Glucocorticoid receptor (GR) signaling elicits diverse transcriptional responses through dynamic and context-dependent interactions with chromatin. Here, we define a temporally resolved and mechanistically integrated framework for GR-mediated gene regulation. Time-resolved analyses identify three conserved classes of GR chromatin binding (sustained, transient, and late), distinguished by differences in motif strength, chromatin accessibility, and cofactors engagement. Early GR binding preferentially occurs at high-affinity glucocorticoid response elements (GREs) within pre-accessible regulatory regions, whereas late binding is associated with weaker motifs and requires chromatin remodeling activity. Enhancer activation, marked by H3K27ac deposition, closely tracks GR occupancy, supporting a model in which GR recruits acetyltransferase activity to drive coordinated enhancer activation. Concurrently, GR-centered interaction networks are dynamically reconfigured, and motif enrichment analyses identify distinct transcription factor signatures across binding classes, including AP-1/JUNB at transient sites and CEBP family members at late-binding regions. Integration of chromatin binding, chromatin interaction, and transcriptomic datasets reveals that temporal and combinatorial GR occupancy is functionally linked to gene expression programs. Distinct GR binding clusters are nonrandomly associated with specific transcriptional trajectories, including sustained, transient, and late gene induction. Moreover, combinatorial occupancy across multiple regulatory elements correlates quantitatively with transcriptional output, indicating that GR functions not as a simple binary regulator, but as an integrator of multilayered regulatory inputs. These findings support a unified model in which temporal binding dynamics, chromatin state, and combinatorial enhancer activity collectively encode transcriptional specificity, providing a general framework for stimulus-responsive nuclear receptor signaling.
Behavior change beyond intervention: an activity-theoretical perspective on human-centered design of personal health technology
IntroductionModern personal technologies, such as smartphone apps with artificial intelligence (AI) capabilities, have a significant potential for helping people make necessary changes in their behavior
