjected to the object recognition test. We observed an impairment in object recognition memory after rotenone administration, which was prevented by supplementation (p < 0.01). Supplementation may adjust gene expression through efficient DNA methylation. To verify this, we measured the expression and methylation of the kynureninase gene (Kynu), whose product metabolizes neurotoxic metabolites often accumulated in PD as kynurenine. Supplementation prevented the decrease in Kynu expression induced by rotenone in the substantia nigra (p < 0.05), corroborating the behavioral data. No differences were observed concerning the methylation analysis of two CpG sites in the Kynu promoter. Instead, we suggest that folic acid and vitamin B12 increased global DNA methylation, reduced the expression of Kynu inhibitors, maintained Kynu-dependent pathway homeostasis, and prevented the memory impairment induced by rotenone. Our study raises the possibility of adjuvant therapy for PD with folic acid and vitamin B12.

LncBook 2.0: integrating human long non-coding RNAs with multi-omics annotations

LncBook, a comprehensive resource of human long non-coding RNAs (lncRNAs), has been used in a wide range of lncRNA studies across various biological contexts. Here, we present LncBook 2.0 (https://ngdc.cncb.ac.cn/lncbook), with significant updates and enhancements as follows: (i) incorporation of 119 722 new transcripts, 9632 new genes, and gene structure update of 21 305 lncRNAs; (ii) characterization of conservation features of human lncRNA genes across 40 vertebrates; (iii) integration of lncRNA-encoded small proteins; (iv) enrichment of expression and DNA methylation profiles with more biological contexts and (v) identification of lncRNA-protein interactions and improved prediction of lncRNA-miRNA interactions. Collectively, LncBook 2.0 accommodates a high-quality collection of 95 243 lncRNA genes and 323 950 transcripts and incorporates their abundant annotations at different omics levels, thereby enabling users to decipher functional significance of lncRNAs in different biological contexts.

Finding lncRNA-Protein Interactions Based on Deep Learning With Dual-Net Neural Architecture

The identification of lncRNA-protein interactions (LPIs) is important to understand the biological functions and molecular mechanisms of lncRNAs. However, most computational models are evaluated on a unique dataset, thereby resulting in prediction bias. Furthermore, previous models have not uncovered potential proteins (or lncRNAs) interacting with a new lncRNA (or protein). Finally, the performance of these models can be improved. In this study, we develop a Deep Learning framework with Dual-net Neural architecture to find potential LPIs (LPI-DLDN). First, five LPI datasets are collected. Second, the features of lncRNAs and proteins are extracted by Pyfeat and BioTriangle, respectively. Third, these features are concatenated as a vector after dimension reduction. Finally, a deep learning model with dual-net neural architecture is designed to classify lncRNA-protein pairs. LPI-DLDN is compared with six state-of-the-art LPI prediction methods (LPI-XGBoost, LPI-HeteSim, LPI-NRLMF, PLIPCOM, LPI-CNNCP, and Capsule-LPI) under four cross validations. The results demonstrate the powerful LPI classification performance of LPI-DLDN. Case study analyses show that there may be interactions between RP11-439E19.10 and Q15717, and between RP11-196G18.22 and Q9NUL5. The novelty of LPI-DLDN remains, integrating various biological features, designing a novel deep learning-based LPI identification framework, and selecting the optimal LPI feature subset based on feature importance ranking.

Identification of Key Non-coding RNAs and Transcription Factors in Calcific Aortic Valve Disease

Background

Calcific aortic valve disease (CAVD) is one of the most frequently occurring valvular heart diseases among the aging population. Currently, there is no known pharmacological treatment available to delay or reverse CAVD progression. The regulation of gene expression could contribute to the initiation, progression, and treatment of CAVD. Non-coding RNAs (ncRNAs) and transcription factors play essential regulatory roles in gene expression in CAVD; thus, further research is urgently needed.

Materials and Methods

The gene-expression profiles of GSE51472 and GSE12644 were obtained from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) were identified in each dataset. A protein-protein-interaction (PPI) network of DEGs was then constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins database, and functional modules were analyzed with ClusterOne plugin in Cytoscape. Furthermore, Gene Ontology-functional annotation and Kyoto Encyclopedia of Genes and Genomes-pathway analysis were conducted for each functional module. Most crucially, ncRNAs and transcription factors acting on each functional module were separately identified using the RNAInter and TRRUST databases. The expression of predicted transcription factors and key genes was validated using GSE51472 and GSE12644. Furthermore, quantitative real-time PCR (qRT-PCR) experiments were performed to validate the differential expression of most promising candidates in human CAVD and control samples.

Results

Among 552 DEGs, 383 were upregulated and 169 were downregulated. In the PPI network, 15 functional modules involving 182 genes and proteins were identified. After hypergeometric testing, 45 ncRNAs and 33 transcription factors were obtained. Among the predicted transcription factors, CIITA, HIF1A, JUN, POU2F2, and STAT6 were differentially expressed in both the training and validation sets. In addition, we found that key genes, namely, CD2, CD86, CXCL8, FCGR3B, GZMB, ITGB2, LY86, MMP9, PPBP, and TYROBP were also differentially expressed in both the training and validation sets. Among the most promising candidates, differential expressions of ETS1, JUN, NFKB1, RELA, SP1, STAT1, ANCR, and LOC101927497 were identified via qRT-PCR experiments.

Conclusion

In this study, we identified functional modules with ncRNAs and transcription factors involved in CAVD pathogenesis. The current results suggest candidate molecules for further research on CAVD.

A multifunctional locus controls motor neuron differentiation through short and long noncoding RNAs

The transition from dividing progenitors to postmitotic motor neurons (MNs) is orchestrated by a series of events, which are mainly studied at the transcriptional level by analyzing the activity of specific programming transcription factors. Here, we identify a post‐transcriptional role of a MN‐specific transcriptional unit (MN2) harboring a lncRNA (lncMN2‐203) and two miRNAs (miR‐325‐3p and miR‐384‐5p) in this transition. Through the use of in vitro mESC differentiation and single‐cell sequencing of CRISPR/Cas9 mutants, we demonstrate that lncMN2‐203 affects MN differentiation by sponging miR‐466i‐5p and upregulating its targets, including several factors involved in neuronal differentiation and function. In parallel, miR‐325‐3p and miR‐384‐5p, co‐transcribed with lncMN2‐203, act by repressing proliferation‐related factors. These findings indicate the functional relevance of the MN2 locus and exemplify additional layers of specificity regulation in MN differentiation.

MALAT1 lncRNA and Parkinson's Disease: The role in the Pathophysiology and Significance for Diagnostic and Therapeutic Approaches

Parkinson's disease (PD) is the second most common age-related neurodegenerative disorder. PD is characterized by progressive loss of dopamine-producing neurons in the substantia nigra (SN) region of brain tissue followed by the α-synuclein-based Lewy bodies' formation. These conditions are manifested by various motor and non-motor symptoms such as resting tremor, limb rigidity, bradykinesia and posture instability, cognitive impairment, sleep disorders, and emotional and memory dysfunctions. Long non-coding RNAs (lncRNAs) are closely related to protein-coding genes and are involved in various biological processes. Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) lncRNA is involved in different pathways, including alternative splicing, transcriptional regulation, and post-transcriptional regulation, and also interacts with RNAs as a miRNA sponge. MALAT1 is highly expressed in brain tissues and several lines of evidence suggested it is probably involved in synapse generation and other neurophysiological pathways. This narrative review discussed all aspects of MALAT1-associated mechanisms involved in the PD pathogenesis, i.e., perturbed α-synuclein homeostasis, apoptosis and autophagy, and neuro-inflammation. Lastly, the possible applications of MALAT1 as a diagnostic biomarker and its importance to developing therapeutic strategies were highlighted. The literature search was conducted using neurodegeneration, neurodegenerative disorders, Parkinson's disease, lncRNA, and MALAT1 as search items in Google Scholar, Web of Knowledge, PubMed, and Scopus up to December 2021.

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

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

Construction of integrative transcriptome to boost systematic exploration of Bougainvillea

Members of the genus Bougainvillea are rich sources of natural dyes, pigments, and traditional medicines. They are also commonly used as ornamentals in roadside landscape construction. However, the horticultural development of Bougainvillea flowers with extended growth periods and coloration is not always feasible. One reason is limited molecular knowledge and no genomic information for Bougainvillea. Here, we compiled an integrative transcriptome of all expressed transcripts for Bougainvillea × buttiana Miss Manila by integrating 20 Illumina-sequencing RNA transcriptomes. The integrative transcriptome consisted of 97,623 distinct transcripts. Of these, 47,006 were protein-coding, 31,109 were non-coding, and 19,508 were unannotated. In addition, we affirmed that the integrative transcriptome could serve as a surrogate reference to the genome in aiding accurate transcriptome assembly. For convenience, we curated the integrative transcriptome database for Bougainvillea, namely InTransBo, which can be freely accessed at http://www.bio-add.org/InTransBo/index.jsp . To the best of our knowledge, the present study is the most comprehensive genomic resource for Bougainvillea up-to-date. The integrative transcriptome helps fill the genomic gap and elucidate the transcriptional nature of Bougainvillea. It may also advance progress in the precise regulation of flowering in horticulture. The same strategy can be readily applied toward the systematic exploration of other plant species lacking complete genomic information.

gGATLDA: lncRNA-disease association prediction based on graph-level graph attention network

Background

Long non-coding RNAs (lncRNAs) are related to human diseases by regulating gene expression. Identifying lncRNA-disease associations (LDAs) will contribute to diagnose, treatment, and prognosis of diseases. However, the identification of LDAs by the biological experiments is time-consuming, costly and inefficient. Therefore, the development of efficient and high-accuracy computational methods for predicting LDAs is of great significance.

Results

In this paper, we propose a novel computational method (gGATLDA) to predict LDAs based on graph-level graph attention network. Firstly, we extract the enclosing subgraphs of each lncRNA-disease pair. Secondly, we construct the feature vectors by integrating lncRNA similarity and disease similarity as node attributes in subgraphs. Finally, we train a graph neural network (GNN) model by feeding the subgraphs and feature vectors to it, and use the trained GNN model to predict lncRNA-disease potential association scores. The experimental results show that our method can achieve higher area under the receiver operation characteristic curve (AUC), area under the precision recall curve (AUPR), accuracy and F1-Score than the state-of-the-art methods in five fold cross-validation. Case studies show that our method can effectively identify lncRNAs associated with breast cancer, gastric cancer, prostate cancer, and renal cancer.

Conclusion

The experimental results indicate that our method is a useful approach for predicting potential LDAs.

LPI-EnEDT: an ensemble framework with extra tree and decision tree classifiers for imbalanced lncRNA-protein interaction data classification

BACKGROUND

Long noncoding RNAs (lncRNAs) have dense linkages with various biological processes. Identifying interacting lncRNA-protein pairs contributes to understand the functions and mechanisms of lncRNAs. Wet experiments are costly and time-consuming. Most computational methods failed to observe the imbalanced characterize of lncRNA-protein interaction (LPI) data. More importantly, they were measured based on a unique dataset, which produced the prediction bias.

RESULTS

In this study, we develop an Ensemble framework (LPI-EnEDT) with Extra tree and Decision Tree classifiers to implement imbalanced LPI data classification. First, five LPI datasets are arranged. Second, lncRNAs and proteins are separately characterized based on Pyfeat and BioTriangle and concatenated as a vector to represent each lncRNA-protein pair. Finally, an ensemble framework with Extra tree and decision tree classifiers is developed to classify unlabeled lncRNA-protein pairs. The comparative experiments demonstrate that LPI-EnEDT outperforms four classical LPI prediction methods (LPI-BLS, LPI-CatBoost, LPI-SKF, and PLIPCOM) under cross validations on lncRNAs, proteins, and LPIs. The average AUC values on the five datasets are 0.8480, 0,7078, and 0.9066 under the three cross validations, respectively. The average AUPRs are 0.8175, 0.7265, and 0.8882, respectively. Case analyses suggest that there are underlying associations between HOTTIP and Q9Y6M1, NRON and Q15717.

CONCLUSIONS

Fusing diverse biological features of lncRNAs and proteins and exploiting an ensemble learning model with Extra tree and decision tree classifiers, this work focus on imbalanced LPI data classification as well as interaction information inference for a new lncRNA (or protein).

LPI-HyADBS: a hybrid framework for lncRNA-protein interaction prediction integrating feature selection and classification

Background

Long noncoding RNAs (lncRNAs) have dense linkages with a plethora of important cellular activities. lncRNAs exert functions by linking with corresponding RNA-binding proteins. Since experimental techniques to detect lncRNA-protein interactions (LPIs) are laborious and time-consuming, a few computational methods have been reported for LPI prediction. However, computation-based LPI identification methods have the following limitations: (1) Most methods were evaluated on a single dataset, and researchers may thus fail to measure their generalization ability. (2) The majority of methods were validated under cross validation on lncRNA-protein pairs, did not investigate the performance under other cross validations, especially for cross validation on independent lncRNAs and independent proteins. (3) lncRNAs and proteins have abundant biological information, how to select informative features need to further investigate.

Results

Under a hybrid framework (LPI-HyADBS) integrating feature selection based on AdaBoost, and classification models including deep neural network (DNN), extreme gradient Boost (XGBoost), and SVM with a penalty Coefficient of misclassification (C-SVM), this work focuses on finding new LPIs. First, five datasets are arranged. Each dataset contains lncRNA sequences, protein sequences, and an LPI network. Second, biological features of lncRNAs and proteins are acquired based on Pyfeat. Third, the obtained features of lncRNAs and proteins are selected based on AdaBoost and concatenated to depict each LPI sample. Fourth, DNN, XGBoost, and C-SVM are used to classify lncRNA-protein pairs based on the concatenated features. Finally, a hybrid framework is developed to integrate the classification results from the above three classifiers. LPI-HyADBS is compared to six classical LPI prediction approaches (LPI-SKF, LPI-NRLMF, Capsule-LPI, LPI-CNNCP, LPLNP, and LPBNI) on five datasets under 5-fold cross validations on lncRNAs, proteins, lncRNA-protein pairs, and independent lncRNAs and independent proteins. The results show LPI-HyADBS has the best LPI prediction performance under four different cross validations. In particular, LPI-HyADBS obtains better classification ability than other six approaches under the constructed independent dataset. Case analyses suggest that there is relevance between ZNF667-AS1 and Q15717.

Conclusions

Integrating feature selection approach based on AdaBoost, three classification techniques including DNN, XGBoost, and C-SVM, this work develops a hybrid framework to identify new linkages between lncRNAs and proteins.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04485-x.

Long Intergenic Noncoding RNA OIN1 Promotes Ovarian Cancer Growth by Modulating Apoptosis-Related Gene Expression

Patients with advanced ovarian cancer usually exhibit high mortality rates, thus more efficient therapeutic strategies are expected to be developed. Recent transcriptomic studies revealed that long intergenic noncoding RNAs (lincRNAs) can be a new class of molecular targets for cancer management, because lincRNAs likely exert tissue-specific activities compared with protein-coding genes or other noncoding RNAs. We here show that an unannotated lincRNA originated from chromosome 10q21 and designated as ovarian cancer long intergenic noncoding RNA 1 (OIN1), is often overexpressed in ovarian cancer tissues compared with normal ovaries as analyzed by RNA sequencing. OIN1 silencing by specific siRNAs significantly exerted proliferation inhibition and enhanced apoptosis in ovarian cancer cells. Notably, RNA sequencing showed that OIN1 expression was negatively correlated with the expression of apoptosis-related genes ras association domain family member 5 (RASSF5) and adenosine A1 receptor (ADORA1), which were upregulated by OIN1 knockdown in ovarian cancer cells. OIN1-specifc siRNA injection was effective to suppress in vivo tumor growth of ovarian cancer cells inoculated in immunodeficient mice. Taken together, OIN1 could function as a tumor-promoting lincRNA in ovarian cancer through modulating apoptosis and will be a potential molecular target for ovarian cancer management.

LPI-deepGBDT: a multiple-layer deep framework based on gradient boosting decision trees for lncRNA-protein interaction identification

Background

Long noncoding RNAs (lncRNAs) play important roles in various biological and pathological processes. Discovery of lncRNA–protein interactions (LPIs) contributes to understand the biological functions and mechanisms of lncRNAs. Although wet experiments find a few interactions between lncRNAs and proteins, experimental techniques are costly and time-consuming. Therefore, computational methods are increasingly exploited to uncover the possible associations. However, existing computational methods have several limitations. First, majority of them were measured based on one simple dataset, which may result in the prediction bias. Second, few of them are applied to identify relevant data for new lncRNAs (or proteins). Finally, they failed to utilize diverse biological information of lncRNAs and proteins.

Results

Under the feed-forward deep architecture based on gradient boosting decision trees (LPI-deepGBDT), this work focuses on classify unobserved LPIs. First, three human LPI datasets and two plant LPI datasets are arranged. Second, the biological features of lncRNAs and proteins are extracted by Pyfeat and BioProt, respectively. Thirdly, the features are dimensionally reduced and concatenated as a vector to represent an lncRNA–protein pair. Finally, a deep architecture composed of forward mappings and inverse mappings is developed to predict underlying linkages between lncRNAs and proteins. LPI-deepGBDT is compared with five classical LPI prediction models (LPI-BLS, LPI-CatBoost, PLIPCOM, LPI-SKF, and LPI-HNM) under three cross validations on lncRNAs, proteins, lncRNA–protein pairs, respectively. It obtains the best average AUC and AUPR values under the majority of situations, significantly outperforming other five LPI identification methods. That is, AUCs computed by LPI-deepGBDT are 0.8321, 0.6815, and 0.9073, respectively and AUPRs are 0.8095, 0.6771, and 0.8849, respectively. The results demonstrate the powerful classification ability of LPI-deepGBDT. Case study analyses show that there may be interactions between GAS5 and Q15717, RAB30-AS1 and O00425, and LINC-01572 and P35637.

Conclusions

Integrating ensemble learning and hierarchical distributed representations and building a multiple-layered deep architecture, this work improves LPI prediction performance as well as effectively probes interaction data for new lncRNAs/proteins.

Global identification of long non-coding RNAs involved in the induction of spinach flowering

Background

Spinach is a beneficial annual vegetable species and sensitive to the bolting or early flowering, which causes a large reduction in quality and productivity. Indeed, bolting is an event induced by the coordinated effects of various environmental factors and endogenous genetic components. Although some key flowering responsive genes have been identified in spinach, non-coding RNA molecules like long non-coding RNAs (lncRNAs) were not investigated yet. Herein, we used bioinformatic approaches to analyze the transcriptome datasets from two different accessions Viroflay and Kashan at two vegetative and reproductive stages to reveal novel lncRNAs and the construction of the lncRNA-mRNA co-expression network. Additionally, correlations among gene expression modules and phenotypic traits were investigated; day to flowering was chosen as our interesting trait.

Results

In the present study, we identified a total of 1141 lncRNAs, of which 111 were differentially expressed between vegetative and reproductive stages. The GO and KEGG analyses carried out on the cis target gene of lncRNAs showed that the lncRNAs play an important role in the regulation of flowering spinach. Network analysis pinpointed several well-known flowering-related genes such as ELF, COL1, FLT, and FPF1 and also some putative TFs like MYB, WRKY, GATA, and MADS-box that are important regulators of flowering in spinach and could be potential targets for lncRNAs.

Conclusions

This study is the first report on identifying bolting and flowering-related lncRNAs based on transcriptome sequencing in spinach, which provides a useful resource for future functional genomics studies, genes expression researches, evaluating genes regulatory networks and molecular breeding programs in the regulation of the genetic mechanisms related to bolting in spinach.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07989-1.

A novel lncRNA-protein interaction prediction method based on deep forest with cascade forest structure

Long noncoding RNAs (lncRNAs) regulate many biological processes by interacting with corresponding RNA-binding proteins. The identification of lncRNA-protein Interactions (LPIs) is significantly important to well characterize the biological functions and mechanisms of lncRNAs. Existing computational methods have been effectively applied to LPI prediction. However, the majority of them were evaluated only on one LPI dataset, thereby resulting in prediction bias. More importantly, part of models did not discover possible LPIs for new lncRNAs (or proteins). In addition, the prediction performance remains limited. To solve with the above problems, in this study, we develop a Deep Forest-based LPI prediction method (LPIDF). First, five LPI datasets are obtained and the corresponding sequence information of lncRNAs and proteins are collected. Second, features of lncRNAs and proteins are constructed based on four-nucleotide composition and BioSeq2vec with encoder-decoder structure, respectively. Finally, a deep forest model with cascade forest structure is developed to find new LPIs. We compare LPIDF with four classical association prediction models based on three fivefold cross validations on lncRNAs, proteins, and LPIs. LPIDF obtains better average AUCs of 0.9012, 0.6937 and 0.9457, and the best average AUPRs of 0.9022, 0.6860, and 0.9382, respectively, for the three CVs, significantly outperforming other methods. The results show that the lncRNA FTX may interact with the protein P35637 and needs further validation.

Far from the nuclear crowd: Cytoplasmic lncRNA and their implications in synaptic plasticity and memory

A striking proportion of long non-coding RNAs are expressed specifically in the mammalian brain. Advances in genome-wide sequencing detected widespread diversity in neuronal lncRNAs based on their expression pattern, localization and function. A growing body of literature proposes that localization of lncRNAs is a critical determinant of their function. A rising number of recent findings documented distinct cytoplasmic functions of lncRNAs that are linked to activity-induced control of synaptic plasticity. However, the comprehensive role of cytoplasmic lncRNAs in neuronal functions is less understood. This review surveys our current understanding of lncRNAs that regulate the cytoplasmic life of mRNAs. We discuss the necessity of subcellular localization of lncRNAs in neuronal dendrites and the impact of their compartmentalized positioning on localized translation at the synapse. We have highlighted how lncRNAs modify a functional compartment to meet the demand for input-specific control of synaptic plasticity and memory.

Brain Long Noncoding RNAs: Multitask Regulators of Neuronal Differentiation and Function

The extraordinary cellular diversity and the complex connections established within different cells types render the nervous system of vertebrates one of the most sophisticated tissues found in living organisms. Such complexity is ensured by numerous regulatory mechanisms that provide tight spatiotemporal control, robustness and reliability. While the unusual abundance of long noncoding RNAs (lncRNAs) in nervous tissues was traditionally puzzling, it is becoming clear that these molecules have genuine regulatory functions in the brain and they are essential for neuronal physiology. The canonical view of RNA as predominantly a 'coding molecule' has been largely surpassed, together with the conception that lncRNAs only represent 'waste material' produced by cells as a side effect of pervasive transcription. Here we review a growing body of evidence showing that lncRNAs play key roles in several regulatory mechanisms of neurons and other brain cells. In particular, neuronal lncRNAs are crucial for orchestrating neurogenesis, for tuning neuronal differentiation and for the exact calibration of neuronal excitability. Moreover, their diversity and the association to neurodegenerative diseases render them particularly interesting as putative biomarkers for brain disease. Overall, we foresee that in the future a more systematic scrutiny of lncRNA functions will be instrumental for an exhaustive understanding of neuronal pathophysiology.

Long non-coding RNAs and their involvement in bipolar disorders

Non-coding RNAs (nc-RNAs) can be defined as RNA molecules that are not translated into proteins. Although the functional meaning of many nc-RNAs remains still to be verified, several of these molecules have a clear biological importance, which goes from translation of mRNAs to DNA replication. Indeed, regulatory nc-RNAs can be classified into two groups: short non-coding RNAs (sncRNAs) and long-non coding RNAs (lncRNAs). In the last years, lncRNAs have gained increasing importance in the study of gene regulation, helping authors understand the molecular mechanisms underlying cellular physiology and pathology. LncRNAs are greater than 200 bp and accumulate in nucleus, cytoplasm and exosomes with high tissue specificity, acting in cis or in trans in order to exert enhancer or silencer modulation on gene expression. Such regulatory features, which are widespread in human cells and tissues, can be disrupted in several morbid states. Recent evidences may suggest a disruption of lncRNAs in bipolar disorders, a cluster of severe, chronic and disabling psychiatric diseases, which are characterized by major depressive states cyclically alternating with manic episodes. Here, the authors reviewed genes, classification, biogenesis, structures, functions and databases regarding lncRNAs, and also focused on bipolar disorders, in which some lncRNAs, especially those involved in inflammation and neuronal development, has reported to be dysregulated.

Deeply Mining a Universe of Peptides Encoded by Long Noncoding RNAs

Many small ORFs embedded in long noncoding RNA (lncRNA) transcripts have been shown to encode biologically functional polypeptides (small ORF-encoded polypeptides [SEPs]) in different organisms. Despite some novel SEPs have been found, the identification is still hampered by their poor predictability, diminutive size, and low relative abundance. Here, we take advantage of NONCODE, a repository containing the most complete collection and annotation of lncRNA transcripts from different species, to build a novel database that attempts to maximize a collection of SEPs from human and mouse lncRNA transcripts. In order to further improve SEP discovery, we implemented two effective and complementary polypeptide enrichment strategies using 30-kDa molecular weight cutoff filter and C8 solid-phase extraction column. These combined strategies enabled us to discover 353 SEPs from eight human cell lines and 409 SEPs from three mouse cell lines and eight mouse tissues. Importantly, 19 of them were then verified through in vitro expression, immunoblotting, parallel reaction monitoring, and synthetic peptides. Subsequent bioinformatics analysis revealed that some of the physical and chemical properties of these novel SEPs, including amino acid composition and codon usage, are different from those commonly found in canonical proteins. Intriguingly, nearly 65% of the identified SEPs were found to be initiated with non-AUG start codons. The 762 novel SEPs probably represent the largest number of SEPs detected by MS reported to date. These novel SEPs might not only provide new clues for the annotation of noncoding elements in the genome but also serve as a valuable resource for functional study.

Multi-omics annotation of human long non-coding RNAs

LncRNAs (long non-coding RNAs) are pervasively transcribed in the human genome and also extensively involved in a variety of essential biological processes and human diseases. The comprehensive annotation of human lncRNAs is of great significance in navigating the functional landscape of the human genome and deepening the understanding of the multi-featured RNA world. However, the unique characteristics of lncRNAs as well as their enormous quantity have complicated and challenged the annotation of lncRNAs. Advances in high-throughput sequencing technologies give rise to a large volume of omics data that are generated at an unprecedented rate and scale, providing possibilities in the identification, characterization and functional annotation of lncRNAs. Here, we review the recent important discoveries of human lncRNAs through analysis of various omics data and summarize specialized lncRNA database resources. Moreover, we highlight the multi-omics integrative analysis as a powerful strategy to efficiently discover and characterize the functional lncRNAs and elucidate their potential molecular mechanisms.

Long non‑coding RNA NEAT1 regulates glioma cell proliferation and apoptosis by competitively binding to microRNA‑324‑5p and upregulating KCTD20 expression

Previous studies have demonstrated that long non‑coding RNAs (lncRNAs) serve a key role in the development and progression of several types of cancer, including glioma. The lncRNA nuclear paraspeckle assembly transcript 1 (NEAT1) contributes to cancer growth through its effects on cell proliferation, migration, invasion and drug resistance. However, the exact regulatory mechanisms via which NEAT1 acts in glioma are unclear. In the present study, the expression levels and function of NEAT1 in glioma tissues and cell lines were examined in vitro and in vivo. By reverse transcription‑quantitative PCR and fluorescence in situ hybridization analysis, NEAT1 expression was upregulated in glioma tissues compared with in adjacent normal brain tissues, and elevated NEAT1 levels were associated with poor prognosis. Cell Counting Kit‑8, colony formation, ethynyldeoxyuridine, flow cytometry and western blotting assays were performed to detect the effects of NEAT1 on cell biological behavior. Knockdown of NEAT1 in glioma cell lines was associated with cell cycle arrest at the G₀/G₁ phase, decreased proliferation and elevated apoptosis in vitro, and resulted in reduced tumor growth and increased survival in a mouse xenograft model of glioma. Using bioinformatics analysis, RNA immunoprecipitation experiments and luciferase reporter assays, it was demonstrated that NEAT1 may competitively bind to microRNA (miR)‑324‑5p, thus blocking its interaction with target mRNAs. Potassium channel tetramerization protein domain containing 20 (KCTD20) was identified as a specific miR‑324‑5p target. Accordingly, the inhibition of NEAT1 resulted in the downregulation of KCTD20 through competitive binding with miR‑324‑5p, decreased cell proliferation and increased apoptosis. Concomitant NEAT1 knockdown and inhibition of miR‑324‑5p partially reversed the effects of NEAT1 knockdown on cell proliferation and apoptosis, and further regulated KCTD20 expression. Collectively, the present findings demonstrated that NEAT1 acted as a competing endogenous RNA for miR‑324‑5p, and identified the NEAT1/miR‑324‑5p/KCTD20 axis as a novel regulatory axis and a potential therapeutic target for human glioma.

ILDMSF: Inferring Associations Between Long Non-Coding RNA and Disease Based on Multi-Similarity Fusion

The dysregulation and mutation of long non-coding RNAs (lncRNAs) have been proved to result in a variety of human diseases. Identifying potential disease-related lncRNAs may benefit disease diagnosis, treatment and prognosis. A number of methods have been proposed to predict the potential lncRNA-disease relationships. However, most of them may give rise to incorrect results due to relying on single similarity measure. This article proposes a novel framework (ILDMSF) by fusing the lncRNA similarities and disease similarities, which are measured by lncRNA-related gene and known lncRNA-disease interaction and disease semantic interaction, and known lncRNA-disease interaction, respectively. Further, the support vector machine is employed to identify the potential lncRNA-disease associations based on the integrated similarity. The leave-one-out cross validation is performed to compare ILDMSF with other state of the art methods. The experimental results demonstrate our method is prospective in exploring potential correlations between lncRNA and disease.

Acetyl oxygen benzoate engeletin ester promotes KLF4 degradation leading to the attenuation of pulmonary fibrosis via inhibiting TGFβ1-smad/p38MAPK-lnc865/lnc556-miR-29b-2-5p-STAT3 signal pathway

Pulmonary fibrosis is a common pulmonary interstitial disease of pathogenesis without effective drugs for treatment. Therefore, discovering new and effective drugs is urgently needed. In the present study, we prepared a novel compound named acetyl oxygen benzoate engeletin ester (AOBEE), investigated its effect on experimental pulmonary fibrosis, and proposed a long non-coding RNA (lncRNA)-mediated mechanism of its action. Bleomycin-induced pulmonary fibrosis in mice exhibited that AOBEE improved forced vital capacity (FVC) and alveolar structure and inhibited α-SMA, vimentin, and collagen expression. TGFβ1-stimulated fibroblast L929 cells showed that AOBEE reduced these fibrotic proteins expression and inhibited the activated-fibroblast proliferation and migration. Whole transcriptome sequencing was performed to screen out lncRNA-lnc865 and lnc556 with high expression under bleomycin treatment, but AOBEE caused a considerable decrease in lnc865 and lnc556. Mechanistic study elucidated that AOBEE alleviated pulmonary fibrosis through lnc865- and lnc556-mediated mechanism, in which both lnc865 and lnc556 sponged miR-29b-2-5p to target signal transducer and activator of transcription 3 (STAT3). Further signal pathway inhibitors and the Cignal Finder 45-pathway reporter array illustrated that the up- and downstream pathways were TGFβ1-smad2/3 and p38MAPK, and Krüppel-like factor 4 (KLF4), respectively. In conclusion, AOBEE promoted KLF4 degradation leading to the attenuation of pulmonary fibrosis by inhibiting TGFβ1-smad/p38MAPK-lnc865/lnc556-miR-29b-2-5p-STAT3 signal pathway. We hope this work will provide valuable information to design new drugs and therapeutic targets of lncRNAs for pulmonary fibrosis treatment.

Computational Analysis Predicts Hundreds of Coding lncRNAs in Zebrafish

Simple Summary

Noncoding RNAs (ncRNAs) regulate a variety of fundamental life processes such as development, physiology, metabolism and circadian rhythmicity. RNA-sequencing (RNA-seq) technology has facilitated the sequencing of the whole transcriptome, thereby capturing and quantifying the dynamism of transcriptome-wide RNA expression profiles. However, much remains unrevealed in the huge noncoding RNA datasets that require further bioinformatic analysis. In this study, we applied six bioinformatic tools to investigate coding potentials of approximately 21,000 lncRNAs. A total of 313 lncRNAs are predicted to be coded by all the six tools. Our findings provide insights into the regulatory roles of lncRNAs and set the stage for the functional investigation of these lncRNAs and their encoded micropeptides.

Abstract

Recent studies have demonstrated that numerous long noncoding RNAs (ncRNAs having more than 200 nucleotide base pairs (lncRNAs)) actually encode functional micropeptides, which likely represents the next regulatory biology frontier. Thus, identification of coding lncRNAs from ever-increasing lncRNA databases would be a bioinformatic challenge. Here we employed the Coding Potential Alignment Tool (CPAT), Coding Potential Calculator 2 (CPC2), LGC web server, Coding-Non-Coding Identifying Tool (CNIT), RNAsamba, and MicroPeptide identification tool (MiPepid) to analyze approximately 21,000 zebrafish lncRNAs and computationally to identify 2730–6676 zebrafish lncRNAs with high coding potentials, including 313 coding lncRNAs predicted by all the six bioinformatic tools. We also compared the sensitivity and specificity of these six bioinformatic tools for identifying lncRNAs with coding potentials and summarized their strengths and weaknesses. These predicted zebrafish coding lncRNAs set the stage for further experimental studies.

LncRNA NOP14-AS1 Promotes Tongue Squamous Cell Carcinoma Progression by Targeting MicroRNA-665/HMGB3 Axis

Purpose

The expression profile, clinical effects, and detailed roles of NOP14 antisense RNA 1 (NOP14-AS1) in tongue squamous cell carcinoma (TSCC) remain ambiguous and need to be further explored. Thus, this work was initiated to offer further solid evidence regarding the expression and roles of NOP14-AS1 in TSCC. Furthermore, additional efforts were exerted to reveal the molecular events by which NOP14-AS1 affects the malignant behaviours of TSCC.

Methods

NOP14-AS1 expression was detected in TSCC tissues and cell lines using quantitative reverse transcription-polymerase chain reaction. Cell Counting Kit-8 assay, flow cytometric analysis, Transwell migration and invasion assays, and xenograft tumor model analysis were performed to assess the malignant biological behaviors of TSCC cells after NOP14-AS1 depletion. Mechanistic studies were performed using bioinformatics analysis, luciferase reporter assay, RNA immunoprecipitation, and rescue experiments.

Results

NOP14-AS1 upregulation was identified in TSCC tissues and cell lines. Patients with TSCC exhibiting a high NOP14-AS1 expression faced shorter overall survival than those with a low NOP14-AS1 expression. Functionally, NOP14-AS1 depletion facilitated apoptosis and impeded cell proliferation, migration, and invasion in TSCC. In vivo, the growth of TSCC cells was hindered by NOP14-AS1 depletion. Mechanically, NOP14-AS1 functioned as a competing endogenous RNA by sponging microRNA-665 (miR-665), thereby overexpressing the target high mobility group box 3 (HMGB3) of miR-665. Lastly, rescue experiments confirmed that the introduction of HMGB3 overexpression plasmid or miR-665 inhibitor could abrogate the inhibition of aggressive phenotypes triggered by NOP14-AS1 knockdown.

Conclusion

NOP14-AS1 executed pro-oncogenic activities in TSCC cells by targeting the miR-665/HMGB3 axis. The NOP14-AS1/miR-665/HMGB pathway may be a valuable prognostic indicator and therapeutic target for preventing TSCC.

Genome-wide scan of long noncoding RNA single nucleotide polymorphisms and pancreatic cancer susceptibility

Pancreatic ductal adenocarcinoma (PDAC) is projected to become the second cancer-related cause of death by 2030. Identifying novel risk factors, including genetic risk loci, could be instrumental in risk stratification and implementation of prevention strategies. Long noncoding RNAs (lncRNAs) are involved in regulation of key biological processes, and the possible role of their genetic variability has been unexplored so far. Combining genome wide association studies and functional data, we investigated the genetic variability in all lncRNAs. We analyzed 9893 PDAC cases and 9969 controls and identified a genome-wide significant association between the rs7046076 SNP and risk of developing PDAC (P = 9.73 × 10^-9 ). This SNP is located in the NONHSAG053086.2 (lnc-SMC2-1) gene and the risk allele is predicted to disrupt the binding of the lncRNA with the micro-RNA (miRNA) hsa-mir-1256 that regulates several genes involved in cell cycle, such as CDKN2B. The CDKN2B region is pleiotropic and its genetic variants have been associated with several human diseases, possibly though an imperfect interaction between lncRNA and miRNA. We present a novel PDAC risk locus, supported by a genome-wide statistical significance and a plausible biological mechanism.

LncExpDB: an expression database of human long non-coding RNAs

Expression profiles of long non-coding RNAs (lncRNAs) across diverse biological conditions provide significant insights into their biological functions, interacting targets as well as transcriptional reliability. However, there lacks a comprehensive resource that systematically characterizes the expression landscape of human lncRNAs by integrating their expression profiles across a wide range of biological conditions. Here, we present LncExpDB (https://bigd.big.ac.cn/lncexpdb), an expression database of human lncRNAs that is devoted to providing comprehensive expression profiles of lncRNA genes, exploring their expression features and capacities, identifying featured genes with potentially important functions, and building interactions with protein-coding genes across various biological contexts/conditions. Based on comprehensive integration and stringent curation, LncExpDB currently houses expression profiles of 101 293 high-quality human lncRNA genes derived from 1977 samples of 337 biological conditions across nine biological contexts. Consequently, LncExpDB estimates lncRNA genes' expression reliability and capacities, identifies 25 191 featured genes, and further obtains 28 443 865 lncRNA-mRNA interactions. Moreover, user-friendly web interfaces enable interactive visualization of expression profiles across various conditions and easy exploration of featured lncRNAs and their interacting partners in specific contexts. Collectively, LncExpDB features comprehensive integration and curation of lncRNA expression profiles and thus will serve as a fundamental resource for functional studies on human lncRNAs.

NONCODEV6: an updated database dedicated to long non-coding RNA annotation in both animals and plants

NONCODE (http://www.noncode.org/) is a comprehensive database of collection and annotation of noncoding RNAs, especially long non-coding RNAs (lncRNAs) in animals. NONCODEV6 is dedicated to providing the full scope of lncRNAs across plants and animals. The number of lncRNAs in NONCODEV6 has increased from 548 640 to 644 510 since the last update in 2017. The number of human lncRNAs has increased from 172 216 to 173 112. The number of mouse lncRNAs increased from 131 697 to 131 974. The number of plant lncRNAs is 94 697. The relationship between lncRNAs in human and cancer were updated with transcriptome sequencing profiles. Three important new features were also introduced in NONCODEV6: (i) updated human lncRNA-disease relationships, especially cancer; (ii) lncRNA annotations with tissue expression profiles and predicted function in five common plants; iii) lncRNAs conservation annotation at transcript level for 23 plant species. NONCODEV6 is accessible through http://www.noncode.org/.

EVLncRNAs 2.0: an updated database of manually curated functional long non-coding RNAs validated by low-throughput experiments

Long non-coding RNAs (lncRNAs) play important functional roles in many diverse biological processes. However, not all expressed lncRNAs are functional. Thus, it is necessary to manually collect all experimentally validated functional lncRNAs (EVlncRNA) with their sequences, structures, and functions annotated in a central database. The first release of such a database (EVLncRNAs) was made using the literature prior to 1 May 2016. Since then (till 15 May 2020), 19 245 articles related to lncRNAs have been published. In EVLncRNAs 2.0, these articles were manually examined for a major expansion of the data collected. Specifically, the number of annotated EVlncRNAs, associated diseases, lncRNA-disease associations, and interaction records were increased by 260%, 320%, 484% and 537%, respectively. Moreover, the database has added several new categories: 8 lncRNA structures, 33 exosomal lncRNAs, 188 circular RNAs, and 1079 drug-resistant, chemoresistant, and stress-resistant lncRNAs. All records have checked against known retraction and fake articles. This release also comes with a highly interactive visual interaction network that facilitates users to track the underlying relations among lncRNAs, miRNAs, proteins, genes and other functional elements. Furthermore, it provides links to four new bioinformatics tools with improved data browsing and searching functionality. EVLncRNAs 2.0 is freely available at https://www.sdklab-biophysics-dzu.net/EVLncRNAs2/.

LncRNAs and Available Databases

Long noncoding RNAs (lncRNAs) are involved in many regulatory mechanisms in practically every step of the RNA cycle, from transcription to RNA stability and translation. They are a highly heterogeneous class of molecules in terms of site of production, interaction networks, and functions. More and more databases are available on the web with the aim to make public information about lncRNA accessible to the scientific community. Here we review the most interesting resources with the purpose to organize a compendium of useful tools to interrogate before studying a lncRNA of interest.

Long Non-Coding RNA 554 Promotes Cardiac Fibrosis via TGF-β1 Pathway in Mice Following Myocardial Infarction

Rationale: Cardiac fibrosis is observed in nearly every form of myocardial disease. Long non-coding RNAs (lncRNAs) have been shown to play an important role in cardiac fibrosis, but the detailed molecular mechanism remains unknown.

Object: We aimed at characterizing lncRNA 554 expression in murine cardiac fibroblasts (CFs) after myocardial infarction (MI) to identify CF-enriched lncRNA and investigate its function and contribution to cardiac fibrosis and function.

Methods and Results: In this study, we identified lncRNA NONMMUT022554 (lncRNA 554) as a regulator of MI-induced cardiac fibrosis. We found that lncRNA 554 was significantly up-regulated in the mouse hearts following MI. Further study showed that lncRNA 554 was predominantly expressed in cardiac fibroblasts, indicating a potential role of lncRNA 554 in cardiac fibrosis. In vitro knockdown of lncRNA 554 by siRNA suppressed fibroblasts migration and expression of extracellular matrix (ECM); while overexpression of lncRNA 554 promoted expression of ECM genes. Consistently, lentivirus mediated in vivo knockdown of lncRNA 554 could inhibit cardiac fibrosis and improve cardiac function in mouse model of MI. More importantly, TGF-β1 inhibitor (TEW-7197) could reverse the pro-fibrotic function of lncRNA 554 in CFs. This suggests that the effects of lncRNA 554 on cardiac fibrosis is TGF-β1 dependent.

Conclusion: Collectively, our study illustrated the role of lncRNA 554 in cardiac fibrosis, suggested that lncRNA 554 might be a novel target for cardiac fibrosis.

Association of Long Non-Coding RNA Polymorphisms with Gastric Cancer and Atrophic Gastritis

Long non-coding RNAs (lncRNA) play an important role in the carcinogenesis of various tumours, including gastric cancer. This study aimed to assess the associations of lncRNA ANRIL, H19, MALAT1, MEG3, HOTAIR single-nucleotide polymorphisms (SNPs) with gastric cancer and atrophic gastritis. SNPs were analyzed in 613 gastric cancer patients, 118 patients with atrophic gastritis and 476 controls from three tertiary centers in Germany, Lithuania and Latvia. Genomic DNA was extracted from peripheral blood leukocytes. SNPs were genotyped by the real-time polymerase chain reaction. Results showed that carriers of MALAT1 rs3200401 CT genotype had the significantly higher odds of atrophic gastritis than those with CC genotype (OR-1.81; 95% CI 1.17–2.80, p = 0.0066). Higher odds of AG were found in a recessive model (CC vs. TT + CT) for ANRIL rs1333045 (OR-1.88; 95% CI 1.19–2.95, p = 0.0066). Carriers of ANRIL (rs17694493) GG genotype had higher odds of gastric cancer (OR-4.93; 95% CI 1.28–19.00) and atrophic gastritis (OR-5.11; 95% CI 1.10–23.80) compared with the CC genotype, and carriers of HOTAIR rs17840857 TG genotype had higher odds of atrophic gastritis (OR-1.61 95% CI 1.04–2.50) compared with the TT genotype; however, the ORs did not reach the adjusted significance threshold (p < 0.007). In summary, our data provide novel evidence for a possible link between lncRNA SNPs and premalignant condition of gastric cancer, suggesting the involvement of lncRNAs in gastric cancer development.

Integrated Analysis of an lncRNA-Associated ceRNA Network Reveals Potential Biomarkers for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common malignant tumor worldwide. In this study, we aimed to explore the potential biomarkers and key regulatory pathways related to HCC using integrated bioinformatic analysis and validation. The microarray data of GSE12717 and GSE54238 were downloaded from the Gene Expression Omnibus database. A competing endogenous RNA (ceRNA) network was constructed based on potential long-noncoding RNA (lncRNA)-microRNA (miRNA)-mRNA interactions. A total of 191 mRNAs, 8 miRNAs, and 5 lncRNAs were selected to construct the ceRNA network. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were used to predict their biological functions. The PI3K-Akt signaling pathway was significantly enriched. Kaplan-Meier survival analysis based on the Gene Expression Profiling Interactive Analysis (GEPIA) database was conducted for the weighted mRNAs and lncRNAs. The results showed that SRC, GMPS, CDK2, FEN1, EZH2, ZWINT, MTHFD1L, GINS2, and MAPKAPK5-AS1 were significantly upregulated in tumor tissues. The relative expression levels of these genes were significantly upregulated in HCC patients based on the StarBase database. For further validation, the expression levels of these genes were detected by real-time quantitative reverse transcription-polymerase chain reaction in 20 HCC tumor tissues and paired paracancerous tissues. Receiver operating characteristic analysis revealed that CDK2, MTHFD1L, SRC, ZWINT, and MAPKAPK5-AS1 had significant diagnostic value in HCC, but further studies are needed to explore their mechanisms in HCC.

Long noncoding RNA FGF14-AS2 inhibits breast cancer metastasis by regulating the miR-370-3p/FGF14 axis

Long noncoding RNAs (lncRNAs) have emerged as important regulators in cancers, including breast cancer. However, the overall biological roles and clinical significance of most lncRNAs are not fully understood. This study aimed to elucidate the potential role of a novel lncRNA FGF14-AS2 and the mechanisms underlying metastasis in breast cancer. The lncRNA FGF14-AS2 was significantly downregulated in breast cancer tissues; patients with lower FGF14-AS2 expression had advanced clinical stage. In vitro and in vivo assays of FGF14-AS2 alterations revealed a complex integrated phenotype affecting breast cancer cell migration, invasion, and tumor metastasis. Mechanistically, FGF14-AS2 functioned as a competing endogenous RNA of miR-370-3p, thereby leading to the activation of its coding counterpart, FGF14. Clinically, we observed increased miR-370-3p expression in breast cancer tissues, whereas FGF14 expression was decreased in breast cancer tissues compared to the adjacent normal breast tissues. FGF14-AS2 expression was significantly negatively correlated with miR-370-3p expression, and correlated positively to FGF14 expression. Collectively, our findings support a model in which the FGF14-AS2/miR-370-3p/FGF14 axis is a critical regulator in breast cancer metastasis, suggesting a new therapeutic direction in breast cancer.