Despite the prevalence of long noncoding RNA (lncRNA) genes in eukaryotic genomes, only a small proportion have been examined for biological function. lncRNAdb, available at http://lncrnadb.org, provides users with a comprehensive, manually curated reference database of 287 eukaryotic lncRNAs that have been described independently in the scientific literature. In addition to capturing a great proportion of the recent literature describing functions for individual lncRNAs, lncRNAdb now offers an improved user interface enabling greater accessibility to sequence information, expression data and the literature. The new features in lncRNAdb include the integration of Illumina Body Atlas expression profiles, nucleotide sequence information, a BLAST search tool and easy export of content via direct download or a REST API. lncRNAdb is now endorsed by RNAcentral and is in compliance with the International Nucleotide Sequence Database Collaboration.

The previous decade has seen long non-coding RNAs (lncRNAs) rise from obscurity to being defined as a category of genetic elements, leaving its mark on the field of cancer biology. With the current number of curated lncRNAs increasing by 10,000 in the last five years, the field is moving from annotation of lncRNA expression in various tumours to understanding their importance in the key cancer signalling networks and characteristic behaviours. Here, we summarize the previously identified as well as recently discovered mechanisms of lncRNA function and their roles in the hallmarks of cancer. Furthermore, we identify novel technologies for investigation of lncRNA properties and their function in carcinogenesis, which will be important for their translation to the clinic as novel biomarkers and therapeutic targets.Electronic supplementary materialThe online version of this article (doi:10.1186/s12943-016-0530-6) contains supplementary material, which is available to authorized users.

G.H. contributed to the study design and collection and interpretation of the data. R.P.K. performed the analysis of Circle-seq and whole-genome sequencing. E.R.F. performed the data analysis of the whole-genome sequencing data. I.

Memory B cells (MBCs) and plasma cells (PCs) constitute the two cellular outputs of germinal center (GC) responses that together facilitate long-term humoral immunity. Although expression of the transcription factor BLIMP-1 identifies cells undergoing PC differentiation, no such marker exists for cells committed to the MBC lineage. Here, we report that the chemokine receptor CCR6 uniquely marks MBC precursors in both mouse and human GCs. CCR6 GC B cells were highly enriched within the GC light zone (LZ), were the most quiescent of all GC B cells, exhibited a cell-surface phenotype and gene expression signature indicative of an MBC transition, and possessed the augmented response characteristics of MBCs. MBC precursors within the GC LZ predominantly possessed a low affinity for antigen but also included cells from within the high-affinity pool. These data indicate a fundamental dichotomy between the processes that drive MBC and PC differentiation during GC responses.

RNA-sequencing has become the gold standard for whole-transcriptome gene expression quantification. Multiple algorithms have been developed to derive gene counts from sequencing reads. While a number of benchmarking studies have been conducted, the question remains how individual methods perform at accurately quantifying gene expression levels from RNA-sequencing reads. We performed an independent benchmarking study using RNA-sequencing data from the well established MAQCA and MAQCB reference samples. RNA-sequencing reads were processed using five workflows (Tophat-HTSeq, Tophat-Cufflinks, STAR-HTSeq, Kallisto and Salmon) and resulting gene expression measurements were compared to expression data generated by wet-lab validated qPCR assays for all protein coding genes. All methods showed high gene expression correlations with qPCR data. When comparing gene expression fold changes between MAQCA and MAQCB samples, about 85% of the genes showed consistent results between RNA-sequencing and qPCR data. Of note, each method revealed a small but specific gene set with inconsistent expression measurements. A significant proportion of these method-specific inconsistent genes were reproducibly identified in independent datasets. These genes were typically smaller, had fewer exons, and were lower expressed compared to genes with consistent expression measurements. We propose that careful validation is warranted when evaluating RNA-seq based expression profiles for this specific gene set.Due to the drop in cost of massively parallel sequencing, RNA-sequencing (RNA-seq) has become a viable alternative to gene expression microarrays 1 . Nowadays, RNA-seq is generally considered the gold standard for whole transcriptome gene expression quantification, not only in research but also for clinical applications. Compared to microarrays, RNA-seq has several major advantages. First, no prior knowledge about the content of the transcriptome is required, providing an unbiased view on the ensemble of transcripts in a sample and the possibility of evaluating allelic expression. Second, RNA-seq enables a much more detailed analysis of alternative splicing events. While certain microarray platforms can be used to study alternative splicing 2 , this is typically limited to known isoforms and occurs at much lower resolution. Finally, RNA-seq gene expression measurements tend to cover a much broader dynamic range and can be more sensitive compared to microarrays 3, 4 . Nevertheless, the field of RNA-seq still faces many challenges, especially in terms of data processing and analyses. In contrast to the microarray field, where data processing converged over the years into a well-defined set of broadly accepted workflows, the number of RNA-seq data processing workflows is still increasing, with none accepted as the standard so far. RNA-seq data processing workflows typically come in two different flavours. First, there are methods that align reads directly to a reference genome, followed by quantification of mapped reads (e.g. Tophat-Cufflinks 5...

Tissue damage increases cancer risk through poorly understood mechanisms 1 . In the pancreas, pancreatitis associated with tissue injury collaborates with activating mutations in the Kras oncogene to dramatically accelerate the formation of early neoplastic lesions and ultimately pancreatic cancer 2 , 3 . By integrating genomics, single-cell chromatin assays and spatiotemporally-controlled functional perturbations in autochthonous mouse models, we show that the combination of Kras mutation and tissue damage promotes a unique chromatin state in the pancreatic epithelium that distinguishes neoplastic transformation from normal regeneration and is selected for throughout malignant evolution. This cancer-associated epigenetic state emerges within 48 hours of pancreatic injury, and involves an acinar-to-neoplasia ‘chromatin switch’ that contributes to the early dysregulation of genes defining human pancreatic cancer. Among the genes most rapidly activated upon tissue damage in the pre-malignant pancreatic epithelium is the alarmin cytokine IL-33, which cooperates with mutant Kras in unleashing the epigenetic remodeling program of early neoplasia and neoplastic transformation in the absence of injury. Collectively, our study demonstrates how gene-environment interactions can rapidly produce gene regulatory programs that dictate early neoplastic commitment and provides a molecular framework for understanding the interplay between genetics and environmental cues in cancer initiation.

Despite their abundance, the molecular functions of long non-coding RNAs in mammalian nervous systems remain poorly understood. Here we show that the long non-coding RNA, NEAT1, directly modulates neuronal excitability and is associated with pathological seizure states. Specifically, NEAT1 is dynamically regulated by neuronal activity in vitro and in vivo, binds epilepsy-associated potassium channel-interacting proteins including KCNAB2 and KCNIP1, and induces a neuronal hyper-potentiation phenotype in iPSC-derived human cortical neurons following antisense oligonucleotide knockdown. Next generation sequencing reveals a strong association of NEAT1 with increased ion channel gene expression upon activation of iPSC-derived neurons following NEAT1 knockdown. Furthermore, we show that while NEAT1 is acutely down-regulated in response to neuronal activity, repeated stimulation results in NEAT1 becoming chronically unresponsive in independent in vivo rat model systems relevant to temporal lobe epilepsy. We extended previous studies showing increased NEAT1 expression in resected cortical tissue from high spiking regions of patients suffering from intractable seizures. Our results indicate a role for NEAT1 in modulating human neuronal activity and suggest a novel mechanistic link between an activity-dependent long non-coding RNA and epilepsy.

Esophageal adenocarcinoma (EAC) has one of the fastest increases in incidence of any cancer, along with poor five-year survival rates. Barrett's esophagus (BE) is the main risk factor for EAC; however, the mechanisms driving EAC development remain poorly understood. Here, transcriptomic profiling was performed using RNA-sequencing (RNA-seq) on premalignant and malignant Barrett's tissues to better understand this disease. Machine-learning and network analysis methods were applied to discover novel driver genes for EAC development. Identified gene expression signatures for the distinction of EAC from BE were validated in separate datasets. An extensive analysis of the noncoding RNA (ncRNA) landscape was performed to determine the involvement of novel transcriptomic elements in Barrett's disease and EAC. Finally, transcriptomic mutational investigation of genes that are recurrently mutated in EAC was performed. Through these approaches, novel driver genes were discovered for EAC, which involved key cell cycle and DNA repair genes, such as BRCA1 and PRKDC. A novel 4-gene signature (CTSL, COL17A1, KLF4, and E2F3) was identified, externally validated, and shown to provide excellent distinction of EAC from BE. Furthermore, expression changes were observed in 685 long noncoding RNAs (lncRNA) and a systematic dysregulation of repeat elements across different stages of Barrett's disease, with wide-ranging downregulation of Alu elements in EAC. Mutational investigation revealed distinct pathways activated between EAC tissues with or without TP53 mutations compared with Barrett's disease. In summary, transcriptome sequencing revealed altered expression of numerous novel elements, processes, and networks in EAC and premalignant BE.Implications: This study identified opportunities to improve early detection and treatment of patients with BE and esophageal adenocarcinoma. Mol Cancer Res; 15(11); 1558-69. Ó2017 AACR.