PLIT: An alignment-free computational tool for identification of long non-coding RNAs in plant transcriptomic datasets

Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs which play a significant role in several biological processes. RNA-seq based transcriptome sequencing has been extensively used for identification of lncRNAs. However, accurate identification of lncRNAs in RNA-seq datasets is crucial for exploring their characteristic functions in the genome as most coding potential computation (CPC) tools fail to accurately identify them in transcriptomic data. Well-known CPC tools such as CPC2, lncScore, CPAT are primarily designed for prediction of lncRNAs based on the GENCODE, NONCODE and CANTATAdb databases. The prediction accuracy of these tools often drops when tested on transcriptomic datasets. This leads to higher false positive results and inaccuracy in the function annotation process. In this study, we present a novel tool, PLIT, for the identification of lncRNAs in plants RNA-seq datasets. PLIT implements a feature selection method based on L1 regularization and iterative Random Forests (iRF) classification for selection of optimal features. Based on sequence and codon-bias features, it classifies the RNA-seq derived FASTA sequences into coding or long non-coding transcripts. Using L1 regularization, 31 optimal features were obtained based on lncRNA and protein-coding transcripts from 8 plant species. The performance of the tool was evaluated on 7 plant RNA-seq datasets using 10-fold cross-validation. The analysis exhibited superior accuracy when evaluated against currently available state-of-the-art CPC tools

The non-coding landscape of head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC) is an aggressive disease marked by frequent recurrence and metastasis and stagnant survival rates. To enhance molecular knowledge of HNSCC and define a non-coding RNA (ncRNA) landscape of the disease, we profiled the transcriptome-wide dysregulation of long non-coding RNA (lncRNA), microRNA (miRNA), and PIWI-interacting RNA (piRNA) using RNA-sequencing data from 422 HNSCC patients in The Cancer Genome Atlas (TCGA). 307 non-coding transcripts differentially expressed in HNSCC were significantly correlated with patient survival, and associated with mutations in TP53, CDKN2A, CASP8, PRDM9, and FBXW7 and copy number variations in chromosomes 3, 5, 7, and 18. We also observed widespread ncRNA correlation to concurrent TP53 and chromosome 3p loss, a compelling predictor of poor prognosis in HNSCCs. Three selected ncRNAs were additionally associated with tumor stage, HPV status, and other clinical characteristics, and modulation of their expression in vitro reveals differential regulation of genes involved in epithelial-mesenchymal transition and apoptotic response. This comprehensive characterization of the HNSCC non-coding transcriptome introduces new layers of understanding for the disease, and nominates a novel panel of transcripts with potential utility as prognostic markers or therapeutic targets

Characterization of the lncRNA transcriptome in mESC-derived motor neurons: Implications for FUS-ALS

Long non-coding RNAs (lncRNAs) are currently recognized as crucial players in nervous system development, function and pathology. In Amyotrophic Lateral Sclerosis (ALS), identification of causative mutations in FUS and TDP-43 or hexanucleotide repeat expansion in C9ORF72 point to the essential role of aberrant RNA metabolism in neurodegeneration. In this study, by taking advantage of an in vitro differentiation system generating mouse motor neurons (MNs) from embryonic stem cells, we identified and characterized the long non-coding transcriptome of MNs. Moreover, by using mutant mouse MNs carrying the equivalent of one of the most severe ALS-associated FUS alleles (P517L), we identified lncRNAs affected by this mutation. Comparative analysis with humanMNs derived in vitro frominduced pluripotent stemcells indicated that candidate lncRNAs are conserved between mouse and human. Our work provides a global view of the long non-coding transcriptome of MN, as a prerequisite toward the comprehension of the still poorly characterized non-coding side ofMNphysiopatholog

Long non-coding RNA SENCR is a positive regulator of ETV2

Department of Biological SciencesAlthough long non-coding RNAs (lncRNAs) have emerged as novel regulator of cell fate and gene expression, the regulation of vascular specific transcription factor by lncRNA in generation of induced endothelial cells (iEndo) has not been studied yet. In this study, ETS variant 2 (ETV2) converts human fibroblasts into iEndo, and smooth muscle and endothelial cell enriched migration/differentiationassociated long non-coding RNA (SENCR) was identified as a regulator of ETV2. iEndo showed similar morphology, endothelial cell markers, and tubular structure formation compared to human umbilical vein endothelial cell (HUVEC). Furthermore, over-expression of SENCR increased ETV2 gene and protein expression by enhancing ETV2 promoter activity through recruitment of PSPC1. This is the first study demonstrates the role of SENCR contributed to ETV2 activation in generation of iEndo.ope

MALAT1 Long Non-Coding RNA: Functional Implications

The mammalian genome is pervasively transcribed and the functional significance of many long non-coding RNA (lncRNA) transcripts are gradually being elucidated. Metastasis Associated Lung Adenocarcinoma Transcript 1 (MALAT1) is one of the most well-studied lncRNAs. MALAT1 is a highly conserved nuclear retained lncRNA that is abundantly expressed in cells and tissues and has been shown to play a role in regulating genes at both the transcriptional and post-transcriptional levels in a context-dependent manner. However, Malat1 has been shown to be dispensable for normal development and viability in mice. Interestingly, accumulating evidence suggests that MALAT1 plays an important role in numerous diseases including cancer. Here, we discuss the current state-of-knowledge in regard to MALAT1 with respect to its function, role in diseases, and the potential therapeutic opportunities for targeting MALAT1 using antisense oligonucleotides and small molecules

RUNX2 associated long non-coding RNA characterization

RUNX2 is a lineage-specific transcription factor (TF) known to promote cancer progression. However, the molecular mechanisms that control RUNX2 expression in cancer remain widely unknown. Long non-coding RNAs (lncRNAs) are a novel class of transcripts that do not code for proteins and are often engaged in gene expression regulation. Using the ENCODE annotation data, we identified a previously uncharacterized family of lncRNAs within the RUNX2 locus, that we named RAIN (RUNX2 Associated Intergenic Non-coding RNA). We showed that RAIN comprises 4 major variants that share a common central region but differ at the 5'- and 3'- ends. The longest isoform (l-RAIN) is nuclear and strongly associated with chromatin, suggesting a role of RAIN in gene expression regulation. Expression analysis in cancer cell lines and patient samples demonstrated that RAIN correlates with RUNX2. Furthermore, RAIN silencing resulted in a significant RUNX2 repression demonstrating that this lncRNA is required for the expression of this TF in cancer. We showed that RAIN promotes RUNX2 expression at least through two distinct mechanisms. Interacting with WDR5 and directing its recruitment to the P2 promoter, RAIN modifies its transcriptional activation status, bursting transcription initiation. In parallel, RAIN sequesters NELFe preventing the binding of the NELF complex to the RUNX2 P2 promoter and restraining its inhibitory function on nascent transcripts elongation. Finally, we investigated the RAIN associated transcriptional profile in thyroid cancer showing that beside RUNX2, this lncRNA controls a panel of cancer associated TFs. Overall, our data characterize the function of novel lncRNA and identify an additional layer in the complex RUNX2 regulation in cancer

Human Cancer Long Non-Coding RNA Transcriptomes

Once thought to be a part of the ‘dark matter’ of the genome, long non-coding RNAs (lncRNAs) are emerging as an integral functional component of the mammalian transcriptome. LncRNAs are a novel class of mRNA-like transcripts which, despite no known protein-coding potential, demonstrate a wide range of structural and functional roles in cellular biology. However, the magnitude of the contribution of lncRNA expression to normal human tissues and cancers has not been investigated in a comprehensive manner. In this study, we compiled 272 human serial analysis of gene expression (SAGE) libraries to delineate lncRNA transcription patterns across a broad spectrum of normal human tissues and cancers. Using a novel lncRNA discovery pipeline we parsed over 24 million SAGE tags and report lncRNA expression profiles across a panel of 26 different normal human tissues and 19 human cancers. Our findings show extensive, tissue-specific lncRNA expression in normal tissues and highly aberrant lncRNA expression in human cancers. Here, we present a first generation atlas for lncRNA profiling in cancer

Long non-coding RNA modifies chromatin: Epigenetic silencing by long non-coding RNAs

Common themes are emerging in the molecular mechanisms of long non-coding RNA-mediated gene repression. Long non-coding RNAs (lncRNAs) participate in targeted gene silencing through chromatin remodelling, nuclear reorganisation, formation of a silencing domain and precise control over the entry of genes into silent compartments. The similarities suggest that these are fundamental processes of transcription regulation governed by lncRNAs. These findings have paved the way for analogous investigations on other lncRNAs and chromatin remodelling enzymes. Here we discuss these common mechanisms and provide our view on other molecules that warrant similar investigations. We also present our concepts on the possible mechanisms that may facilitate the exit of genes from the silencing domains and their potential therapeutic applications. Finally, we point to future areas of research and put forward our recommendations for improvements in resources and applications of existing technologies towards targeted outcomes in this active area of research