Research ResourceNuclear Receptors

Discovering relationships between nuclear receptor signaling pathways, genes, and tissues in Transcriptomine

See allHide authors and affiliations

Sci. Signal.  25 Apr 2017:
Vol. 10, Issue 476, eaah6275
DOI: 10.1126/scisignal.aah6275
  • Fig. 1 Transcriptomine enables a cycle of data set discovery, reuse, and attribution to illuminate uncharacterized biology of NR signaling pathways.

    (A to H) The UI is designed to allow for seamless bidirectional navigation between browsable Data Set Pages and gene Regulation Reports. The Data Set Pages (browsable at the Data Set Directory) link to associated journal articles (A) to extend the value of the original study. The Regulation Report is accessible from the data set pages (B), through links embedded in external gene and small-molecule knowledge databases (C), or directly through a user-configurable query form (D) and enables hypothesis generation through visualization of pathway-gene-tissue relationships. Regulation Report data points link to detailed contextual windows (E) that, in turn, close the loop back to the parental data set (F). Finally, the data set pages enable one-click citation in manuscripts and research proposals (G), which, in turn, drives further discovery of data set citations in article reference lists (H).

  • Fig. 2 Breakdown of Transcriptomine data sets by signaling pathway and physiological system and organ.

    (A) Data sets relevant to ERs and estrogens signaling constitute the largest pathway class in Transcriptomine, followed by, in descending order, studies of signaling by PPARs and fatty acids, GR and glucocorticoids, and AR and androgens. Reflecting cross-talk between NRs and cytoplasmic kinase pathways (141), data sets involving manipulations of cell surface receptors, signaling enzymes, and non-NR transcription factors are an expanding sector of the Transcriptomine data set library. FXR, farnesoid X receptor; TRs, thyroid hormone receptors; VDR, vitamin D3 receptor; CAR, constitutive androstane receptor; PXR, pregnane X receptor; ERR, ER-related receptor; RARs, retinoic acid receptors. (B) Data sets involving female reproductive and metabolic tissue model systems constitute nearly two-thirds of the database. The prominence of female reproductive biosamples reflects the popularity of mammary epithelial cell line models, whereas the large number of metabolic biosamples is due, in part, to our curation of data sets emerging from a TG-GATEs (142), a large-scale toxicotranscriptomic screen in liver and kidney model systems. GI, gastrointestinal; CNS, central nervous system; PNS, peripheral nervous system; UC, umbilical cord.

  • Fig. 3 Use Cases illustrating development of research hypotheses in Transcriptomine.

    Use Case 1: ER and estrogen signaling pathway regulates the UPR. KO, knockout. Use Case 2: Regulation of 3T3-L1 adipogenesis by the PPARγ/PPARG and RORα/RORA signaling pathways involves antagonistic regulation of gap junction formation. Use Case 3: Combination Rev-erbAα/NR1D1 agonism and MET/HGF antagonism in chemoresistant gastric cancer. Use Case 4: The spermine oxidase (SMOX) gene is regulated by multiple NR signaling pathways in different physiological contexts. All mechanistic relationships are inferred from data points in Transcriptomine data sets. Use Case parameters and corresponding search results are contained in data file S1. Direct links to relevant gene Regulation Reports are embedded in the respective sections in the main text.

Supplementary Materials

  • www.sciencesignaling.org/cgi/content/full/10/476/eaah6275/DC1

    Fig. S1. The NURSA data set page.

    Fig. S2. Transcriptomine query form.

    Fig. S3. Transcriptomine GREB1 Regulation Report (Pathway view).

    Fig. S4. Transcriptomine CA12 Regulation Report (Biosample view).

    Fig. S5. FCD window.

    Fig. S6. Corroborating receptor promoter binding evidence from the GTRD.

    Table S1. Receptor and small-molecule signaling pathway mappings.

    Table S2. Cell line and tissue biosample mappings to physiological systems and organs.

    Table S3. Examples of animal and cell models and clinical data sets.

    Table S4. Experiment naming convention.

    Table S5. Nonstandard abbreviations in NURSA experiment names.

    Data file S1. Use Case Query parameters and search results.

  • Supplementary Materials for:

    Discovering relationships between nuclear receptor signaling pathways, genes, and tissues in Transcriptomine

    Lauren B. Becnel, Scott A. Ochsner, Yolanda F. Darlington, Apollo McOwiti, Wasula H. Kankanamge, Michael Dehart, Alexey Naumov, Neil J. McKenna*

    *Corresponding author. Email: nmckenna{at}bcm.edu

    This PDF file includes:

    • Fig. S1. The NURSA data set page.
    • Fig. S2. Transcriptomine query form.
    • Fig. S3. Transcriptomine GREB1 Regulation Report (Pathway view).
    • Fig. S4. Transcriptomine CA12 Regulation Report (Biosample view).
    • Fig. S5. FCD window.
    • Fig. S6. Corroborating receptor promoter binding evidence from the GTRD.
    • Table S1. Receptor and small-molecule signaling pathway mappings.
    • Table S2. Cell line and tissue biosample mappings to physiological systems and organs.
    • Table S3. Examples of animal and cell models and clinical data sets.
    • Table S4. Experiment naming convention.
    • Table S5. Nonstandard abbreviations in NURSA experiment names.
    • Legend for data file S1

    [Download PDF]

    Technical Details

    Format: Adobe Acrobat PDF

    Size: 1.46 MB

    Other Supplementary Material for this manuscript includes the following:

    • Data file S1 (Microsoft Excel format). Use Case Query parameters and search results.

    [Download Data file S1]


    Citation: L. B. Becnel, S. A. Ochsner, Y. F. Darlington, A. McOwiti, W. H. Kankanamge, M. Dehart, A. Naumov, N. J. McKenna, Discovering relationships between nuclear receptor signaling pathways, genes, and tissues in Transcriptomine. Sci. Signal. 10, eaah6275 (2017).

    © 2017 American Association for the Advancement of Science

Navigate This Article