Long noncoding RNAs (lncRNAs) regulate gene expression by association with chromatin, but how they target chromatin remains poorly understood. We have used chromatin RNA immunoprecipitation-coupled high-throughput sequencing to identify 276 lncRNAs enriched in repressive chromatin from breast cancer cells. Using one of the chromatin-interacting lncRNAs, MEG3, we explore the mechanisms by which lncRNAs target chromatin. Here we show that MEG3 and EZH2 share common target genes, including the TGF-β pathway genes. Genome-wide mapping of MEG3 binding sites reveals that MEG3 modulates the activity of TGF-β genes by binding to distal regulatory elements. MEG3 binding sites have GA-rich sequences, which guide MEG3 to the chromatin through RNA–DNA triplex formation. We have found that RNA–DNA triplex structures are widespread and are present over the MEG3 binding sites associated with the TGF-β pathway genes. Our findings suggest that RNA–DNA triplex formation could be a general characteristic of target gene recognition by the chromatin-interacting lncRNAs.

Neuroblastoma is an embryonal tumor of the sympathetic nervous system and the most common extracranial tumor of childhood. By sequencing transcriptomes of low- and high-risk neuroblastomas, we detected differentially expressed annotated and nonannotated long noncoding RNAs (lncRNAs). We identified a lncRNA neuroblastoma associated transcript-1 (NBAT-1) as a biomarker significantly predicting clinical outcome of neuroblastoma. CpG methylation and a high-risk neuroblastoma associated SNP on chromosome 6p22 functionally contribute to NBAT-1 differential expression. Loss of NBAT-1 increases cellular proliferation and invasion. It controls these processes via epigenetic silencing of target genes. NBAT-1 loss affects neuronal differentiation through activation of the neuronal-specific transcription factor NRSF/REST. Thus, loss of NBAT-1 contributes to aggressive neuroblastoma by increasing proliferation and impairing differentiation of neuronal precursors.

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Trait-associated loci often map to genomic regions encoding long noncoding RNAs (lncRNAs), but the role of these lncRNAs in disease etiology is largely unexplored. We show that a pair of sense/antisense lncRNA (6p22lncRNAs) encoded by CASC15 and NBAT1 located at the neuroblastoma (NB) risk-associated 6p22.3 locus are tumor suppressors and show reduced expression in high-risk NBs. Loss of functional synergy between 6p22lncRNAs results in an undifferentiated state that is maintained by a gene-regulatory network, including SOX9 located on 17q, a region frequently gained in NB. 6p22lncRNAs regulate SOX9 expression by controlling CHD7 stability via modulating the cellular localization of USP36, encoded by another 17q gene. This regulatory nexus between 6p22.3 and 17q regions may lead to potential NB treatment strategies.

Despite improvement in our understanding of long noncoding RNAs (lncRNAs) role in cancer, efforts to find clinically relevant cancer-associated lncRNAs are still lacking. Here, using nascent RNA capture sequencing, we identify 1145 temporally expressed S-phase-enriched lncRNAs. Among these, 570 lncRNAs show significant differential expression in at least one tumor type across TCGA data sets. Systematic clinical investigation of 14 Pan-Cancer data sets identified 633 independent prognostic markers. Silencing of the top differentially expressed and clinically relevant S-phase-enriched lncRNAs in several cancer models affects crucial cancer cell hallmarks. Mechanistic investigations on SCAT7 in multiple cancer types reveal that it interacts with hnRNPK/YBX1 complex and affects cancer cell hallmarks through the regulation of FGF/FGFR and its downstream PI3K/AKT and MAPK pathways. We also implement a LNA-antisense oligo-based strategy to treat cancer cell line and patient-derived tumor (PDX) xenografts. Thus, this study provides a comprehensive list of lncRNA-based oncogenic drivers with potential prognostic value.

BackgroundHigh-throughput technologies such as ChIP-sequencing, RNA-sequencing, DNA sequencing and quantitative metabolomics generate a huge volume of data. Researchers often rely on functional enrichment tools to interpret the biological significance of the affected genes from these high-throughput studies. However, currently available functional enrichment tools need to be updated frequently to adapt to new entries from the functional database repositories. Hence there is a need for a simplified tool that can perform functional enrichment analysis by using updated information directly from the source databases such as KEGG, Reactome or Gene Ontology etc.ResultsIn this study, we focused on designing a command-line tool called GeneSCF (Gene Set Clustering based on Functional annotations), that can predict the functionally relevant biological information for a set of genes in a real-time updated manner. It is designed to handle information from more than 4000 organisms from freely available prominent functional databases like KEGG, Reactome and Gene Ontology. We successfully employed our tool on two of published datasets to predict the biologically relevant functional information. The core features of this tool were tested on Linux machines without the need for installation of more dependencies.ConclusionsGeneSCF is more reliable compared to other enrichment tools because of its ability to use reference functional databases in real-time to perform enrichment analysis. It is an easy-to-integrate tool with other pipelines available for downstream analysis of high-throughput data. More importantly, GeneSCF can run multiple gene lists simultaneously on different organisms thereby saving time for the users. Since the tool is designed to be ready-to-use, there is no need for any complex compilation and installation procedures.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1250-z) contains supplementary material, which is available to authorized users.

Abbreviations: Ethynyl Uridine, EtUTranscriptional events during S-phase are critical for cell cycle progression. Here, by using a nascent RNA capture assay coupled with high-throughput sequencing, we determined the temporal patterns of transcriptional events that occur during S-phase. We show that genes involved in critical S-phase-specific biological processes such as nucleosome assembly and DNA repair have temporal transcription patterns across S-phase that are not evident from total RNA levels. By comparing transcription timing with replication timing in S-phase, we show that early replicating genes show increased transcription late in S-phase whereas late replicating genes are predominantly transcribed early in S-phase. Global anti-correlation between replication and transcription timing was observed only based on nascent RNA but not total RNA. Our data provides a detailed view of ongoing transcriptional events during the S-phase of cell cycle, and supports that transcription and replication are temporally separated.

BackgroundMethyl-CpG-binding domain protein enriched genome-wide sequencing (MBD-Seq) is a robust and powerful method for analyzing methylated CpG-rich regions with complete genome-wide coverage. In chronic lymphocytic leukemia (CLL), the role of CpG methylated regions associated with transcribed long noncoding RNAs (lncRNA) and repetitive genomic elements are poorly understood. Based on MBD-Seq, we characterized the global methylation profile of high CpG-rich regions in different CLL prognostic subgroups based on IGHV mutational status.ResultsOur study identified 5800 hypermethylated and 12,570 hypomethylated CLL-specific differentially methylated genes (cllDMGs) compared to normal controls. From cllDMGs, 40 % of hypermethylated and 60 % of hypomethylated genes were mapped to noncoding RNAs. In addition, we found that the major repetitive elements such as short interspersed elements (SINE) and long interspersed elements (LINE) have a high percentage of cllDMRs (differentially methylated regions) in IGHV subgroups compared to normal controls. Finally, two novel lncRNAs (hypermethylated CRNDE and hypomethylated AC012065.7) were validated in an independent CLL sample cohort (48 samples) compared with 6 normal sorted B cell samples using quantitative pyrosequencing analysis. The methylation levels showed an inverse correlation to gene expression levels analyzed by real-time quantitative PCR. Notably, survival analysis revealed that hypermethylation of CRNDE and hypomethylation of AC012065.7 correlated with an inferior outcome.ConclusionsThus, our comprehensive methylation analysis by MBD-Seq provided novel hyper and hypomethylated long noncoding RNAs, repetitive elements, along with protein coding genes as potential epigenetic-based CLL-signature genes involved in disease pathogenesis and prognosis.Electronic supplementary materialThe online version of this article (doi:10.1186/s13148-016-0274-6) contains supplementary material, which is available to authorized users.