NRED: a database of long noncoding RNA expression

A novel end-to-end learning framework for inferring lncRNA-disease associations based on convolution neural network

Introduction

In recent years, lots of computational models have been proposed to infer potential lncRNA-disease associations.

Methods

In this manuscript, we introduced a novel end-to-end learning framework named CNMCLDA, in which, we first adopted two convolutional neural networks to extract hidden features of diseases and lncRNAs separately. And then, by combining these hidden features of diseases and lncRNAs with known lncRNA-disease associations, we designed five different loss functions. Next, based on errors obtained by these loss functions, we would perform back propagation to fit parameters in CNMCLDA, and complete those missing values in lncRNA-disease relational matrix according to these fitted parameters. In order to demonstrate the prediction performance of CNMCLDA, intensive experiments have been carried out and experimental results show that CNMCLDA can achieve better performances than state-of-the-art competitive predictive models in frameworks of five-fold cross validation, ten-fold cross validation and leave-one-disease-out cross validation respectively.

Results and Discussion

Moreover, in case studies of gastric cancer, glioma and breast cancer, there are 19, 17 and 16 out of top 20 candidate lncRNAs inferred by CNMCLDA having been confirmed by recent relevant literatures separately, which demonstrated the outstanding performance of CNMCLDA as well. Hence, it is obvious that CNMCLDA may be an effective tool for prediction of potential lncRNA-disease associations in the future.

Computational Prediction of Gene Regulation by lncRNAs

High-throughput sequencing technologies and innovative bioinformatics tools discovered that most of the genome is transcribed into RNA. However, only a fraction of the RNAs in cell translates into proteins, while the majority of them are categorized as noncoding RNAs (ncRNAs). The ncRNAs with more than 200 nt without protein-coding ability are termed long noncoding RNAs (lncRNAs). Hundreds of studies established that lncRNAs are a crucial RNA family regulating gene expression. Regulatory RNAs, including lncRNAs, modulate gene expression by interacting with RNA, DNA, and proteins. Several databases and computational tools have been developed to explore the functions of lncRNAs in cellular physiology. This chapter discusses the tools available for lncRNA functional analysis and provides a detailed workflow for the computational analysis of lncRNAs.

Computational Resources for lncRNA Functions and Targetome

Long non-coding RNAs (lncRNAs) are a type of non-coding RNA molecules exceeding 200 nucleotides in length and that do not encode proteins. The dysregulated expression of lncRNAs has been identified in various diseases, holding therapeutic significance. Over the past decade, numerous computational resources have been published in the field of lncRNA. In this chapter, we have provided a comprehensive review of the databases as well as predictive tools, that is, lncRNA databases, machine learning based algorithms, and tools predicting lncRNAs utilizing different techniques. The chapter will focus on the importance of lncRNA resources developed for different organisms specifically for humans, mouse, plants, and other model organisms. We have enlisted important databases, primarily focusing on comprehensive information related to lncRNA registries, associations with diseases, differential expression, lncRNA transcriptome, target regulations, and all-in-one resources. Further, we have also included the updated version of lncRNA resources. Additionally, computational identification of lncRNAs using algorithms like Deep learning, Support Vector Machine (SVM), and Random Forest (RF) was also discussed. In conclusion, this comprehensive overview concludes by summarizing vital in silico resources, empowering biologists to choose the most suitable tools for their lncRNA research endeavors. This chapter serves as a valuable guide, emphasizing the significance of computational approaches in understanding lncRNAs and their implications in various biological contexts.

Decoding the Non-coding: Tools and Databases Unveiling the Hidden World of "Junk" RNAs for Innovative Therapeutic Exploration

Non-coding RNAs are pivotal regulators of gene and protein expression, exerting crucial influences on diverse biological processes. Their dysregulation is frequently implicated in the onset and progression of diseases, notably cancer. A profound comprehension of the intricate mechanisms governing ncRNAs is imperative for devising innovative therapeutic interventions against these debilitating conditions. Significantly, nearly 80% of our genome comprises ncRNAs, underscoring their centrality in cellular processes. The elucidation of ncRNA functions is pivotal for grasping the complexities of gene regulation and its implications for human health. Modern genome sequencing techniques yield vast datasets, stored in specialized databases. To harness this wealth of information and to understand the crosstalk of non-coding RNAs, knowledge of available databases is required, and many new sophisticated computational tools have emerged. These tools play a pivotal role in the identification, prediction, and annotation of ncRNAs, thereby facilitating their experimental validation. This Review succinctly outlines the current understanding of ncRNAs, emphasizing their involvement in disease development. It also highlights the databases and tools instrumental in classifying, annotating, and evaluating ncRNAs. By extracting meaningful biological insights from seemingly "junk" data, these tools empower scientists to unravel the intricate roles of ncRNAs in shaping human health.

Finding potential lncRNA-disease associations using a boosting-based ensemble learning model

Introduction: Long non-coding RNAs (lncRNAs) have been in the clinical use as potential prognostic biomarkers of various types of cancer. Identifying associations between lncRNAs and diseases helps capture the potential biomarkers and design efficient therapeutic options for diseases. Wet experiments for identifying these associations are costly and laborious. Methods: We developed LDA-SABC, a novel boosting-based framework for lncRNA-disease association (LDA) prediction. LDA-SABC extracts LDA features based on singular value decomposition (SVD) and classifies lncRNA-disease pairs (LDPs) by incorporating LightGBM and AdaBoost into the convolutional neural network. Results: The LDA-SABC performance was evaluated under five-fold cross validations (CVs) on lncRNAs, diseases, and LDPs. It obviously outperformed four other classical LDA inference methods (SDLDA, LDNFSGB, LDASR, and IPCAF) through precision, recall, accuracy, F1 score, AUC, and AUPR. Based on the accurate LDA prediction performance of LDA-SABC, we used it to find potential lncRNA biomarkers for lung cancer. The results elucidated that 7SK and HULC could have a relationship with non-small-cell lung cancer (NSCLC) and lung adenocarcinoma (LUAD), respectively. Conclusion: We hope that our proposed LDA-SABC method can help improve the LDA identification.

Long noncoding RNA from Betula platyphylla, BplncSIR1, confers salt tolerance by regulating BpNAC2 to mediate reactive oxygen species scavenging and stomatal movement

Long noncoding RNAs (lncRNAs) play an important role in abiotic stress tolerance. However, their function in conferring abiotic stress tolerance is still unclear. Herein, we characterized the function of a salt-responsive nuclear lncRNA (BplncSIR1) from Betula platyphylla (birch). Birch plants overexpressing and knocking out for BplncSIR1 were generated. BplncSIR1 was found to improve salt tolerance by inducing antioxidant activity and stomatal closure, and also accelerate plant growth. Chromatin isolation by RNA purification (ChIRP) combined with RNA sequencing indicated that BplncSIR1 binds to the promoter of BpNAC2 (encoding NAC domain-containing protein 2) to activate its expression. Plants overexpressing and knocking out for BpNAC2 were generated. Consistent with that of BplncSIR1, overexpression of BpNAC2 also accelerated plant growth and conferred salt tolerance. In addition, BpNAC2 binds to different cis-acting elements, such as G-box and 'CCAAT' sequences, to regulate the genes involved in salt tolerance, resulting in reduced ROS accumulation and decreased water loss rate by stomatal closure. Taken together, BplncSIR1 serves as the regulator of BpNAC2 to induce its expression in response to salt stress, and activated BpNAC2 accelerates plant growth and improves salt tolerance. Therefore, BplncSIR1 might be a candidate gene for molecular breeding to cultivate plants with both a high growth rate and improved salt tolerance.

LDA-VGHB: identifying potential lncRNA-disease associations with singular value decomposition, variational graph auto-encoder and heterogeneous Newton boosting machine

Long noncoding RNAs (lncRNAs) participate in various biological processes and have close linkages with diseases. In vivo and in vitro experiments have validated many associations between lncRNAs and diseases. However, biological experiments are time-consuming and expensive. Here, we introduce LDA-VGHB, an lncRNA-disease association (LDA) identification framework, by incorporating feature extraction based on singular value decomposition and variational graph autoencoder and LDA classification based on heterogeneous Newton boosting machine. LDA-VGHB was compared with four classical LDA prediction methods (i.e. SDLDA, LDNFSGB, IPCARF and LDASR) and four popular boosting models (XGBoost, AdaBoost, CatBoost and LightGBM) under 5-fold cross-validations on lncRNAs, diseases, lncRNA-disease pairs and independent lncRNAs and independent diseases, respectively. It greatly outperformed the other methods with its prominent performance under four different cross-validations on the lncRNADisease and MNDR databases. We further investigated potential lncRNAs for lung cancer, breast cancer, colorectal cancer and kidney neoplasms and inferred the top 20 lncRNAs associated with them among all their unobserved lncRNAs. The results showed that most of the predicted top 20 lncRNAs have been verified by biomedical experiments provided by the Lnc2Cancer 3.0, lncRNADisease v2.0 and RNADisease databases as well as publications. We found that HAR1A, KCNQ1DN, ZFAT-AS1 and HAR1B could associate with lung cancer, breast cancer, colorectal cancer and kidney neoplasms, respectively. The results need further biological experimental validation. We foresee that LDA-VGHB was capable of identifying possible lncRNAs for complex diseases. LDA-VGHB is publicly available at https://github.com/plhhnu/LDA-VGHB.

The lncRNA HOXA11os regulates mitochondrial function in myeloid cells to maintain intestinal homeostasis

This study reveals a previously uncharacterized mechanism to restrict intestinal inflammation via a regulatory RNA transcribed from a noncoding genomic locus. We identified a novel transcript of the lncRNA HOXA11os specifically expressed in the distal colon that is reduced to undetectable levels in colitis. HOXA11os is localized to mitochondria under basal conditions and interacts with a core subunit of complex 1 of the electron transport chain (ETC) to maintain its activity. Deficiency of HOXA11os in colonic myeloid cells results in complex I deficiency, dysfunctional oxidative phosphorylation (OXPHOS), and the production of mitochondrial reactive oxygen species (mtROS). As a result, HOXA11os-deficient mice develop spontaneous intestinal inflammation and are hypersusceptible to colitis. Collectively, these studies identify a new regulatory axis whereby a lncRNA maintains intestinal homeostasis and restricts inflammation in the colon through the regulation of complex I activity.

BRWMC: Predicting lncRNA-disease associations based on bi-random walk and matrix completion on disease and lncRNA networks

Many experiments have proved that long non-coding RNAs (lncRNAs) in humans have been implicated in disease development. The prediction of lncRNA-disease association is essential in promoting disease treatment and drug development. It is time-consuming and laborious to explore the relationship between lncRNA and diseases in the laboratory. The computation-based approach has clear advantages and has become a promising research direction. This paper proposes a new lncRNA disease association prediction algorithm BRWMC. Firstly, BRWMC constructed several lncRNA (disease) similarity networks based on different measurement angles and fused them into an integrated similarity network by similarity network fusion (SNF). In addition, the random walk method is used to preprocess the known lncRNA-disease association matrix and calculate the estimated scores of potential lncRNA-disease associations. Finally, the matrix completion method accurately predicts the potential lncRNA-disease associations. Under the framework of leave-one-out cross-validation and 5-fold cross-validation, the AUC values obtained by BRWMC are 0.9610 and 0.9739, respectively. In addition, case studies of three common diseases show that BRWMC is a reliable method for prediction.

RNA out of the mist

RNA has long been regarded primarily as the intermediate between genes and proteins. It was a surprise then to discover that eukaryotic genes are mosaics of mRNA sequences interrupted by large tracts of transcribed but untranslated sequences, and that multicellular organisms also express many long 'intergenic' and antisense noncoding RNAs (lncRNAs). The identification of small RNAs that regulate mRNA translation and half-life did not disturb the prevailing view that animals and plant genomes are full of evolutionary debris and that their development is mainly supervised by transcription factors. Gathering evidence to the contrary involved addressing the low conservation, expression, and genetic visibility of lncRNAs, demonstrating their cell-specific roles in cell and developmental biology, and their association with chromatin-modifying complexes and phase-separated domains. The emerging picture is that most lncRNAs are the products of genetic loci termed 'enhancers', which marshal generic effector proteins to their sites of action to control cell fate decisions during development.

Non-coding RNAs as key players in the neurodegenerative diseases: Multi-platform strategies and approaches for exploring the Genome's dark matter

A growing amount of evidence in the last few years has begun to unravel that non-coding RNAs have a myriad of functions in gene regulation. Intensive investigation on non-coding RNAs (ncRNAs) has led to exploring their broad role in neurodegenerative diseases (NDs) owing to their regulatory role in gene expression. RNA sequencing technologies and transcriptome analysis has unveiled significant dysregulation of ncRNAs attributed to their biogenesis, upregulation, downregulation, aberrant epigenetic regulation, and abnormal transcription. Despite these advances, the understanding of their potential as therapeutic targets and biomarkers underpinning detailed mechanisms is still unknown. Advancements in bioinformatics and molecular technologies have improved our knowledge of the dark matter of the genome in terms of recognition and functional validation. This review aims to shed light on ncRNAs biogenesis, function, and potential role in NDs. Further deepening of their role is provided through a focus on the most recent platforms, experimental approaches, and computational analysis to investigate ncRNAs. Furthermore, this review summarizes and evaluates well-studied miRNAs, lncRNAs and circRNAs concerning their potential role in pathogenesis and use as biomarkers in NDs. Finally, a perspective on the main challenges and novel methods for the future and broad therapeutic use of ncRNAs is offered.

Particulate Matters Affecting lncRNA Dysregulation and Glioblastoma Invasiveness: In Silico Applications and Current Insights

With a reported rise in global air pollution, more than 50% of the population remains exposed to toxic air pollutants in the form of particulate matters (PMs). PMs, from various sources and of varying sizes, have a significant impact on health as long-time exposure to them has seen a correlation with various health hazards and have also been determined to be carcinogenic. In addition to disrupting known cellular pathways, PMs have also been associated with lncRNA dysregulation-a factor that increases predisposition towards the onset or progression of cancer. lncRNA dysregulation is further seen to mediate glioblastoma multiforme (GBM) progression. The vast array of information regarding cancer types including GBM and its various precursors can easily be obtained via innovative in silico approaches in the form of databases such as GEO and TCGA; however, a need to obtain selective and specific information correlating anthropogenic factors and disease progression-in the case of GBM-can serve as a critical tool to filter down and target specific PMs and lncRNAs responsible for regulating key cancer hallmarks in glioblastoma. The current review article proposes an in silico approach in the form of a database that reviews current updates on correlation of PMs with lncRNA dysregulation leading to GBM progression.

Heterogeneous graph neural network for lncRNA-disease association prediction

Identifying lncRNA-disease associations is conducive to the diagnosis, treatment and prevention of diseases. Due to the expensive and time-consuming methods verified by biological experiments, prediction methods based on computational models have gradually become an important means of lncRNA-disease associations discovery. However, existing methods still have challenges to make full use of network topology information to identify potential associations between lncRNA and disease in multi-source data. In this study, we propose a novel method called HGNNLDA for lncRNA-disease association prediction. First, HGNNLDA constructs a heterogeneous network composed of lncRNA similarity network, lncRNA-disease association network and lncRNA-miRNA association network; Then, on this heterogeneous network, various types of strong correlation neighbors with fixed size are sampled for each node by restart random walk; Next, the embedding information of lncRNA and disease in each lncRNA-disease association pair is obtained by the method of type-based neighbor aggregation and all types combination though heterogeneous graph neural network, in which attention mechanism is introduced considering that different types of neighbors will make different contributions to the prediction of lncRNA-disease association. As a result, the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUPR) under fivefold cross-validation (5FCV) are 0.9786 and 0.8891, respectively. Compared with five state-of-art prediction models, HGNNLDA has better prediction performance. In addition, in two types of case studies, it is further verified that our method can effectively predict the potential lncRNA-disease associations, and have ability to predict new diseases without any known lncRNAs.

PpTCP18 is upregulated by lncRNA5 and controls branch number in peach (Prunus persica) through positive feedback regulation of strigolactone biosynthesis

Branch number is an important agronomic trait in peach (Prunus persica) trees because plant architecture affects fruit yield and quality. Although breeders can select varieties with different tree architecture, the biological mechanisms underlying architecture remain largely unclear. In this study, a pillar peach ('Zhaoshouhong') and a standard peach ('Okubo') were compared. 'Zhaoshouhong' was found to have significantly fewer secondary branches than 'Okubo'. Treatment with the synthetic strigolactone (SL) GR24 decreased branch number. Transcriptome analysis indicated that PpTCP18 (a homologous gene of Arabidopsis thaliana BRC1) expression was negatively correlated with strigolactone synthesis gene expression, indicating that PpTCP18 may play an important role in peach branching. Yeast one-hybrid, electrophoretic mobility shift, dual-luciferase assays and PpTCP18-knockdown in peach leaf buds indicated that PpTCP18 could increase expression of PpLBO1, PpMAX1, and PpMAX4. Furthermore, transgenic Arabidopsis plants overexpressing PpTCP18 clearly exhibited reduced primary rosette-leaf branches. Moreover, lncRNA sequencing and transient expression analysis revealed that lncRNA5 targeted PpTCP18, significantly increasing PpTCP18 expression. These results provide insights into the mRNA and lncRNA network in the peach SL signaling pathway and indicate that PpTCP18, a transcription factor downstream of SL signaling, is involved in positive feedback regulation of SL biosynthesis. This role of PpTCP18 may represent a novel mechanism in peach branching regulation. Our study improves current understanding of the mechanisms underlying peach branching and provides theoretical support for genetic improvement of peach tree architecture.

B, Iquebal MA, Bhattacharya RC, Rai A, Kumar D. Genome wide identification of lncRNAs and circRNAs having regulatory role in fruit shelf life in health crop cucumber (Cucumis sativus L.)

Cucumber is an extremely perishable vegetable; however, under room conditions, the fruits become unfit for consumption 2-3 days after harvesting. One natural variant, DC-48 with an extended shelf-life was identified, fruits of which can be stored up to 10-15 days under room temperature. The genes involved in this economically important trait are regulated by non-coding RNAs. The study aims to identify the long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) by taking two contrasting genotypes, DC-48 and DC-83, at two different fruit developmental stages. The upper epidermis of the fruits was collected at 5 days and 10 days after pollination (DAP) for high throughput RNA sequencing. The differential expression analysis was performed to identify differentially expressed (DE) lncRNAs and circRNAs along with the network analysis of lncRNA, miRNA, circRNA, and mRNA interactions. A total of 97 DElncRNAs were identified where 18 were common under both the developmental stages (8 down regulated and 10 upregulated). Based on the back-spliced reads, 238 circRNAs were found to be distributed uniformly throughout the cucumber genomes with the highest numbers (71) in chromosome 4. The majority of the circRNAs (49%) were exonic in origin followed by inter-genic (47%) and intronic (4%) origin. The genes related to fruit firmness, namely, polygalacturonase, expansin, pectate lyase, and xyloglucan glycosyltransferase were present in the target sites and co-localized networks indicating the role of the lncRNA and circRNAs in their regulation. Genes related to fruit ripening, namely, trehalose-6-phosphate synthase, squamosa promoter binding protein, WRKY domain transcription factors, MADS box proteins, abscisic stress ripening inhibitors, and different classes of heat shock proteins (HSPs) were also found to be regulated by the identified lncRNA and circRNAs. Besides, ethylene biosynthesis and chlorophyll metabolisms were also found to be regulated by DElncRNAs and circRNAs. A total of 17 transcripts were also successfully validated through RT PCR data. These results would help the breeders to identify the complex molecular network and regulatory role of the lncRNAs and circRNAs in determining the shelf-life of cucumbers.

dbEssLnc: A manually curated database of human and mouse essential lncRNA genes

Long non-coding RNAs (lncRNAs) play important roles in many biological processes. Knocking out or knocking down some lncRNAs will lead to lethality or infertility. These lncRNAs are called essential lncRNAs. Knowledges of essential lncRNAs are important in establishing minimal genomes of living cells, developing drug therapies and early diagnostic approaches for complex diseases. However, existing databases focus on collecting essential coding genes. Essential non-coding gene records are rare in existing databases. A comprehensive collection of essential non-coding genes, particularly essential lncRNA genes, is demanded. We manually curated 207 essential lncRNAs from literatures for establishing a database on essential lncRNAs, which is named as dbEssLnc (Database of essential lncRNAs). The dbEssLnc database has a web-based user-friendly interface for the users to browse, to search, to visualize and to blast search records in the database. The dbEssLnc database is freely accessible at https://esslnc.pufengdu.org. All data and source codes for mirroring the dbEssLnc database have been deposited in GitHub (https://github.com/yyZhang14/dbEssLnc).

The Genome-Wide Identification of Long Non-Coding RNAs Involved in Floral Thermogenesis in Nelumbo nucifera Gaertn

The sacred lotus (Nelumbo nucifera Gaertn.) can maintain a stable floral chamber temperature when blooming, despite ambient temperature fluctuations; however, the long non-coding RNAs (lncRNAs) involved in floral thermogenesis remain unclear. In the present study, we obtain comprehensive lncRNAs expression profiles from receptacles at five developmental stages by strand-specific RNA sequencing to reveal the lncRNAs regulatory mechanism of the floral thermogenesis of N. nucifera. A total of 22,693 transcripts were identified as lncRNAs, of which approximately 44.78% had stage-specific expression patterns. Subsequently, we identified 2579 differential expressed lncRNAs (DELs) regulating 2367 protein-coding genes mainly involved in receptacle development and reproductive process. Then, lncRNAs with floral thermogenesis identified by weighted gene co-expression network analysis (WGCNA) were mainly related to sulfur metabolism and mitochondrial electron transport chains. Meanwhile, 70 lncRNAs were predicted to act as endogenous target mimics (eTMs) for 29 miRNAs and participate in the regulation of 16 floral thermogenesis-related genes. Our dual luciferase reporter assays indicated that lncRNA LTCONS_00068702 acted as eTMs for miR164a_4 to regulate the expression of TrxL2 gene. These results deepen our understanding of the regulation mechanism of floral thermogenesis by lncRNAs and accumulate data for further research.

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

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

Importance of the long non-coding RNA (lncRNA) transcript HULC for the regulation of phenylalanine hydroxylase and treatment of phenylketonuria

More than 1280 variants in the phenylalanine hydroxylase (PAH) gene are responsible for a broad spectrum of phenylketonuria (PKU) phenotypes. While the genotype-phenotype correlation is reaching 88%, for some inconsistent phenotypes with the same genotype additional factors like tetrahydrobiopterin (BH₄), the PAH co-chaperone DNAJC12, phosphorylation of the PAH residues or epigenetic factors may play an important role. Very recently an additional player, the long non-coding RNA (lncRNA) transcript HULC, was described to regulate PAH activity and enhance residual enzyme activity of some PAH variants (e.g., the most common p.R408W) by using HULC mimics. In this review we present an overview of the lncRNA function and in particular the interplay of the HUCL transcript with the PAH and discuss potential applications for the future treatment of some PKU patients.

LDA-LNSUBRW: lncRNA-Disease Association Prediction Based on Linear Neighborhood Similarity and Unbalanced bi-Random Walk

Increasing number of experiments show that lncRNAs are involved in many biological processes, and their mutations and disorders are associated with many diseases. However, verifying the relationships between lncRNAs and diseases is time consuming and laborio. Searching for effective computational methods will contribute to our understanding of the underlying mechanisms of disease and identifying biomarkers of diseases. Therefore, we proposed a method called lncRNA-disease association prediction based on linear neighborhood similarity and unbalanced bi-random walk (LDA-LNSUBRW). Given that the known lncRNA-disease associations are rare, a pretreatment step should be performed to obtain the interaction possibility of unknown cases, so as to help us predict the potential associations. In the framework of leave-one-out cross-validation (LOOCV)and fivefold cross-validation (5-fold CV), LDA-LNSUBRW achieved effective performance with AUC of 0.8874 and 0.8632 ± 0.0051, respectively. The experimental results in this paper show that the proposed method is superior to five other state-of-the-art methods. In addition, case studies of three diseases (lung cancer, breast cancer, and osteosarcoma)were carried out to illustrate that LDA-LNSUBRW could predict the relevant lncRNAs.

Long Non-Coding RNAs as Emerging Regulators of Pathogen Response in Plants

Long non-coding RNAs (lncRNAs) are transcripts without protein-coding potential that contain more than 200 nucleotides that play important roles in plant survival in response to different stresses. They interact with molecules such as DNA, RNA, and protein, and play roles in the regulation of chromatin remodeling, RNA metabolism, and protein modification activities. These lncRNAs regulate the expression of their downstream targets through epigenetic changes, at the level of transcription and post-transcription. Emerging information from computational biology and functional characterization of some of them has revealed their diverse mechanisms of action and possible roles in biological processes such as flowering time, reproductive organ development, as well as biotic and abiotic stress responses. In this review, we have mainly focused on the role of lncRNAs in biotic stress response due to the limited availability of knowledge in this domain. We have discussed the available molecular mechanisms of certain known lncRNAs against specific pathogens. Further, considering that fungal, viral, and bacterial diseases are major factors in the global food crisis, we have highlighted the importance of lncRNAs against pathogen responses and the progress in plant research to develop a better understanding of their functions and molecular mechanisms.

Bioinformatics Analysis of Long Non-coding RNA and Related Diseases: An Overview

Long non-coding RNAs (lncRNAs) are usually located in the nucleus and cytoplasm of cells. The transcripts of lncRNAs are >200 nucleotides in length and do not encode proteins. Compared with small RNAs, lncRNAs have longer sequences, more complex spatial structures, and more diverse and complex mechanisms involved in the regulation of gene expression. LncRNAs are widely involved in the biological processes of cells, and in the occurrence and development of many human diseases. Many studies have shown that lncRNAs can induce the occurrence of diseases, and some lncRNAs undergo specific changes in tumor cells. Research into the roles of lncRNAs has covered the diagnosis of, for example, cardiovascular, cerebrovascular, and central nervous system diseases. The bioinformatics of lncRNAs has gradually become a research hotspot and has led to the discovery of a large number of lncRNAs and associated biological functions, and lncRNA databases and recognition models have been developed. In this review, the research progress of lncRNAs is discussed, and lncRNA-related databases and the mechanisms and modes of action of lncRNAs are described. In addition, disease-related lncRNA methods and the relationships between lncRNAs and human lung adenocarcinoma, rectal cancer, colon cancer, heart disease, and diabetes are discussed. Finally, the significance and existing problems of lncRNA research are considered.

LINC00857 promotes colorectal cancer progression by sponging miR-150-5p and upregulating HMGB3 (high mobility group box 3) expression

Colorectal cancer (CRC) is the third most commonly diagnosed malignant tumor worldwide. LINC00857 has been reported as a dysregulated long non-coding RNAs (lncRNAs) involved in the genesis and development of different cancers. In CRC, accumulating evidence indicates that high mobility group box 3 (HMGB3) is over-expressed and contributes to CRC development. However, the mechanism underlying HMGB3 upregulation in CRC remains unclear. The present work aims to investigate the role of LINC00857 and its functional interaction with HMGB3 in regulating CRC progression. Differential expression of LINC00857 between CRC tissues and normal tissues was identified in TCGA (The Cancer Genome Atlas) database. In vitro functional assays were performed to explore the biological functions of LINC00857 in CRC cells. In vivo xenograft model was employed to investigate the role of LINC00857 in CRC tumorigenesis. We found that LINC00857 was significant upregulated in CRC tissues and cell lines. LINC00857 knockdown significantly inhibited the proliferation, migration and invasion of CRC cells, and also induced apoptosis. Moreover, LINC00857 knockdown suppressed CRC tumorigenesis in vivo. We further demonstrated that the effects of LINC00857 in CRC cells were mediated through miR-150-5p/HMGB3 axis. LINC00857 negatively regulates the activity of miR-150-5p, which releases its inhibition on HMGB3 expression. Our data indicate that LINC00857/miR-150-5p/HMGB3 axis plays a fundamental role in regulating the malignant phenotype and tumorigenesis of CRC. Targeting this axis may serve as novel therapeutic strategies for CRC treatment.

Fusion of KATZ measure and space projection to fast probe potential lncRNA-disease associations in bipartite graphs

It is well known that numerous long noncoding RNAs (lncRNAs) closely relate to the physiological and pathological processes of human diseases and can serves as potential biomarkers. Therefore, lncRNA-disease associations that are identified by computational methods as the targeted candidates reduce the cost of biological experiments focusing on deep study furtherly. However, inaccurate construction of similarity networks and inadequate numbers of observed known lncRNA–disease associations, such inherent problems make many mature computational methods that have been developed for many years still exit some limitations. It motivates us to explore a new computational method that was fused with KATZ measure and space projection to fast probing potential lncRNA-disease associations (namely KATZSP). KATZSP is comprised of following key steps: combining all the global information with which to change Boolean network of known lncRNA–disease associations into the weighted networks; changing the similarities calculation into counting the number of walks that connect lncRNA nodes and disease nodes in bipartite graphs; obtaining the space projection scores to refine the primary prediction scores. The process to fuse KATZ measure and space projection was simplified and uncomplicated with needing only one attenuation factor. The leave-one-out cross validation (LOOCV) experimental results showed that, compared with other state-of-the-art methods (NCPLDA, LDAI-ISPS and IIRWR), KATZSP had a higher predictive accuracy shown with area-under-the-curve (AUC) value on the three datasets built, while KATZSP well worked on inferring potential associations related to new lncRNAs (or isolated diseases). The results from real cases study (such as pancreas cancer, lung cancer and colorectal cancer) further confirmed that KATZSP is capable of superior predictive ability to be applied as a guide for traditional biological experiments.

(In)Distinctive Role of Long Non-Coding RNAs in Common and Rare Ovarian Cancers

Rare ovarian cancers (ROCs) are OCs with an annual incidence of fewer than 6 cases per 100,000 women. They affect women of all ages, but due to their low incidence and the potential clinical inexperience in management, there can be a delay in diagnosis, leading to a poor prognosis. The underlying causes for these tumors are varied, but generally, the tumors arise due to alterations in gene/protein expression in cellular processes that regulate normal proliferation and its checkpoints. Dysregulation of the cellular processes that lead to cancer includes gene mutations, epimutations, non-coding RNA (ncRNA) regulation, posttranscriptional and posttranslational modifications. Long non-coding RNA (lncRNA) are defined as transcribed RNA molecules, more than 200 nucleotides in length which are not translated into proteins. They regulate gene expression through several mechanisms and therefore add another level of complexity to the regulatory mechanisms affecting tumor development. Since few studies have been performed on ROCs, in this review we summarize the mechanisms of action of lncRNA in OC, with an emphasis on ROCs.

lncExplore: a database of pan-cancer analysis and systematic functional annotation for lncRNAs from RNA-sequencing data

Over the past few years, with the rapid growth of deep-sequencing technology and the development of computational prediction algorithms, a large number of long non-coding RNAs (lncRNAs) have been identified in various types of human cancers. Therefore, it has become critical to determine how to properly annotate the potential function of lncRNAs from RNA-sequencing (RNA-seq) data and arrange the robust information and analysis into a useful system readily accessible by biological and clinical researchers. In order to produce a collective interpretation of lncRNA functions, it is necessary to integrate different types of data regarding the important functional diversity and regulatory role of these lncRNAs. In this study, we utilized transcriptomic sequencing data to systematically observe and identify lncRNAs and their potential functions from 5034 The Cancer Genome Atlas RNA-seq datasets covering 24 cancers. Then, we constructed the 'lncExplore' database that was developed to comprehensively integrate various types of genomic annotation data for collective interpretation. The distinctive features in our lncExplore database include (i) novel lncRNAs verified by both coding potential and translation efficiency score, (ii) pan-cancer analysis for studying the significantly aberrant expression across 24 human cancers, (iii) genomic annotation of lncRNAs, such as cis-regulatory information and gene ontology, (iv) observation of the regulatory roles as enhancer RNAs and competing endogenous RNAs and (v) the findings of the potential lncRNA biomarkers for the user-interested cancers by integrating clinical information and disease specificity score. The lncExplore database is to our knowledge the first public lncRNA annotation database providing cancer-specific lncRNA expression profiles for not only known but also novel lncRNAs, enhancer RNAs annotation and clinical analysis based on pan-cancer analysis. lncExplore provides a more complete pathway to highly efficient, novel and more comprehensive translation of laboratory discoveries into the clinical context and will assist in reinterpreting the biological regulatory function of lncRNAs in cancer research. Database URL http://lncexplore.bmi.nycu.edu.tw.

Vitamin D regulation of and by long non coding RNAs

Two percent or less of the genome is used to transcribe mRNAs encoding proteins. Nearly all the remainder is utilized in transcribing non coding RNAs, the bulk of which are RNAs at least 200 base in length, long non coding RNAs (lncRNA). Their number is estimated to be about 28,000, but only a small fraction of lncRNAs are well characterized. That said lncRNAs have been found to regulate a very diverse array of biochemical and genomic functions. One of the transcription factors found to be regulated by and to regulate lncRNA is the vitamin D receptor (VDR). Like lncRNAs VDR is involved in the regulation of numerous biochemical and genomic processes, so it is not surprising that there would be a number of interactions between lncRNAs and VDR in their diverse functions. However, the study of these interactions is in its infancy. To date most attention has been paid to their interactions in cancer. Our own studies have focused on non melanoma skin cancers, keratinocyte carcinomas to be precise. Deletion of VDR from keratinocytes predisposes them to malignant transformation. Among a number of potential mechanisms we found that VDR deletion from these cells alters the lncRNA profile to a more oncogenic configuration, increasing the expression of well known oncogenic lncRNAs and decreasing the expression of well known tumor suppressor lncRNAs. Subsequent studies in other cancers have found similar associations between VDR and oncogenic lncRNAs with evidence of tumor specificity. To date these studies primarily reveal associations rather than causality, but causal links should be expected as research in this field continues to develop.

LDAH2V: Exploring Meta-Paths Across Multiple Networks for lncRNA-Disease Association Prediction

Accumulating evidence has demonstrated dysfunctions of long non-coding RNAs (lncRNAs) are involved in various complex human diseases. However, even today, the relationships between lncRNAs and diseases remain unknown in most cases. Developing effective computational approaches to identify potential lncRNA-disease associations has become a hot topic. Existing network-based approaches are usually focused on the intrinsic features of lncRNAs and diseases but ignore the heterogeneous information of biological networks. Considering the limitations in previous methods, we propose LDAH2V, an efficient computational framework for predicting potential lncRNA-disease associations. LDAH2V uses the HIN2Vec to calculate the meta-path and feature vector for each lncRNA-disease pair in the heterogeneous information network (HIN), which consists of lncRNA similarity network, disease similarity network, miRNA similarity network, and the associations between them. Then, a Gradient Boosting Tree (GBT) classifier to predict lncRNA-disease associations is built with the feature vectors. The results show that LDAH2V performs significantly better than the four existing state-of-the-art methods and gains an AUC of 0.97 in the 10-fold cross-validation test. Furthermore, case studies of colon cancer and ovarian cancer-related lncRNAs have been confirmed in related databases and medical literature.

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.

Advances in Cereal Crop Genomics for Resilience under Climate Change

Adapting to climate change, providing sufficient human food and nutritional needs, and securing sufficient energy supplies will call for a radical transformation from the current conventional adaptation approaches to more broad-based and transformative alternatives. This entails diversifying the agricultural system and boosting productivity of major cereal crops through development of climate-resilient cultivars that can sustainably maintain higher yields under climate change conditions, expanding our focus to crop wild relatives, and better exploitation of underutilized crop species. This is facilitated by the recent developments in plant genomics, such as advances in genome sequencing, assembly, and annotation, as well as gene editing technologies, which have increased the availability of high-quality reference genomes for various model and non-model plant species. This has necessitated genomics-assisted breeding of crops, including underutilized species, consequently broadening genetic variation of the available germplasm; improving the discovery of novel alleles controlling important agronomic traits; and enhancing creation of new crop cultivars with improved tolerance to biotic and abiotic stresses and superior nutritive quality. Here, therefore, we summarize these recent developments in plant genomics and their application, with particular reference to cereal crops (including underutilized species). Particularly, we discuss genome sequencing approaches, quantitative trait loci (QTL) mapping and genome-wide association (GWAS) studies, directed mutagenesis, plant non-coding RNAs, precise gene editing technologies such as CRISPR-Cas9, and complementation of crop genotyping by crop phenotyping. We then conclude by providing an outlook that, as we step into the future, high-throughput phenotyping, pan-genomics, transposable elements analysis, and machine learning hold much promise for crop improvements related to climate resilience and nutritional superiority.

A Novel Approach for Potential Human LncRNA-Disease Association Prediction Based on Local Random Walk

In recent years, lncRNAs (long non-coding RNAs) have been proved to be closely related to many diseases that are seriously harmful to human health. Although researches on clarifying the relationships between lncRNAs and diseases are developing rapidly, associations between the lncRNAs and diseases are still remaining largely unknown. In this manuscript, a novel Local Random Walk based prediction model called LRWHLDA is proposed for inferring potential associations between human lncRNAs and diseases. In LRWHLDA, a new heterogeneous network is established first, which allows that LRWHLDA can be implemented in the case of lacking known lncRNA-disease associations. And then, an improved local random walk method is designed for prediction of novel lncRNA-disease associations, which can help LRWHLDA achieve high prediction accuracy but with low time complexity. Finally, in order to evaluate the prediction performance of LRWHLDA, different frameworks such as LOOCV, 2-folds CV, and 5-folds CV have been implemented, simulation results indicate that LRWHLDA can achieve reliable AUCs of 0.8037, 0.8354, and 0.8556 under the frameworks of 2-fold CV, 5-fold CV, and LOOCV, respectively. Hence, it is easy to know that LRWHLDA contains the potential to be a representative of emerging methods in the field of research on potential lncRNA-disease associations prediction.

RWSF-BLP: a novel lncRNA-disease association prediction model using random walk-based multi-similarity fusion and bidirectional label propagation

An increasing number of studies and experiments have demonstrated that long noncoding RNAs (lncRNAs) have a massive impact on various biological processes. Predicting potential associations between lncRNAs and diseases not only can improve our understanding of the molecular mechanisms of human diseases but also can facilitate the identification of biomarkers for disease diagnosis, treatment, and prevention. However, identifying such associations through experiments is costly and demanding, thereby prompting researchers to develop computational methods to complement these experiments. In this paper, we constructed a novel model called RWSF-BLP (a novel lncRNA-disease association prediction model using Random Walk-based multi-Similarity Fusion and Bidirectional Label Propagation), which applies an efficient random walk-based multi-similarity fusion (RWSF) method to fuse different similarity matrices and utilizes bidirectional label propagation to predict potential lncRNA-disease associations. Leave-one-out cross-validation (LOOCV) and 5-fold cross-validation (5-fold-CV) were implemented in the evaluation RWSF-BLP performance. Results showed that, RWSF-BLP has reliable AUCs of 0.9086 and 0.9115 ± 0.0044 under the framework of LOOCV and 5-fold-CV and outperformed other four canonical methods. Case studies on lung cancer and leukemia demonstrated that potential lncRNA-disease associations can be predicted through our method. Therefore, our method can accurately infer potential lncRNA-disease associations and may be a good choice in future biomedical research.

The Functions and Unique Features of LncRNAs in Cancer Development and Tumorigenesis

Over the past decades, research on cancer biology has focused on the involvement of protein-coding genes in cancer development. Long noncoding RNAs (lncRNAs), which are transcripts longer than 200 nucleotides that lack protein-coding potential, are an important class of RNA molecules that are involved in a variety of biological functions. Although the functions of a majority of lncRNAs have yet to be clarified, some lncRNAs have been shown to be associated with human diseases such as cancer. LncRNAs have been shown to contribute to many important cancer phenotypes through their interactions with other cellular macromolecules including DNA, protein and RNA. Here we describe the literature regarding the biogenesis and features of lncRNAs. We also present an overview of the current knowledge regarding the roles of lncRNAs in cancer from the view of various aspects of cellular homeostasis, including proliferation, survival, migration and genomic stability. Furthermore, we discuss the methodologies used to identify the function of lncRNAs in cancer development and tumorigenesis. Better understanding of the molecular mechanisms involving lncRNA functions in cancer is critical for the development of diagnostic and therapeutic strategies against tumorigenesis.

A novel computational model for predicting potential LncRNA-disease associations based on both direct and indirect features of LncRNA-disease pairs

Background

Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) are closely associated with human diseases, and it is useful for the diagnosis and treatment of diseases to get the relationships between lncRNAs and diseases. Due to the high costs and time complexity of traditional bio-experiments, in recent years, more and more computational methods have been proposed by researchers to infer potential lncRNA-disease associations. However, there exist all kinds of limitations in these state-of-the-art prediction methods as well.

Results

In this manuscript, a novel computational model named FVTLDA is proposed to infer potential lncRNA-disease associations. In FVTLDA, its major novelty lies in the integration of direct and indirect features related to lncRNA-disease associations such as the feature vectors of lncRNA-disease pairs and their corresponding association probability fractions, which guarantees that FVTLDA can be utilized to predict diseases without known related-lncRNAs and lncRNAs without known related-diseases. Moreover, FVTLDA neither relies solely on known lncRNA-disease nor requires any negative samples, which guarantee that it can infer potential lncRNA-disease associations more equitably and effectively than traditional state-of-the-art prediction methods. Additionally, to avoid the limitations of single model prediction techniques, we combine FVTLDA with the Multiple Linear Regression (MLR) and the Artificial Neural Network (ANN) for data analysis respectively. Simulation experiment results show that FVTLDA with MLR can achieve reliable AUCs of 0.8909, 0.8936 and 0.8970 in 5-Fold Cross Validation (fivefold CV), 10-Fold Cross Validation (tenfold CV) and Leave-One-Out Cross Validation (LOOCV), separately, while FVTLDA with ANN can achieve reliable AUCs of 0.8766, 0.8830 and 0.8807 in fivefold CV, tenfold CV, and LOOCV respectively. Furthermore, in case studies of gastric cancer, leukemia and lung cancer, experiment results show that there are 8, 8 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with MLR, and 8, 7 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with ANN, having been verified by recent literature. Comparing with the representative prediction model of KATZLDA, comparison results illustrate that FVTLDA with MLR and FVTLDA with ANN can achieve the average case study contrast scores of 0.8429 and 0.8515 respectively, which are both notably higher than the average case study contrast score of 0.6375 achieved by KATZLDA.

Conclusion

The simulation results show that FVTLDA has good prediction performance, which is a good supplement to future bioinformatics research.

Role of microRNA and Long Non-Coding RNA in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) accounts for about 80-90% of all liver cancers and is found to be the third most common cause of cancer mortality in the Asia-Pacific region. Risk factors include hepatitis B and C virus, cirrhosis, aflatoxin-contaminated food, alcohol, and diabetes. Surgically removing the tumor tissue seems effective but a high chance of recurrence has led to an urgent need to develop novel molecules for the treatment of HCC. Clinical management with sorafenib is found to be effective but it is only able to prolong survival for a few months. Various side effects like gastrointestinal and abdominal pain, hypertension, and hemorrhage are also associated with sorafenib, which calls for the unmet need of effective therapies against HCC. Similarly, the genetic mechanisms behind the occurrence of HCC are still unknown and need to be expounded further for developing newer candidates. Since unearthing the concept of these variants, transcriptomics has revealed the role of non-coding RNAs (ncRNAs) in many cellular, physiological and pathobiological processes. They are also found to be widely associated and abundantly expressed in a variety of cancer. Aberrant expression and mutations are closely related to tumorigenesis and metastasis and hence are classified as novel biomarkers and therapeutic targets for the treatment of cancer, including HCC. Herein, this review summarises the relationship between ncRNAs and hepatocellular carcinoma.

Identification of small non-coding RNAs responsive to GUN1 and GUN5 related retrograde signals in Arabidopsis thaliana

Chloroplast perturbations activate retrograde signalling pathways, causing dynamic changes of gene expression. Besides transcriptional control of gene expression, different classes of small non-coding RNAs (sRNAs) act in gene expression control, but comprehensive analyses regarding their role in retrograde signalling are lacking. We performed sRNA profiling in response to norflurazon (NF), which provokes retrograde signals, in Arabidopsis thaliana wild type (WT) and the two retrograde signalling mutants gun1 and gun5. The RNA samples were also used for mRNA and long non-coding RNA profiling to link altered sRNA levels to changes in the expression of their cognate target RNAs. We identified 122 sRNAs from all known sRNA classes that were responsive to NF in the WT. Strikingly, 142 and 213 sRNAs were found to be differentially regulated in both mutants, indicating a retrograde control of these sRNAs. Concomitant with the changes in sRNA expression, we detected about 1500 differentially expressed mRNAs in the NF-treated WT and around 900 and 1400 mRNAs that were differentially regulated in the gun1 and gun5 mutants, with a high proportion (~30%) of genes encoding plastid proteins. Furthermore, around 20% of predicted miRNA targets code for plastid-localised proteins. Among the sRNA-target pairs, we identified pairs with an anticorrelated expression as well pairs showing other expressional relations, pointing to a role of sRNAs in balancing transcriptional changes upon retrograde signals. Based on the comprehensive changes in sRNA expression, we assume a considerable impact of sRNAs in retrograde-dependent transcriptional changes to adjust plastidic and nuclear gene expression.

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

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

Long noncoding RNAs: new insights in modulating mammalian spermatogenesis

Spermatogenesis is a complex differentiating developmental process in which undifferentiated spermatogonial germ cells differentiate into spermatocytes, spermatids, and finally, to mature spermatozoa. This multistage developmental process of spermatogenesis involves the expression of many male germ cell-specific long noncoding RNAs (lncRNAs) and highly regulated and specific gene expression. LncRNAs are a recently discovered large class of noncoding cellular transcripts that are still relatively unexplored. Only a few of them have post-meiotic; however, lncRNAs are involved in many cellular biological processes. The expression of lncRNAs is biologically relevant in the highly dynamic and complex program of spermatogenesis and has become a research focus in recent genome studies. This review considers the important roles and novel regulatory functions whereby lncRNAs modulate mammalian spermatogenesis.

LDAI-ISPS: LncRNA-Disease Associations Inference Based on Integrated Space Projection Scores

Long non-coding RNAs (long ncRNAs, lncRNAs) of all kinds have been implicated in a range of cell developmental processes and diseases, while they are not translated into proteins. Inferring diseases associated lncRNAs by computational methods can be helpful to understand the pathogenesis of diseases, but those current computational methods still have not achieved remarkable predictive performance: such as the inaccurate construction of similarity networks and inadequate numbers of known lncRNA-disease associations. In this research, we proposed a lncRNA-disease associations inference based on integrated space projection scores (LDAI-ISPS) composed of the following key steps: changing the Boolean network of known lncRNA-disease associations into the weighted networks via combining all the global information (e.g., disease semantic similarities, lncRNA functional similarities, and known lncRNA-disease associations); obtaining the space projection scores via vector projections of the weighted networks to form the final prediction scores without biases. The leave-one-out cross validation (LOOCV) results showed that, compared with other methods, LDAI-ISPS had a higher accuracy with area-under-the-curve (AUC) value of 0.9154 for inferring diseases, with AUC value of 0.8865 for inferring new lncRNAs (whose associations related to diseases are unknown), with AUC value of 0.7518 for inferring isolated diseases (whose associations related to lncRNAs are unknown). A case study also confirmed the predictive performance of LDAI-ISPS as a helper for traditional biological experiments in inferring the potential LncRNA-disease associations and isolated diseases.

The lncRNA Fer1L4 is an adverse prognostic parameter in clear-cell renal-cell carcinoma

Purpose

Long non-coding RNAs (lncRNA) are involved in oncogenesis and tumor progression in various tumor entities. At present, little is known about the role in tumor biology of the lncRNA Fer-1 like family member 4 (Fer1L4) in clear-cell renal-cell carcinoma (ccRCC). The aim of this study is to evaluate the expression of Fer1L4 in patients with ccRCC, its association with clinicopathological parameters, and value as prognostic biomarker.

Material and methods

The expression of Fer1L4 was analyzed in the TCGA ccRCC cohort (n = 603; ccRCC n = 522, normal n = 81) and subsequently validated by quantitative real-time PCR in an independent cohort (n = 103, ccRCC n = 69, normal n = 34). Expression profiles were statistically correlated with clinicopathological and survival data.

Results

Fer1L4 lncRNA is overexpressed in ccRCC compared to adjacent normal tissues. Increased expression significantly correlates with tumor aggressiveness: high expression levels of Fer1L4 RNA were found in higher grade, higher stage, and metastatic tumors. Furthermore, Fer1L4 overexpression is an independent prognostic factor for overall, cancer-specific, and progression-free survival of patients with ccRCC.

Conclusion

Fer1L4 expression significantly correlates with aspects of tumor aggressiveness. Based on this impact on tumor progression and its influence as an independent prognostic factor, Fer1L4 appears to exert properties as an oncogene in ccRCC. As a prognostic tissue biomarker, further functional investigations are warranted to investigate Fer1L4 as a potential therapeutic target.

Computational models for lncRNA function prediction and functional similarity calculation

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

SKmDB: an integrated database of next generation sequencing information in skeletal muscle

MOTIVATION

Skeletal muscles have indispensable functions and also possess prominent regenerative ability. The rapid emergence of Next Generation Sequencing (NGS) data in recent years offers us an unprecedented perspective to understand gene regulatory networks governing skeletal muscle development and regeneration. However, the data from public NGS database are often in raw data format or processed with different procedures, causing obstacles to make full use of them.

RESULTS

We provide SKmDB, an integrated database of NGS information in skeletal muscle. SKmDB not only includes all NGS datasets available in the human and mouse skeletal muscle tissues and cells, but also provide preliminary data analyses including gene/isoform expression levels, gene co-expression subnetworks, as well as assembly of putative lincRNAs, typical and super enhancers and transcription factor hotspots. Users can efficiently search, browse and visualize the information with the well-designed user interface and server side. SKmDB thus will offer wet lab biologists useful information to study gene regulatory mechanisms in the field of skeletal muscle development and regeneration.

AVAILABILITY AND IMPLEMENTATION

Freely available on the web at http://sunlab.cpy.cuhk.edu.hk/SKmDB.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Role of lncRNAs in stem cell maintenance and differentiation

Embryonic Stem cells are widely studied to elucidate the disease and developmental processes because of their capability to differentiate into cells of any lineage, Pervasive transcription is a distinct feature of all multicellular organisms and genomic elements such as enhancers and bidirectional or unidirectional promoters regulate these processes. Thousands of loci in each species produce a class of transcripts called noncoding RNAs (ncRNAs), that are well known for their influential regulatory roles in multiple biological processes including stem cell pluripotency and differentiation. The number of lncRNA species increases in more complex organisms highlighting the importance of RNA-based control in the evolution of multicellular organisms. Over the past decade, numerous studies have shed light on lncRNA biogenesis and functional significance in the cell and the organism. In this review, we focus primarily on lncRNAs affecting the stem cell state and developmental pathways.

IDLDA: An Improved Diffusion Model for Predicting LncRNA-Disease Associations

It has been demonstrated that long non-coding RNAs (lncRNAs) play important roles in a variety of biological processes associated with human diseases. However, the identification of lncRNA–disease associations by experimental methods is time-consuming and labor-intensive. Computational methods provide an effective strategy to predict more potential lncRNA–disease associations to some degree. Based on the hypothesis that phenotypically similar diseases are often associated with functionally similar lncRNAs and vice versa, we developed an improved diffusion model to predict potential lncRNA–disease associations (IDLDA). As a result, our model performed well in the global and local cross-validations, which indicated that IDLDA had a great performance in predicting novel associations. Case studies of colon cancer, breast cancer, and gastric cancer were also implemented, all lncRNAs which ranked top 10 in both databases were verified by databases and related literature. The results showed that IDLDA might play a key role in biomedical research.

Research progress on the interactions between long non-coding RNAs and microRNAs in human cancer

Numerous types of molecular mechanisms mediate the development of cancer. Non-coding RNAs (ncRNAs) are being increasingly recognized to play important role in mediating the development of diseases, including cancer. Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are the two most widely studied ncRNAs. Thus far, lncRNAs are known to have biological roles through a variety of mechanisms, including genetic imprinting, chromatin remodeling, cell cycle control, splicing regulation, mRNA decay and translational regulation, and miRNAs regulate gene expression through the degradation of mRNAs and lncRNAs. Although ncRNAs account for a major proportion of the total RNA, the mechanisms underlying the physiological or pathological processes mediated by various types of ncRNAs, and the specific interaction mechanisms between miRNAs and lncRNAs in various physiological and pathological processes, remain largely unknown. Thus, further research in this field is required. In general, the interaction mechanisms between miRNAs and lncRNAs in human cancer have become important research topics, and the study thereof has led to the recent development of related technologies. By providing examples and descriptions, and performing chart analysis, the present study aimed to review the interaction mechanisms and research approaches for these two types of ncRNAs, as well as their roles in the occurrence and development of cancer. These details have far-reaching significance for the utilization of these molecules in the diagnosis and treatment of cancer.

Long Noncoding Transcriptome in Chronic Obstructive Pulmonary Disease

Chronic airway inflammation from recurring exposures to noxious environmental stimuli results in a progressive and irreversible airflow limitation and the lung parenchymal damage that characterizes chronic obstructive pulmonary disease (COPD). The large variability observed in the onset and progression of COPD is primarily driven by complex gene-environment interactions. The transcriptomic and epigenetic memory potential of lung epithelial and innate immune cells drive responses, such as mucus hyperreactivity and airway remodeling, that are tightly regulated by various molecular mechanisms, for which several candidate susceptibility genes have been described. However, the recently described noncoding RNA species, in particular the long noncoding RNAs, may also have an important role in modulating pulmonary responses to chronic inhalation of toxic substances and the development of COPD. This review outlines the features of long noncoding RNAs that have been implicated in regulating the airway inflammatory responses to cigarette smoke exposure and their possible association with COPD pathogenesis. As COPD continues to debilitate the increasingly aging population and contribute to higher morbidity and mortality rates worldwide, the search for better biomarkers and alternative therapeutic options is pivotal.

LncRNA-Disease Associations Prediction Using Bipartite Local Model With Nearest Profile-Based Association Inferring

There is much evidence that long non-coding RNA (lncRNA) is associated with many diseases. However, it is time-consuming and expensive to identify meaningful lncRNA-disease associations (LDAs) through medical or biological experiments. Therefore, investigating how to identify more meaningful LDAs is necessary, and at the same time it is conducive to the prevention, diagnosis and treatment of complex diseases. Considering the limitations of some current prediction models, a novel model based on bipartite local model with nearest profile-based association inferring, BLM-NPAI, is developed for predicting LDAs. This model predicts novel LDAs from the lncRNA side and the disease side, respectively. More importantly, for some lncRNAs and diseases without any association, the model can also be predicted by their nearest neighbors. Leave-one-out cross validation (LOOCV) and 5-fold cross validation are implemented for BLM-NPAI to evaluate the performance of this model. Our model is superior to current advanced methods in most cases. In addition, to verify the validity and reliability of BLM-NPAI, three disease cases and three lncRNA cases are analyzed to further evaluate BLM-NPAI. Finally, these predicted novel LDAs are confirmed by using the LncRNA-disease database.

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

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

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

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The model automatically captures important association features

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

Long Noncoding RNA Analyses for Osteoporosis Risk in Caucasian Women

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSION

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

Long noncoding RNA FER1L4 acts as an oncogenic driver in human pan-cancer

The function of Fer-1 like family member 4 (FER1L4) in human pan-cancer is unknown. Expression of FER1L4 in tumor tissues and nontumor tissues, upstream regulation of FER1L4, and the relationship between its expression with prognosis and chemoresistance were examined by The Cancer Genome Atlas and Gene Expression Omnibus databases. Next, these results were validated in breast tumor and paired nontumor tissues in our cohort. FER1L4 expression is higher in tumor tissues compared with the adjacent nontumor tissues. High FER1L4 expression is associated with worse disease outcomes. Hypomethylation and H3K4me3 accumulation in FER1L4 promoter locus activate its transcriptional expression. Moreover, FER1L4 may trigger chemoresistance in human cancer. Gene Ontology enrichment analysis revealed that FER1L4 may be involved in processes associated with tumorigenesis. These results indicated that FER1L4 may act as an oncogenic driver and it may be a potential therapy target in human cancer.