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Background: Discovery-scale omics datasets relevant to thyroid receptors (TRs) and their physiological and synthetic bioactive small-molecule ligands allow for genome-wide interrogation of TR-regulated genes. These datasets have considerable collective value as a reference resource to allow researchers to routinely generate hypotheses addressing the mechanisms underlying the cell biology and physiology of TR signaling in normal and disease states. Methods: Here, we searched the Gene Expression Omnibus database to identify a population of publicly archived transcriptomic datasets involving genetic or pharmacological manipulation of either TR isoform in a mouse tissue or cell line. After initial quality control, samples were organized into contrasts (experiments), and transcript differential expression values and associated measures of significance were generated and committed to a consensome (for consensus omics) meta-analysis pipeline. To gain insight into tissue-selective functions of TRs, we generated liver- and central nervous system (CNS)-specific consensomes and identified evidence for genes that were selectively responsive to TR signaling in each organ. Results: The TR transcriptomic consensome ranks genes based on the frequency of their significant differential expression over the entire group of experiments. The TR consensome assigns elevated rankings both to known TR-regulated genes and to genes previously uncharacterized as TR-regulated, which shed mechanistic light on known cellular and physiological roles of TR signaling in different organs. We identify evidence for unreported genomic targets of TR signaling for which it exhibits strikingly distinct regulatory preferences in the liver and CNS. Moreover, the intersection of the TR consensome with consensomes for other cellular receptors sheds light on transcripts potentially mediating crosstalk between TRs and these other signaling paradigms. Conclusions: The mouse TR datasets and consensomes are freely available in the Signaling Pathways Project website for hypothesis generation, data validation, and modeling of novel mechanisms of TR regulation of gene expression. Our results demonstrate the insights into the mechanistic basis of thyroid hormone action that can arise from an ongoing commitment on the part of the research community to the deposition of discovery-scale datasets.

Mining of integrated public transcriptomic and ChIP-Seq (cistromic) datasets can illuminate functions of mammalian cellular signaling pathways not yet explored in the research literature. Here, we designed a web knowledgebase, the Signaling Pathways Project (SPP), which incorporates community classifications of signaling pathway nodes (receptors, enzymes, transcription factors and co-nodes) and their cognate bioactive small molecules. We then mapped over 10,000 public transcriptomic or cistromic experiments to their pathway node or biosample of study. To enable prediction of pathway node-gene target transcriptional regulatory relationships through SPP, we generated consensus 'omics signatures, or consensomes, which ranked genes based on measures of their significant differential expression or promoter occupancy across transcriptomic or cistromic experiments mapped to a specific node family. Consensomes were validated using alignment with canonical literature knowledge, gene target-level integration of transcriptomic and cistromic data points, and in bench experiments confirming previously uncharacterized node-gene target regulatory relationships. To expose the SPP knowledgebase to researchers, a web browser interface was designed that accommodates numerous routine data mining strategies. SPP is freely accessible at https://www.signalingpathways.org .

Nuclear receptor (NR) signaling pathways impact cellular function in a broad variety of tissues in both normal physiology and disease states. The complex tissue-specific biology of these pathways is an enduring impediment to the development of clinical NR small-molecule modulators that combine therapeutically desirable effects in specific target tissues with suppression of off-target effects in other tissues. Supporting the important primary research in this area is a variety of web-based resources that assist researchers in gaining an appreciation of the molecular determinants of the pharmacology of a NR pathway in a given tissue. In this study, selected representative examples of these tools are reviewed, along with discussions on how current and future generations of tools might optimally adapt to the future of NR signaling research., (Copyright © 2016 by The American Society for Pharmacology and Experimental Therapeutics.)

Signaling pathways involving nuclear receptors (NRs), their ligands and coregulators, regulate tissue-specific transcriptomes in diverse processes, including development, metabolism, reproduction, the immune response and neuronal function, as well as in their associated pathologies. The Nuclear Receptor Signaling Atlas (NURSA) is a Consortium focused around a Hub website (www.nursa.org) that annotates and integrates diverse 'omics datasets originating from the published literature and NURSA-funded Data Source Projects (NDSPs). These datasets are then exposed to the scientific community on an Open Access basis through user-friendly data browsing and search interfaces. Here, we describe the redesign of the Hub, version 3.0, to deploy "Web 2.0" technologies and add richer, more diverse content. The Molecule Pages, which aggregate information relevant to NR signaling pathways from myriad external databases, have been enhanced to include resources for basic scientists, such as post-translational modification sites and targeting miRNAs, and for clinicians, such as clinical trials. A portal to NURSA's Open Access, PubMed-indexed journal Nuclear Receptor Signaling has been added to facilitate manuscript submissions. Datasets and information on reagents generated by NDSPs are available, as is information concerning periodic new NDSP funding solicitations. Finally, the new website integrates the Transcriptomine analysis tool, which allows for mining of millions of richly annotated public transcriptomic data points in the field, providing an environment for dataset re-use and citation, bench data validation and hypothesis generation. We anticipate that this new release of the NURSA database will have tangible, long term benefits for both basic and clinical research in this field.

The field of nuclear receptor and coregulator signaling has grown into one of the most active and interdisciplinary in eukaryotic biology. Papers in this field are spread widely across a vast number of journals, which complicates the task of investigators in keeping current with the literature in the field. In 2003, we launched Nuclear Receptor Signaling as an Open Access reviews, perspectives and methods journal for the nuclear receptor signaling field. Building on its success and impact on the community, we have added primary research and dataset articles to this list of article categories, and we now announce the re-launch of the journal this month. Here we will summarize the rationale that informed the creation and expansion of the journal, and discuss the possibilities for its future development.

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.

Nuclear receptors (NRs) are a superfamily of ligand-regulated transcription factors that interact with coregulators and other transcription factors to direct tissue-specific programs of gene expression. Recent years have witnessed a rapid acceleration of the output of high-content data platforms in this field, generating discovery-driven datasets that have collectively described: the organization of the NR superfamily (phylogenomics); the expression patterns of NRs, coregulators and their target genes (transcriptomics); ligand- and tissue-specific functional NR and coregulator sites in DNA (cistromics); the organization of nuclear receptors and coregulators into higher order complexes (proteomics); and their downstream effects on homeostasis and metabolism (metabolomics). Significant bioinformatics challenges lie ahead both in the integration of this information into meaningful models of NR and coregulator biology, as well as in the archiving and communication of datasets to the global nuclear receptor signaling community. While holding great promise for the field, the ascendancy of discovery-driven research in this field brings with it a collective responsibility for researchers, publishers and funding agencies alike to ensure the effective archiving and management of these data. This review will discuss factors lying behind the increasing impact of discovery-driven research, examples of high-content datasets and their bioinformatic analysis, as well as a summary of currently curated web resources in this field. This article is part of a Special Issue entitled: Translating nuclear receptors from health to disease., (Copyright © 2010 Elsevier B.V. All rights reserved.)

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.

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.

This interactive online course sketches some of the landmark studies and discoveries in the field of nuclear receptors since the field's inception. Although the course is primarily focused at advanced life sciences students and scientists from other disciplines wishing to become acquainted with the field, it is likely to also be a useful resource for anyone associated with the discipline of nuclear receptor signaling. This animated course was created by members of the Nuclear Receptor Signaling Atlas (NURSA; www.nursa.org), which is an NIH-funded research consortium formed with the aim of providing a repository of nuclear receptor-related data for use of the scientific community.