Transcriptomine, a web resource for nuclear receptor signaling transcriptomes.

The nuclear receptor (NR) superfamily of ligand-regulated transcription factors directs ligand- and tissue-specific transcriptomes in myriad developmental, metabolic, immunological, and reproductive processes. The NR signaling field has generated a wealth of genome-wide expression data points, but due to deficits in their accessibility, annotation, and integration, the full potential of these studies has not yet been realized. We searched public gene expression databases and MEDLINE for global transcriptomic datasets relevant to NRs, their ligands, and coregulators. We carried out extensive, deep reannotation of the datasets using controlled vocabularies for RNA Source and regulating molecule and resolved disparate gene identifiers to official gene symbols to facilitate comparison of fold changes and their significance across multiple datasets. We assembled these data points into a database, Transcriptomine (http://www.nursa.org/transcriptomine), that allows for multiple, menu-driven querying strategies of this transcriptomic "superdataset," including single and multiple genes, Gene Ontology terms, disease terms, and uploaded custom gene lists. Experimental variables such as regulating molecule, RNA Source, as well as fold-change and P value cutoff values can be modified, and full data records can be either browsed or downloaded for downstream analysis. We demonstrate the utility of Transcriptomine as a hypothesis generation and validation tool using in silico and experimental use cases. Our resource empowers users to instantly and routinely mine the collective biology of millions of previously disparate transcriptomic data points. By incorporating future transcriptome-wide datasets in the NR signaling field, we anticipate Transcriptomine developing into a powerful resource for the NR- and other signal transduction research communities.

Research resource: dkCOIN, the National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK) consortium interconnectivity network: a pilot program to aggregate research resources generated by multiple research consortia.

The National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK) supports multiple basic science consortia that generate high-content datasets, reagent resources, and methodologies, in the fields of kidney, urology, hematology, digestive, and endocrine diseases, as well as metabolic diseases such as diabetes and obesity. These currently include the Beta Cell Biology Consortium, the Nuclear Receptor Signaling Atlas, the Diabetic Complications Consortium, and the Mouse Metabolic Phenotyping Centers. Recognizing the synergy that would accrue from aggregating information generated and curated by these initiatives in a contiguous informatics network, we created the NIDDK Consortium Interconnectivity Network (dkCOIN; www.dkcoin.org). The goal of this pilot project, organized by the NIDDK, was to establish a single point of access to a toolkit of interconnected resources (datasets, reagents, and protocols) generated from individual consortia that could be readily accessed by biologists of diverse backgrounds and research interests. During the pilot phase of this activity dkCOIN collected nearly 2000 consortium-curated resources, including datasets (functional genomics) and reagents (mouse strains, antibodies, and adenoviral constructs) and built nearly 3000 resource-to-resource connections, thereby demonstrating the feasibility of further extending this database in the future. Thus, dkCOIN promises to be a useful informatics solution for rapidly identifying useful resources generated by participating research consortia.

Research resource: Tissue-specific transcriptomics and cistromics of nuclear receptor signaling: a web research resource.

Nuclear receptors (NRs) are ligand-regulated transcription factors that recruit coregulators and other transcription factors to gene promoters to effect regulation of tissue-specific transcriptomes. The prodigious rate at which the NR signaling field has generated high content gene expression and, more recently, genome-wide location analysis datasets has not been matched by a committed effort to archiving this information for routine access by bench and clinical scientists. As a first step towards this goal, we searched the MEDLINE database for studies, which referenced either expression microarray and/or genome-wide location analysis datasets in which a NR or NR ligand was an experimental variable. A total of 1122 studies encompassing 325 unique organs, tissues, primary cells, and cell lines, 35 NRs, and 91 NR ligands were retrieved and annotated. The data were incorporated into a new section of the Nuclear Receptor Signaling Atlas Molecule Pages, Transcriptomics and Cistromics, for which we designed an intuitive, freely accessible user interface to browse the studies. Each study links to an abstract, the MEDLINE record, and, where available, Gene Expression Omnibus and ArrayExpress records. The resource will be updated on a regular basis to provide a current and comprehensive entrez into the sum of transcriptomic and cistromic research in this field.

Minireview: Evolution of NURSA, the Nuclear Receptor Signaling Atlas.

Nuclear receptors and coregulators are multifaceted players in normal metabolic and homeostatic processes in addition to a variety of disease states including cancer, inflammation, diabetes, obesity, and atherosclerosis. Over the past 7 yr, the Nuclear Receptor Signaling Atlas (NURSA) research consortium has worked toward establishing a discovery-driven platform designed to address key questions concerning the expression, organization, and function of these molecules in a variety of experimental model systems. By applying powerful technologies such as quantitative PCR, high-throughput mass spectrometry, and embryonic stem cell manipulation, we are pursuing these questions in a series of transcriptomics-, proteomics-, and metabolomics-based research projects and resources. The consortium's web site (www.nursa.org) integrates NURSA datasets and existing public datasets with the ultimate goal of furnishing the bench scientist with a comprehensive framework for hypothesis generation, modeling, and testing. We place a strong emphasis on community input into the development of this resource and to this end have published datasets from academic and industrial laboratories, established strategic alliances with Endocrine Society journals, and are developing tools to allow web site users to act as data curators. With the ongoing support of the nuclear receptor and coregulator signaling communities, we believe that NURSA can make a lasting contribution to research in this dynamic field.

GEMS (Gene Expression MetaSignatures), a Web resource for querying meta-analysis of expression microarray datasets: 17beta-estradiol in MCF-7 cells.

With large amounts of public expression microrray data being generated by multiple laboratories, it is a significant task for the bench researcher to routinely identify available datasets, and then to evaluate the collective evidence across these datasets for regulation of a specific gene in a given system. 17beta-Estradiol stimulation of MCF-7 cells is a widely used model in the growth of breast cancer. Although myriad independent studies have profiled the global effects of this hormone on gene expression in these cells, disparate experimental variables and the limited power of the individual studies have combined to restrict the agreement between them as to the specific gene expression signature elicited by this hormone. To address these issues, we have developed a freely accessible Web resource, Gene Expression MetaSignatures (GEMS) that provides the user a consensus for each gene in the system. We conducted a weighted meta-analysis encompassing over 13,000 genes across 10 independent published datasets addressing the effect of 17beta-estradiol on MCF-7 cells at early (3-4 hours) and late (24 hours) time points. In a literature survey of 58 genes previously shown to be regulated by 17beta-estradiol in MCF-7 cells, the meta-analysis combined the statistical power of the underlying datasets to call regulation of these genes with nearly 85% accuracy (false discovery rate-corrected P < 0.05). We anticipate that with future expression microarray dataset contributions from investigators, GEMS will evolve into an important resource for the cancer and nuclear receptor signaling communities.

Novel microRNA candidates and miRNA-mRNA pairs in embryonic stem (ES) cells.

BACKGROUND: MicroRNAS (miRNAS: a class of short non-coding RNAs) are emerging as important agents of post transcriptional gene regulation and integral components of gene networks. MiRNAs have been strongly linked to stem cells, which have a remarkable dual role in development. They can either continuously replenish themselves (self-renewal), or differentiate into cells that execute a limited number of specific actions (pluripotence). METHODOLOGY/PRINCIPAL FINDINGS: In order to identify novel miRNAs from narrow windows of development we carried out an in silico search for micro-conserved elements (MCE) in adult tissue progenitor transcript sequences. A plethora of previously unknown miRNA candidates were revealed including 545 small RNAs that are enriched in embryonic stem (ES) cells over adult cells. Approximately 20% of these novel candidates are down-regulated in ES (Dicer(-/-)) ES cells that are impaired in miRNA maturation. The ES-enriched miRNA candidates exhibit distinct and opposite expression trends from mmu-mirs (an abundant class in adult tissues) during retinoic acid (RA)-induced ES cell differentiation. Significant perturbation of trends is found in both miRNAs and novel candidates in ES (GCNF(-/-)) cells, which display loss of repression of pluripotence genes upon differentiation. CONCLUSION/SIGNIFICANCE: Combining expression profile information with miRNA target prediction, we identified miRNA-mRNA pairs that correlate with ES cell pluripotence and differentiation. Perturbation of these pairs in the ES (GCNF(-/-)) mutant suggests a role for miRNAs in the core regulatory networks underlying ES cell self-renewal, pluripotence and differentiation.

Nuclear Receptor Signaling Atlas (www.nursa.org): hyperlinking the nuclear receptor signaling community.

The nuclear receptor signaling (NRS) field has generated a substantial body of information on nuclear receptors, their ligands and coregulators, with the ultimate goal of constructing coherent models of the biological and clinical significance of these molecules. As a component of the Nuclear Receptor Signaling Atlas (NURSA)--the development of a functional atlas of nuclear receptor biology--the NURSA Bioinformatics Resource is developing a strategy to organize and integrate legacy and future information on these molecules in a single web-based resource (www.nursa.org). This entails parallel efforts of (i) developing an appropriate software framework for handling datasets from NURSA laboratories and (ii) designing strategies for the curation and presentation of public data relevant to NRS. To illustrate our approach, we have described here in detail the development of a web-based interface for the NURSA quantitative PCR nuclear receptor expression dataset, incorporating bioinformatics analysis which provides novel perspectives on functional relationships between these molecules. We anticipate that the free and open access of the community to a platform for data mining and hypothesis generation strategies will be a significant contribution to the progress of research in this field.