Prediction Of lncRNAs Research Articles

Systematic and computational identification of Androctonus crassicauda long non-coding RNAs

The potential function of long non-coding RNAs in regulating neighbor protein-coding genes has attracted scientists’ attention. Despite the important role of lncRNAs in biological processes, a limited number of studies focus on non-model animal lncRNAs. In this study, we used a stringent step-by-step filtering pipeline and machine learning-based tools to identify the specific Androctonus crassicauda lncRNAs and analyze the features of predicted scorpion lncRNAs. 13,401 lncRNAs were detected using pipeline in A. crassicauda transcriptome. The blast results indicated that the majority of these lncRNAs sequences (12,642) have no identifiable orthologs even in closely related species and those considered as novel lncRNAs. Compared to lncRNA prediction tools indicated that our pipeline is a helpful approach to distinguish protein-coding and non-coding transcripts from RNA sequencing data of species without reference genomes. Moreover, analyzing lncRNA characteristics in A. crassicauda uncovered that lower protein-coding potential, lower GC content, shorter transcript length, and less number of isoform per gene are outstanding features of A. crassicauda lncRNAs transcripts.

LncEvo: automated identification and conservation study of long noncoding RNAs

BackgroundLong noncoding RNAs represent a large class of transcripts with two common features: they exceed an arbitrary length threshold of 200 nt and are assumed to not encode proteins. Although a growing body of evidence indicates that the vast majority of lncRNAs are potentially nonfunctional, hundreds of them have already been revealed to perform essential gene regulatory functions or to be linked to a number of cellular processes, including those associated with the etiology of human diseases. To better understand the biology of lncRNAs, it is essential to perform a more in-depth study of their evolution. In contrast to protein-encoding transcripts, however, they do not show the strong sequence conservation that usually results from purifying selection; therefore, software that is typically used to resolve the evolutionary relationships of protein-encoding genes and transcripts is not applicable to the study of lncRNAs.ResultsTo tackle this issue, we developed lncEvo, a computational pipeline that consists of three modules: (1) transcriptome assembly from RNA-Seq data, (2) prediction of lncRNAs, and (3) conservation study—a genome-wide comparison of lncRNA transcriptomes between two species of interest, including search for orthologs. Importantly, one can choose to apply lncEvo solely for transcriptome assembly or lncRNA prediction, without calling the conservation-related part.ConclusionslncEvo is an all-in-one tool built with the Nextflow framework, utilizing state-of-the-art software and algorithms with customizable trade-offs between speed and sensitivity, ease of use and built-in reporting functionalities. The source code of the pipeline is freely available for academic and nonacademic use under the MIT license at https://gitlab.com/spirit678/lncrna_conservation_nf.

Novel Transcript Discovery Expands the Repertoire of Pathologically-Associated, Long Non-Coding RNAs in Vascular Smooth Muscle Cells.

Vascular smooth muscle cells (VSMCs) provide vital contractile force within blood vessel walls, yet can also propagate cardiovascular pathologies through proliferative and pro-inflammatory activities. Such phenotypes are driven, in part, by the diverse effects of long non-coding RNAs (lncRNAs) on gene expression. However, lncRNA characterisation in VSMCs in pathological states is hampered by incomplete lncRNA representation in reference annotation. We aimed to improve lncRNA representation in such contexts by assembling non-reference transcripts in RNA sequencing datasets describing VSMCs stimulated in vitro with cytokines, growth factors, or mechanical stress, as well as those isolated from atherosclerotic plaques. All transcripts were then subjected to a rigorous lncRNA prediction pipeline. We substantially improved coverage of lncRNAs responding to pro-mitogenic stimuli, with non-reference lncRNAs contributing 21–32% for each dataset. We also demonstrate non-reference lncRNAs were biased towards enriched expression within VSMCs, and transcription from enhancer sites, suggesting particular relevance to VSMC processes, and the regulation of neighbouring protein-coding genes. Both VSMC-enriched and enhancer-transcribed lncRNAs were large components of lncRNAs responding to pathological stimuli, yet without novel transcript discovery 33–46% of these lncRNAs would remain hidden. Our comprehensive VSMC lncRNA repertoire allows proper prioritisation of candidates for characterisation and exemplifies a strategy to broaden our knowledge of lncRNA across a range of disease states.

Predicting Long non-coding RNAs through feature ensemble learning

BackgroundMany transcripts have been generated due to the development of sequencing technologies, and lncRNA is an important type of transcript. Predicting lncRNAs from transcripts is a challenging and important task. Traditional experimental lncRNA prediction methods are time-consuming and labor-intensive. Efficient computational methods for lncRNA prediction are in demand.ResultsIn this paper, we propose two lncRNA prediction methods based on feature ensemble learning strategies named LncPred-IEL and LncPred-ANEL. Specifically, we encode sequences into six different types of features including transcript-specified features and general sequence-derived features. Then we consider two feature ensemble strategies to utilize and integrate the information in different feature types, the iterative ensemble learning (IEL) and the attention network ensemble learning (ANEL). IEL employs a supervised iterative way to ensemble base predictors built on six different types of features. ANEL introduces an attention mechanism-based deep learning model to ensemble features by adaptively learning the weight of individual feature types. Experiments demonstrate that both LncPred-IEL and LncPred-ANEL can effectively separate lncRNAs and other transcripts in feature space. Moreover, comparison experiments demonstrate that LncPred-IEL and LncPred-ANEL outperform several state-of-the-art methods when evaluated by 5-fold cross-validation. Both methods have good performances in cross-species lncRNA prediction.ConclusionsLncPred-IEL and LncPred-ANEL are promising lncRNA prediction tools that can effectively utilize and integrate the information in different types of features.

Bioinformatic investigation for candidate genes and molecular mechanism in the pathogenesis of membranous nephropathy.

We aimed to explore the detailed molecular mechanism of immune-associated genes in membranous nephropathy (MN). A microarray data set (GSE133288) was retrieved from the Gene Expression Omnibus database. Differentially expressed mRNAs (DEMs) in MN vs control groups were identified, and MN-related DEMs (MN-DEMs) were further verified and screened using the comparative toxicogenomics database (CTD) database. The publicly available database, InnateDB was used to investigate immune genes, and the overlapped genes between MN-DEMs and the immune genes were considered as MN-related immune genes (iDEMs). A protein-protein interaction network (PPI) was constructed based on these iDEMs, followed by function and pathway enrichment analysis. Finally, microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) associated with iDEMs were predicted, followed by a lncRNA-miRNA-mRNA (competing endogenous RNAs, ceRNA) network construction. A total of 327 DEMs and 48 iDEMs were revealed; a PPI network was constructed with 100 PPI pairs and 37 iDEMs. iDEMs including JUN and FOS were mainly enriched in pathways such as osteoclast differentiation and function including response to immobilization stress, respectively. Based on mRNA-associated miRNA and lncRNA prediction, 30 ceRNA interactions including KCNQ1OT1-miR-204-5p-SRY-Box Transcription Factor 4 (SOX4) were explored. mRNAs including FOS and JUN might participate in MN development via response to immobilization stress function and the osteoclast differentiation pathway. The mRNA SOX4 might contribute to MN progression via sponging KCNQ1OT1-miR-204-5p interaction.

Subtype specific expression and survival prediction of pivotal lncRNAs in muscle invasive bladder cancer

Comprehensive transcriptome expression analyses of bladder cancer revealed distinct lncRNA clusters with differential molecular and clinical characteristics. In this study, pivotal lncRNAs were assessed for their impact on survival and their differential expression between the molecular bladder cancer subtypes. FFPE samples from chemotherapy-naïve patients with muscle invasive bladder cancer (MIBC) were analyzed on the Nanostring nCounter platform for absolute quantification. An established 36-gene panel was used for molecular subtype classification into basal, luminal and infiltrated MIBC. In a second step, 14 pivotal lncRNAs were assessed for their molecular subtype attribution, and their predictive value in disease-specific survival. In silico validation was performed on a total of 487 MIBC patients (MDA, TGCA and Chungbuk cohort). Several pivotal lncRNAs showed a distinct molecular subtype attribution: e.g. MALAT1 showed a downregulation in the basal subtype (p = 0.009), TUG1 and CBR3AS1 showed an upregulation in the luminal subtype (p ≤ 0.001). High transcript levels of SNHG16, CBR3AS1 and H19 appeared to be predictive for a shorter disease-specific survival. Patients overexpressing putative oncogenes MALAT1 and TUG1 in MIBC tissue presented prolonged survival, suggesting tumor suppressive effects of both lncRNAs. The Nanostring nCounter proved to be a valid platform for the quantification of low-abundance transcripts including lncRNAs.

Prediction and prioritization of autism-associated long non-coding RNAs using gene expression and sequence features

BackgroundAutism spectrum disorders (ASD) refer to a range of neurodevelopmental conditions, which are genetically complex and heterogeneous with most of the genetic risk factors also found in the unaffected general population. Although all the currently known ASD risk genes code for proteins, long non-coding RNAs (lncRNAs) as essential regulators of gene expression have been implicated in ASD. Some lncRNAs show altered expression levels in autistic brains, but their roles in ASD pathogenesis are still unclear.ResultsIn this study, we have developed a new machine learning approach to predict candidate lncRNAs associated with ASD. Particularly, the knowledge learnt from protein-coding ASD risk genes was transferred to the prediction and prioritization of ASD-associated lncRNAs. Both developmental brain gene expression data and transcript sequence were found to contain relevant information for ASD risk gene prediction. During the pre-training phase of model construction, an autoencoder network was implemented for a representation learning of the gene expression data, and a random-forest-based feature selection was applied to the transcript-sequence-derived k-mers. Our models, including logistic regression, support vector machine and random forest, showed robust performance based on tenfold cross-validations as well as candidate prioritization with hypothetical loci. We then utilized the models to predict and prioritize a list of candidate lncRNAs, including some reported to be cis-regulators of known ASD risk genes, for further investigation.ConclusionsOur results suggest that ASD risk genes can be accurately predicted using developmental brain gene expression data and transcript sequence features, and the models may provide useful information for functional characterization of the candidate lncRNAs associated with ASD.

A Seven Immune-Related lncRNAs Model to Increase the Predicted Value of Lung Adenocarcinoma.

BackgroundRecent research has shown that immune-related lncRNA plays a crucial part in the tumor immune microenvironment. This study tried to identify immune-related lncRNAs and construct a robust prediction model to increase the predicted value of lung adenocarcinoma (LUAD).MethodsRNA expression data of LUAD were download from the Cancer Genome Atlas (TCGA) database. Immune genes were acquired from the Molecular Signatures Database (MSigDB). The immune gene related lncRNAs were acquired by the “limma R” package and Cytoscape3.7.1. Cox regression analysis was applied to construct this forecast model. The prognostic model was validated by the testing cohort which was acquired by the bootstrap method.ResultsA total of 551 lncRNA expression profiles including 497 LUAD tissues and 54 non-LUAD tissues were obtained. A total of 331 immune genes were acquired. The result of the Cox regression analysis showed that seven lncRNAs (AC022784-1, NKILA, AC026355-1, AC068338-3, LINC01843, SYNPR-AS1, and AC123595-1) can be performed to construct the prediction model to forecast the prognosis of LUAD. Kaplan–Meier curves indicated that our prediction model can distribute LUAD patients into two different risk groups (high and low) with significant statistical significance (P = 1.484e-07). Cox analysis and independent analysis illustrated that the seven-lncRNAs prediction model was an isolated factor by comparing it with other clinical variables. We validated the accuracy of our model in the testing dataset. Furthermore, the prognostic model also showed higher predictive efficiency than three other published prognostic models. The two different survival groups represented diverse immune features according to principal components analysis. GSEA analysis (gene set enrichment analysis) indicated that seven-lncRNAs signatures may be involved in the progression of tumorigenesis.ConclusionsWe have established a seven immune-related lncRNAs prediction model. This prognostic model had significant clinical significance that increased the predicted value and guided the personalized treatment for LUAD patients.

Identification and functional prediction of lncRNAs during cassava post‐harvest physiological deterioration

AbstractCassava (Manihot esculenta Crantz) is an important staple food crop in Latin America and Africa. However, after harvest, the tuberous roots undergo rapid post‐harvest physiological deterioration (PPD), a known perishable phenomenon that usually appears within 72 h post‐harvest. Post‐harvest physiological deterioration largely reduces the quality and economic value of cassava tuberous roots, restricting the use of cassava as a raw material in the food industry. To analyze the regulatory roles of long non‐coding RNAs (lncRNAs) during PPD, whole systematic screening of lncRNAs was performed. In this study, 288 differentially expressed lncRNAs were identified from 2,787 high‐confidence lncRNAs during PPD. cis‐Acting analysis revealed 10 interested lncRNA–mRNA pairs, in which the neighboring protein‐coding genes were mainly related to ethylene signaling, calcium sensors, and transcription factors. trans‐Regulatory coexpression network analysis reported that lncRNAs were potentially associated with genes involved in the oxidation‐reduction process. These results present a broad vision of cassava lncRNAs during PPD and provide the potential candidates for genetic engineering to alleviate deterioration of tuberous roots.

Expression-based prediction of human essential genes and candidate lncRNAs in cancer cells.

Essential genes are required for the reproductive success at either cellular or organismal level. The identification of essential genes is important for understanding the core biological processes and identifying effective therapeutic drug targets. However, experimental identification of essential genes is costly, time consuming and labor intensive. Although several machine learning models have been developed to predict essential genes, these models are not readily applicable to lncRNAs. Moreover, the currently available models cannot be used to predict essential genes in a specific cancer type. In this study, we have developed a new machine learning approach, XGEP (eXpression-based Gene Essentiality Prediction), to predict essential genes and candidate lncRNAs in cancer cells. The novelty of XGEP lies in the utilization of relevant features derived from the TCGA transcriptome dataset through collaborative embedding. When evaluated on the pan-cancer dataset, XGEP was able to accurately predict human essential genes and achieve significantly higher performance than previous models. Notably, several candidate lncRNAs selected by XGEP are reported to promote cell proliferation and inhibit cell apoptosis. Moreover, XGEP also demonstrated superior performance on cancer-type-specific datasets to identify essential genes. The comprehensive lists of candidate essential genes in specific cancer types may be used to guide experimental characterization and facilitate the discovery of drug targets for cancer therapy. The source code and datasets used in this study are freely available at https://github.com/BioDataLearning/XGEP. Supplementary data are available at Bioinformatics online.

Multi-feature fusion for deep learning to predict plant lncRNA-protein interaction

Long non-coding RNAs (lncRNAs) play key roles in regulating cellular biological processes through diverse molecular mechanisms including binding to RNA binding proteins. The majority of plant lncRNAs are functionally uncharacterized, thus, accurate prediction of plant lncRNA–protein interaction is imperative for subsequent functional studies. We present an integrative model, namely DRPLPI. Its uniqueness is that it predicts by multi-feature fusion. Structural and four groups of sequence features are used, including tri-nucleotide composition, gapped k-mer, recursive complement and binary profile. We design a multi-head self-attention long short-term memory encoder-decoder network to extract generative high-level features. To obtain robust results, DRPLPI combines categorical boosting and extra trees into a single meta-learner. Experiments on Zea mays and Arabidopsis thaliana obtained 0.9820 and 0.9652 area under precision/recall curve (AUPRC) respectively. The proposed method shows significant enhancement in the prediction performance compared with existing state-of-the-art methods.

Genome-Wide Analysis and Function Prediction of Long Noncoding RNAs in Sheep Pituitary Gland Associated with Sexual Maturation

Long noncoding RNA (lncRNA) plays a crucial role in the hypothalamic-pituitary-testis (HPT) axis associated with sheep reproduction. The pituitary plays a connecting role in the HPT axis. However, little is known of their expression pattern and potential roles in the pituitary gland. To explore the potential lncRNAs that regulate the male sheep pituitary development and sexual maturation, we constructed immature and mature sheep pituitary cDNA libraries (three-month-old, TM, and nine-month-old, NM, respectively, n = 3) for lncRNA and mRNA high-throughput sequencing. Firstly, the expression of lncRNA and mRNA were comparatively analyzed. 2417 known lncRNAs and 1256 new lncRNAs were identified. Then, 193 differentially expressed (DE) lncRNAs and 1407 DE mRNAs were found in the pituitary between the two groups. Moreover, mRNA-lncRNA interaction network was constructed according to the target gene prediction of lncRNA and functional enrichment analysis. Five candidate lncRNAs and their targeted genes HSD17B12, DCBLD2, PDPK1, GPX3 and DLL1 that enriched in growth and reproduction related pathways were further filtered. Lastly, the interaction of candidate lncRNA TCONS_00066406 and its targeted gene HSD17B12 were validated in in vitro of sheep pituitary cells. Our study provided a systematic presentation of lncRNAs and mRNAs in male sheep pituitary, which revealed the potential role of lncRNA in male reproduction.

Genome-wide discovery and functional prediction of salt-responsive lncRNAs in duckweed

BackgroundSalt significantly depresses the growth and development of the greater duckweed, Spirodela polyrhiza, a model species of floating aquatic plants. Physiological responses of this plant to salt stress have been characterized, however, the roles of long noncoding RNAs (lncRNAs) remain unknown.ResultsIn this work, totally 2815 novel lncRNAs were discovered in S. polyrhiza by strand-specific RNA sequencing, of which 185 (6.6%) were expressed differentially under salinity condition. Co-expression analysis indicated that the trans-acting lncRNAs regulated their co-expressed genes functioning in amino acid metabolism, cell- and cell wall-related metabolism, hormone metabolism, photosynthesis, RNA transcription, secondary metabolism, and transport. In total, 42 lncRNA-mRNA pairs that might participate in cis-acting regulation were found, and these adjacent genes were involved in cell wall, cell cycle, carbon metabolism, ROS regulation, hormone metabolism, and transcription factor. In addition, the lncRNAs probably functioning as miRNA targets were also investigated. Specifically, TCONS_00033722, TCONS_00044328, and TCONS_00059333 were targeted by a few well-studied salt-responsive miRNAs, supporting the involvement of miRNA and lncRNA interactions in the regulation of salt stress responses. Finally, a representative network of lncRNA-miRNA-mRNA was proposed and discussed to participate in duckweed salt stress via auxin signaling.ConclusionsThis study is the first report on salt-responsive lncRNAs in duckweed, and the findings will provide a solid foundation for in-depth functional characterization of duckweed lncRNAs in response to salt stress.

Identification, functional prediction, and key lncRNA verification of cold stress-related lncRNAs in rats liver

Cold stimulation reduces the quality of animal products and increases animal mortality, causing huge losses to the livestock industry in cold regions. Long non-coding RNAs (lncRNAs) take part in many biological processes through transcriptional regulation, intracellular material transport, and chromosome remodeling. Although cold stress-related lncRNAs have been reported in plants, no research is available on the characteristic and functional analysis of lncRNAs after cold stress in rats. Here, we built a cold stress animal model firstly. Six SPF male Wistar rats were randomly divided to the acute cold stress group (4 °C, 12 h) and the normal group (24 °C, 12 h). lncRNA libraries were constructed by high-throughput sequencing (HTS) using rat livers. 2,120 new lncRNAs and 273 differentially expressed (DE) lncRNAs were identified in low temperature environments. The target genes of DElncRNA were predicted by cis and trans, and then functional and pathway analysis were performed to them. GO and KEGG analysis revealed that lncRNA targets were mainly participated in the regulation of nucleic acid binding, cold stimulation reaction, metabolic process, immune system processes, PI3K-Akt signaling pathway and pathways in cancer. Next, a interaction network between lncRNA and its targets was constructed. To further reveal the mechanism of cold stress, DElncRNA and DEmRNA were extracted to reconstruct a co-expression sub-network. We found the key lncRNA MSTRG.80946.2 in sub-network. Functional analysis of key lncRNA targets showed that targets were significantly enriched in fatty acid metabolism, the PI3K-Akt signaling pathway and pathways in cancer under cold stress. qRT-PCR confirmed the sequencing results. Finally, hub lncRNA MSTRG.80946.2 was characterized, and verified its relationship with related mRNAs by antisense oligonucleotide (ASO) interference and qRT-PCR. Results confirmed the accuracy of our analysis. To sum up, our work was the first to perform detailed characterization and functional analysis of cold stress-related lncRNAs in rats liver. lncRNAs played crucial roles in energy metabolism, growth and development, immunity and reproductive performance in cold stressed rats. The MSTRG.80946.2 was verified by network and experiments to be a key functional lncRNA under cold stress, regulating ACP1, TSPY1 and Tsn.

SMRT sequencing of a full-length transcriptome reveals transcript variants involved in C18 unsaturated fatty acid biosynthesis and metabolism pathways at chilling temperature in Pennisetum giganteum

BackgroundPennisetum giganteum, an abundant, fast-growing perennial C4 grass that belongs to the genus Pennisetum, family Poaceae, has been developed as a source of biomass for mushroom cultivation and production, as a source of forage for cattle and sheep, and as a tool to remedy soil erosion. However, having a chilling-sensitive nature, P. giganteum seedlings need to be protected while overwintering in most temperate climate regions.ResultsTo elucidate the cold stress responses of P. giganteum, we carried out comprehensive full-length transcriptomes from leaf and root tissues under room temperature (RT) and chilling temperature (CT) using PacBio Iso-Seq long reads. We identified 196,124 and 140,766 full-length consensus transcripts in the RT and CT samples, respectively. We then systematically performed functional annotation, transcription factor identification, long non-coding RNAs (lncRNAs) prediction, and simple sequence repeat (SSR) analysis of those full-length transcriptomes. Isoform analysis revealed that alternative splicing events may be induced by cold stress in P. giganteum, and transcript variants may be involved in C18 unsaturated fatty acid biosynthesis and metabolism pathways at chilling temperature in P. giganteum. Furthermore, the fatty acid composition determination and gene expression level analysis supported that C18 unsaturated fatty acid biosynthesis and metabolism pathways may play roles during cold stress in P. giganteum.ConclusionsWe provide the first comprehensive full-length transcriptomic resource for the abundant and fast-growing perennial grass Pennisetum giganteum. Our results provide a useful transcriptomic resource for exploring the biological pathways involved in the cold stress responses of P. giganteum.

A Novel lncRNA-Disease Association Prediction Model Using Laplacian Regularized Least Squares and Space Projection-Federated Method

Recent studies indicated that numerous long noncoding RNAs (lncRNAs) are closely related to human diseases and can serve as potential biomarkers and drug targets for complex diseases. Therefore, identifying lncRNAs associated with diseases through computational methods is conducive to the exploration of disease pathogenesis. Most previous studies had shortcomings, such as low prediction accuracy, the need for negative samples, and weak generalization. Such studies established shallow prediction models and failed to fully capture the complex relationships among lncRNA-disease associations, lncRNA similarity, and disease similarity. LRLSSP, a new computational method based on Laplacian regularized least squares (LRLS) and space projection was used to predict candidate disease lncRNAs in this study. LRLSSP deeply integrates information on lncRNA similarity, disease similarity, and known lncRNA-disease associations. The estimated score of lncRNA-disease association was obtained through LRLS, and network projection was utilized to reliably predict disease-related lncRNAs. Leave-one-out cross validation(LOOCV) was implemented to evaluate the prediction performance of LRLSSP. Results showed that LRLSSP performed was better than other state-of-the-art methods in predicting lncRNA-disease associations. In addition, case studies conducted on melanoma,cervical cancer, ovarian cancer and breast cancer indicated that LRLSSP can discover potential and novel lncRNA-disease associations. Overall, the results demonstrated that LRLSSP may serve as a reliable and effective computational tool for disease-related lncRNAs prediction.

A General Computational Framework for Prediction of Disease-associated Non-coding RNAs

Since last decade, we have been witnessing the raise of non-coding RNAs (ncRNAs) in biomedical research. Many ncRNAs have been identified and classified into different classes based on their length in number of base pairs (bp). In parallel, our understanding about functions of ncRNAs is gradually increased. However, only small set among tens of thousands of ncRNAs have been well studied about their functions and their roles in development of diseases. This raises a pressing need to develop computational methods to associate diseases and ncRNAs. Two most widely studied ncRNAs are microRNA (miRNA) and long non-coding RNA (lncRNA), since miRNAs are the regulators of most protein-coding genes and lncRNAs are the most ubiquitously found in mammalian. To date, many computational methods have been also proposed for prediction of disease-associated miRNAs and lncRNAs, and recently comprehensively reviewed. However, in the previous reviews, these computational methods were described separately, thus this limits our understanding about their underlying computational aspects. Therefore, in this study, we propose a general computational framework for prediction of disease-associated ncRNAs. The framework demonstrates a whole computational process from data preparation to computational models.

Competing endogenous network analysis identifies lncRNA Meg3 activates inflammatory damage in UVB induced murine skin lesion by sponging miR-93-5p/epiregulin axis.

In this study, we obtained the RNA expression data of murine skin tissues of control, and UVB irradiated groups. After the re-annotation of lncRNAs, a gene expression similarity analysis was done by WGCNA. The target mRNA prediction of lncRNAs, miRNAs, and ceRNA regulatory networks were constructed by five lncRNAs, 14 miRNAs and 54 mRNAs, respectively. Based on the ceRNA network of UVB-induced skin lesions, it was evident that the dysregulation of Meg3 has critical effects on the UVB-induced inflammatory lesion of murine skin tissues. The overexpression of Meg3 after UVB irradiation was observed in primary murine skin fibroblasts, and the up-regulated Meg3 expression was related to the activation of the inflammatory cytokines. These functional experiments demonstrated that the RNA silencing of Meg3 in murine skin fibroblasts could suppress the expression of the cytokines (in vitro) and UVB-induced skin lesions (in vivo). Moreover, the Meg3 functioned as a competing endogenous RNA (ceRNA) that acted as a sponge for miR-93-5p and thereby modulated the expression of Epiregulin (Ereg). Our results proved that Meg3 was involved in UVB-induced skin inflammation and that the ceRNA networks, which includes miR-93-5p and Ereg, could prove to be a potential therapeutic target for UVB-induced skin damage.

Projection-Based Neighborhood Non-Negative Matrix Factorization for lncRNA-Protein Interaction Prediction.

Many long ncRNAs (lncRNA) make their effort by interacting with the corresponding RNA-binding proteins, and identifying the interactions between lncRNAs and proteins is important to understand the functions of lncRNA. Compared with the time-consuming and laborious experimental methods, more and more computational models are proposed to predict lncRNA-protein interactions. However, few models can effectively utilize the biological network topology of lncRNA (protein) and combine its sequence structure features, and most models cannot effectively predict new proteins (lncRNA) that do not interact with any lncRNA (proteins). In this study, we proposed a projection-based neighborhood non-negative matrix decomposition model (PMKDN) to predict potential lncRNA-protein interactions by integrating multiple biological features of lncRNAs (proteins). First, according to lncRNA (protein) sequences and lncRNA expression profile data, we extracted multiple features of lncRNA (protein). Second, based on protein GO ontology annotation, lncRNA sequences, lncRNA(protein) feature information, and modified lncRNA-protein interaction network, we calculated multiple similarities of lncRNA (protein), and fused them to obtain a more accurate lncRNA(protein) similarity network. Finally, combining the similarity and various feature information of lncRNA (protein), as well as the modified interaction network, we proposed a projection-based neighborhood non-negative matrix decomposition algorithm to predict the potential lncRNA-protein interactions. On two benchmark datasets, PMKDN showed better performance than other state-of-the-art methods for the prediction of new lncRNA-protein interactions, new lncRNAs, and new proteins. Case study further indicates that PMKDN can be used as an effective tool for lncRNA-protein interaction prediction.

Prediction of LncRNA Encoded Small Peptides in Glioma and The Oligomer Channel Functional Analysis Using in Silico Approaches

Prediction of LncRNA Encoded Small Peptides in Glioma and The Oligomer Channel Functional Analysis Using in Silico Approaches