Profiling of 3696 Nuclear Receptor-Coregulator Interactions: A Resource for Biological and Clinical Discovery.

Abstract

Nuclear receptors (NRs) are ligand-inducible transcription factors that play critical roles in metazoan development, reproduction, and physiology and therefore are implicated in a broad range of pathologies. The transcriptional activity of NRs critically depends on their interaction(s) with transcriptional coregulator proteins, including coactivators and corepressors. Short leucine-rich peptide motifs in these proteins (LxxLL in coactivators and LxxxIxxxL in corepressors) are essential and sufficient for NR binding. With 350 different coregulator proteins identified to date and with many coregulators containing multiple interaction motifs, an enormous combinatorial potential is present for selective NR-mediated gene regulation. However, NR-coregulator interactions have often been determined experimentally on a one-to-one basis across diverse experimental conditions. In addition, NR-coregulator interactions are difficult to predict because the molecular determinants that govern specificity are not well established. Therefore, many biologically and clinically relevant NR-coregulator interactions may remain to be discovered. Here, we present a comprehensive overview of 3696 NR-coregulator interactions by systematically characterizing the binding of 24 nuclear receptors with 154 coregulator peptides. We identified unique ligand-dependent NR-coregulator interaction profiles for each NR, confirming many well-established NR-coregulator interactions. Hierarchical clustering based on the NR-coregulator interaction profiles largely recapitulates the classification of NR subfamilies based on the primary amino acid sequences of the ligand-binding domains, indicating that amino acid sequence is an important, although not the only, molecular determinant in directing and fine-tuning NR-coregulator interactions. This NR-coregulator peptide interactome provides an open data resource for future biological and clinical discovery as well as NR-based drug design.