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.
Keywords: Gene Expression Omnibus; bioinformatics; nuclear receptor signaling; thyroid receptor; transcriptomics.