The RIKEN integrated database of mammals (http://scinets.org/db/mammal) is the official undertaking to integrate its mammalian databases produced from multiple large-scale programs that have been promoted by the institute. The database integrates not only RIKEN's original databases, such as FANTOM, the ENU mutagenesis program, the RIKEN Cerebellar Development Transcriptome Database and the Bioresource Database, but also imported data from public databases, such as Ensembl, MGI and biomedical ontologies. Our integrated database has been implemented on the infrastructure of publication medium for databases, termed SciNetS/SciNeS, or the Scientists' Networking System, where the data and metadata are structured as a semantic web and are downloadable in various standardized formats. The top-level ontology-based implementation of mammal-related data directly integrates the representative knowledge and individual data records in existing databases to ensure advanced cross-database searches and reduced unevenness of the data management operations. Through the development of this database, we propose a novel methodology for the development of standardized comprehensive management of heterogeneous data sets in multiple databases to improve the sustainability, accessibility, utility and publicity of the data of biomedical information.

The generation of myeloid cells from their progenitors is regulated at the level of transcription by combinatorial control of key transcription factors influencing cell-fate choice. To unravel the global dynamics of this process at the transcript level, we generated transcription profiles for 91 human cell types of myeloid origin by use of CAGE profiling. The CAGE sequencing of these samples has allowed us to investigate diverse aspects of transcription control during myelopoiesis, such as identification of novel transcription factors, miRNAs, and noncoding RNAs specific to the myeloid lineage. We further reconstructed a transcription regulatory network by clustering coexpressed transcripts and associating them with enriched cis-regulatory motifs. With the use of the bidirectional expression as a proxy for enhancers, we predicted over 2000 novel enhancers, including an enhancer 38 kb downstream of IRF8 and an intronic enhancer in the KIT gene locus. Finally, we highlighted relevance of these data to dissect transcription dynamics during progressive maturation of granulocyte precursors. A multifaceted analysis of the myeloid transcriptome is made available (www.myeloidome.roslin.ed.ac.uk). This high-quality dataset provides a powerful resource to study transcriptional regulation during myelopoiesis and to infer the likely functions of unannotated genes in human innate immunity.

The analysis of CAGE (Cap Analysis of Gene Expression) time-course has been proposed by the FANTOM5 Consortium to extend the understanding of the sequence of events facilitating cell state transition at the level of promoter regulation. To identify the most prominent transcriptional regulations induced by growth factors in human breast cancer, we apply here the Complexity Invariant Dynamic Time Warping motif EnRichment (CIDER) analysis approach to the CAGE time-course datasets of MCF-7 cells stimulated by epidermal growth factor (EGF) or heregulin (HRG). We identify a multi-level cascade of regulations rooted by the Serum Response Factor (SRF) transcription factor, connecting the MAPK-mediated transduction of the HRG stimulus to the negative regulation of the MAPK pathway by the members of the DUSP family phosphatases. The finding confirms the known primary role of FOS and FOSL1, members of AP-1 family, in shaping gene expression in response to HRG induction. Moreover, we identify a new potential regulation of DUSP5 and RARA (known to antagonize the transcriptional regulation induced by the estrogen receptors) by the activity of the AP-1 complex, specific to HRG response. The results indicate that a divergence in AP-1 regulation determines cellular changes of breast cancer cells stimulated by ErbB receptors.

The BioMart Community Portal (www.biomart.org) is a community-driven effort to provide a unified interface to biomedical databases that are distributed worldwide. The portal provides access to numerous database projects supported by 30 scientific organizations. It includes over 800 different biological datasets spanning genomics, proteomics, model organisms, cancer data, ontology information and more. All resources available through the portal are independently administered and funded by their host organizations. The BioMart data federation technology provides a unified interface to all the available data. The latest version of the portal comes with many new databases that have been created by our ever-growing community. It also comes with better support and extensibility for data analysis and visualization tools. A new addition to our toolbox, the enrichment analysis tool is now accessible through graphical and web service interface. The BioMart community portal averages over one million requests per day. Building on this level of service and the wealth of information that has become available, the BioMart Community Portal has introduced a new, more scalable and cheaper alternative to the large data stores maintained by specialized organizations.

The corneal endothelium is a monolayer of hexagonal corneal endothelial cells (CECs) on the inner surface of the cornea. CECs are critical in maintaining corneal transparency through their barrier and pump functions. CECs in vivo have a limited capacity in proliferation, and loss of a significant number of CECs results in corneal edema called bullous keratopathy which can lead to severe visual loss. Corneal transplantation is the most effective method to treat corneal endothelial dysfunction, where it suffers from donor shortage. Therefore, regeneration of CECs from other cell types attracts increasing interests, and specific markers of CECs are crucial to identify actual CECs. However, the currently used markers are far from satisfactory because of their non-specific expression in other cell types. Here, we explored molecular markers to discriminate CECs from other cell types in the human body by integrating the published RNA-seq data of CECs and the FANTOM5 atlas representing diverse range of cell types based on expression patterns. We identified five genes, CLRN1, MRGPRX3, HTR1D, GRIP1 and ZP4 as novel markers of CECs, and the specificities of these genes were successfully confirmed by independent experiments at both the RNA and protein levels. Notably none of them have been documented in the context of CEC function. These markers could be useful for the purification of actual CECs, and also available for the evaluation of the products derived from other cell types. Our results demonstrate an effective approach to identify molecular markers for CECs and open the door for the regeneration of CECs in vitro.

Although it is generally accepted that cellular differentiation requires changes to transcriptional networks, dynamic regulation of promoters and enhancers at specific sets of genes has not been previously studied en masse. Exploiting the fact that active promoters and enhancers are transcribed, we simultaneously measured their activity in 19 human and 14 mouse time courses covering a wide range of cell types and biological stimuli. Enhancer RNAs, then messenger RNAs encoding transcription factors, dominated the earliest responses. Binding sites for key lineage transcription factors were simultaneously overrepresented in enhancers and promoters active in each cellular system. Our data support a highly generalizable model in which enhancer transcription is the earliest event in successive waves of transcriptional change during cellular differentiation or activation.

Deciphering the most common modes by which chromatin regulates transcription, and how this is related to cellular status and processes is an important task for improving our understanding of human cellular biology. The FANTOM5 and ENCODE projects represent two independent large scale efforts to map regulatory and transcriptional features to the human genome. Here we investigate chromatin features around a comprehensive set of transcription start sites in four cell lines by integrating data from these two projects.

Functionality of the non-coding transcripts encoded by the human genome is the coveted goal of the modern genomics research. While commonly relied on the classical methods of forward genetics, integration of different genomics datasets in a global Systems Biology fashion presents a more productive avenue of achieving this very complex aim. Here we report application of a Systems Biology-based approach to dissect functionality of a newly identified vast class of very long intergenic non-coding (vlinc) RNAs. Using highly quantitative FANTOM5 CAGE dataset, we show that these RNAs could be grouped into 1542 novel human genes based on analysis of insulators that we show here indeed function as genomic barrier elements. We show that vlinc RNAs genes likely function in cisto activate nearby genes. This effect while most pronounced in closely spaced vlinc RNA-gene pairs can be detected over relatively large genomic distances. Furthermore, we identified 101 vlinc RNA genes likely involved in early embryogenesis based on patterns of their expression and regulation. We also found another 109 such genes potentially involved in cellular functions also happening at early stages of development such as proliferation, migration and apoptosis. Overall, we show that Systems Biology-based methods have great promise for functional annotation of non-coding RNAs.

VEGF-C/VEGFR-3 signaling plays a central role in lymphatic development, regulating the budding of lymphatic progenitor cells from embryonic veins and maintaining the expression of PROX1 during later developmental stages. However, how VEGFR-3 activation translates into target gene expression is still not completely understood. We used cap analysis of gene expression (CAGE) RNA sequencing to characterize the transcriptional changes invoked by VEGF-C in LECs and to identify the transcription factors (TFs) involved. We found that MAFB, a TF involved in differentiation of various cell types, is rapidly induced and activated by VEGF-C. MAFB induced expression of PROX1 as well as other TFs and markers of differentiated LECs, indicating a role in the maintenance of the mature LEC phenotype. Correspondingly, E14.5 Mafb(-/-) embryos showed impaired lymphatic patterning in the skin. This suggests that MAFB is an important TF involved in lymphangiogenesis.

The number of mammalian transcripts identified by full-length cDNA projects and genome sequencing projects is increasing remarkably. Clustering them into a strictly nonredundant and comprehensive set provides a platform for functional analysis of the transcriptome and proteome, but the quality of the clustering and predictive usefulness have previously required manual curation to identify truncated transcripts and inappropriate clustering of closely related sequences. A Representative Transcript and Protein Sets (RTPS) pipeline was previously designed to identify the nonredundant and comprehensive set of mouse transcripts based on clustering of a large mouse full-length cDNA set (FANTOM2). Here we propose an alternative method that is more robust, requires less manual curation, and is applicable to other organisms in addition to mouse. RTPSs of human, mouse, and rat have been produced by this method and used for validation. Their comprehensiveness and quality are discussed by comparison with other clustering approaches. The RTPSs are available at .

Genetic variants underlying complex traits, including disease susceptibility, are enriched within the transcriptional regulatory elements, promoters and enhancers. There is emerging evidence that regulatory elements associated with particular traits or diseases share similar patterns of transcriptional activity. Accordingly, shared transcriptional activity (coexpression) may help prioritise loci associated with a given trait, and help to identify underlying biological processes. Using cap analysis of gene expression (CAGE) profiles of promoter- and enhancer-derived RNAs across 1824 human samples, we have analysed coexpression of RNAs originating from trait-associated regulatory regions using a novel quantitative method (network density analysis; NDA). For most traits studied, phenotype-associated variants in regulatory regions were linked to tightly-coexpressed networks that are likely to share important functional characteristics. Coexpression provides a new signal, independent of phenotype association, to enable fine mapping of causative variants. The NDA coexpression approach identifies new genetic variants associated with specific traits, including an association between the regulation of the OCT1 cation transporter and genetic variants underlying circulating cholesterol levels. NDA strongly implicates particular cell types and tissues in disease pathogenesis. For example, distinct groupings of disease-associated regulatory regions implicate two distinct biological processes in the pathogenesis of ulcerative colitis; a further two separate processes are implicated in Crohn's disease. Thus, our functional analysis of genetic predisposition to disease defines new distinct disease endotypes. We predict that patients with a preponderance of susceptibility variants in each group are likely to respond differently to pharmacological therapy. Together, these findings enable a deeper biological understanding of the causal basis of complex traits.

The latest project from the FANTOM consortium, an international collaborative effort initiated by RIKEN, generated atlases of transcriptomes, in particular promoters, transcribed enhancers, and long-noncoding RNAs, across a diverse set of mammalian cell types. Here, we introduce the FANTOM5 collection, bringing together data descriptors, articles and analyses of FANTOM5 data published across the Nature Research journals. Associated data are openly available for reuse by all.

The FANTOM5 expression atlas is a quantitative measurement of the activity of nearly 200,000 promoter regions across nearly 2,000 different human primary cells, tissue types and cell lines. Generation of this atlas was made possible by the use of CAGE, an experimental approach to localise transcription start sites at single-nucleotide resolution by sequencing the 5' ends of capped RNAs after their conversion to cDNAs. While 50% of CAGE-defined promoter regions could be confidently associated to adjacent transcriptional units, nearly 100,000 promoter regions remained gene-orphan. To address this, we used the CAGEscan method, in which random-primed 5'-cDNAs are paired-end sequenced. Pairs starting in the same region are assembled in transcript models called CAGEscan clusters. Here, we present the production and quality control of CAGEscan libraries from 56 FANTOM5 RNA sources, which enhances the FANTOM5 expression atlas by providing experimental evidence associating core promoter regions with their cognate transcripts.

For endometrial cancer patients, lymphadenectomy is recommended to exclude rarely metastasized cancer cells. This procedure is performed even in patients with low risk of recurrence despite the risk of complications such as lymphedema. A method to accurately identify cases with no lymph node metastases (LN-) before lymphadenectomy is therefore highly required. We approached this clinical problem by examining primary lesions of endometrial cancers with CAGE (Cap Analysis Gene Expression), which quantifies promoter-level expression across the genome. Fourteen profiles delineated distinct transcriptional networks between LN + and LN- cases, within those classified as having the low or intermediate risk of recurrence. Subsequent quantitative reverse transcription polymerase chain reaction (qRT-PCR) analyses of 115 primary tumors showed SEMA3D mRNA and TACC2 isoforms expressed through a novel promoter as promising biomarkers with high accuracy (area under the receiver operating characteristic curve, 0.929) when used in combination. Our high-resolution transcriptome provided evidence of distinct molecular profiles underlying LN + /LN- status in endometrial cancers, raising the possibility of preoperative diagnosis to reduce unnecessary operations in patients with minimum recurrence risk.

Discrimination of Philadelphia-negative myeloproliferative neoplasms (Ph-MPNs) from reactive hypercytosis and myelofibrosis requires a constellation of testing including driver mutation analysis and bone marrow biopsies. We searched for a biomarker that can more easily distinguish Ph-MPNs from reactive hypercytosis and myelofibrosis by using RNA-seq analysis utilizing platelet-rich plasma (PRP)-derived RNAs from patients with essential thrombocythemia (ET) and reactive thrombocytosis, and CREB3L1 was found to have an extremely high impact in discriminating the two disorders. To validate and further explore the result, expression levels of CREB3L1 in PRP were quantified by reverse-transcription quantitative PCR and compared among patients with ET, other Ph-MPNs, chronic myeloid leukemia (CML), and reactive hypercytosis and myelofibrosis. A CREB3L1 expression cutoff value determined based on PRP of 18 healthy volunteers accurately discriminated 150 driver mutation-positive Ph-MPNs from other entities (71 reactive hypercytosis and myelofibrosis, 6 CML, and 18 healthy volunteers) and showed both sensitivity and specificity of 1.0000. Importantly, CREB3L1 expression levels were significantly higher in ET compared with reactive thrombocytosis (P < .0001), and polycythemia vera compared with reactive erythrocytosis (P < .0001). Pathology-affirmed triple-negative ET (TN-ET) patients were divided into a high- and low-CREB3L1-expression group, and some patients in the low-expression group achieved a spontaneous remission during the clinical course. In conclusion, CREB3L1 analysis has the potential to single-handedly discriminate driver mutation-positive Ph-MPNs from reactive hypercytosis and myelofibrosis, and also may identify a subgroup within TN-ET showing distinct clinical features including spontaneous remission.

The telomerase reverse transcriptase is upregulated in the majority of human cancers and contributes directly to cell transformation. Here we report that hTERT is phosphorylated at threonine 249 during mitosis by the serine/threonine kinase CDK1. Clinicopathological analyses reveal that phosphorylation of hTERT at threonine 249 occurs more frequently in aggressive cancers. Using CRISPR/Cas9 genome editing, we introduce substitution mutations at threonine 249 in the endogenous hTERT locus and find that phosphorylation of threonine 249 is necessary for hTERT-mediated RNA dependent RNA polymerase (RdRP) activity but dispensable for reverse transcriptase and terminal transferase activities. Cap Analysis of Gene Expression (CAGE) demonstrates that hTERT phosphorylation at 249 regulates the expression of specific genes that are necessary for cancer cell proliferation and tumor formation. These observations indicate that phosphorylation at threonine 249 regulates hTERT RdRP and contributes to cancer progression in a telomere independent manner.

MicroRNAs (miRNAs) modulate the post-transcriptional regulation of target genes and are related to biology of complex human traits, but genetic landscape of miRNAs remains largely unknown. Given the strikingly tissue-specific miRNA expression profiles, we here expand a previous method to quantitatively evaluate enrichment of genome-wide association study (GWAS) signals on miRNA-target gene networks (MIGWAS) to further estimate tissue-specific enrichment. Our approach integrates tissue-specific expression profiles of miRNAs (∼1800 miRNAs in 179 cells) with GWAS to test whether polygenic signals enrich in miRNA-target gene networks and whether they fall within specific tissues. We applied MIGWAS to 49 GWASs (nTotal = 3 520 246), and successfully identified biologically relevant tissues. Further, MIGWAS could point miRNAs as candidate biomarkers of the trait. As an illustrative example, we performed differentially expressed miRNA analysis between rheumatoid arthritis (RA) patients and healthy controls (n = 63). We identified novel biomarker miRNAs (e.g. hsa-miR-762) by integrating differentially expressed miRNAs with MIGWAS results for RA, as well as novel associated loci with significant genetic risk (rs56656810 at MIR762 at 16q11; n = 91 482, P = 3.6 × 10-8). Our result highlighted that miRNA-target gene network contributes to human disease genetics in a cell type-specific manner, which could yield an efficient screening of miRNAs as promising biomarkers.

We have fully integrated public chromatin chromatin immunoprecipitation sequencing (ChIP-seq) and DNase-seq data (n > 70,000) derived from six representative model organisms (human, mouse, rat, fruit fly, nematode, and budding yeast), and have devised a data-mining platform-designated ChIP-Atlas (http://chip-atlas.org). ChIP-Atlas is able to show alignment and peak-call results for all public ChIP-seq and DNase-seq data archived in the NCBI Sequence Read Archive (SRA), which encompasses data derived from GEO, ArrayExpress, DDBJ, ENCODE, Roadmap Epigenomics, and the scientific literature. All peak-call data are integrated to visualize multiple histone modifications and binding sites of transcriptional regulators (TRs) at given genomic loci. The integrated data can be further analyzed to show TR-gene and TR-TR interactions, as well as to examine enrichment of protein binding for given multiple genomic coordinates or gene names. ChIP-Atlas is superior to other platforms in terms of data number and functionality for data mining across thousands of ChIP-seq experiments, and it provides insight into gene regulatory networks and epigenetic mechanisms.

Cancer-associated fibroblasts (CAFs) regulate cancer progression through the modulation of extracellular matrix (ECM) and cancer cell adhesion. While undergoing a series of phenotypic changes, CAFs control cancer-stroma interactions through integrin receptor signaling. Here, we isolated CAFs from patients with non-small-cell lung cancer (NSCLC) and examined their gene expression profiles. We identified collagen type XI α1 (COL11A1), integrin α11 (ITGA11), and the ITGA11 major ligand collagen type I α1 (COL1A1) among the 390 genes that were significantly enriched in NSCLC-associated CAFs. Increased ITGA11 expression in cancer stroma was correlated with a poor clinical outcome in patients with NSCLC. Increased expression of fibronectin and collagen type I induced ITGA11 expression in CAFs. The cellular migration of CAFs toward collagen type I and fibronectin was promoted via ERK1/2 signaling, independently of the fibronectin receptor integrin α5β1. Additionally, ERK1/2 signaling induced ITGA11 and COL11A1 expression in cancer stroma. We, therefore, propose that targeting ITGA11 and COL11A1 expressing CAFs to block cancer-stroma interactions may serve as a novel, promising anti-tumor strategy.

Psychotic-like experiences (PLEs) occur occasionally in adolescence and mostly disappear with increasing age. Their presence, if persistent, is considered a robust risk factor for subsequent psychiatric disorders. To date, only a few biological markers have been investigated for persistent PLE prediction. This study identified urinary exosomal microRNAs that can serve as predictive biomarkers for persistent PLEs. This study was part of a population-based biomarker subsample study of the Tokyo Teen Cohort Study. A total of 345 participants aged 13 (baseline) and 14 (follow-up) years underwent PLE assessments by experienced psychiatrists using semi-structured interviews. We defined remitted and persistent PLEs based on longitudinal profiles. We obtained urine at baseline and the expression levels of urinary exosomal miRNAs were compared between 15 individuals with persistent PLEs and 15 age- and sex-matched individuals with remitted PLEs. We constructed a logistic regression model to examine whether miRNA expression levels could predict persistent PLEs. We identified six significant differentially expressed microRNAs, namely hsa-miR-486-5p, hsa-miR-199a-3p, hsa-miR-144-5p, hsa-miR-451a, hsa-miR-143-3p, and hsa-miR-142-3p. The predictive model showed an area under the curve of 0.860 (95% confidence interval: 0.713-0.993) for five-fold cross-validation. We found a subset of urinary exosomal microRNAs that were differentially expressed in persistent PLEs and presented the likelihood that a microRNA-based statistical model could predict them with high accuracy. Therefore, urine exosomal miRNAs may serve as novel biomarkers for the risk of psychiatric disorders.