Transcriptional regulatory network analysis uncovers modular gene control and potential key regulators in diabetic...

Diabetic cardiomyopathy (DCM) arises from the interplay of metabolic overload, inflammation, and structural remodeling that ultimately impair cardiac function. However, the transcriptional mechanisms coordinating these pathogenic processes remain incompletely defined. This study aimed to reconstruct and functionally characterize the transcriptional regulatory architecture associated with DCM.

We combined differential expression data from human hiPSC-derived cardiomyocytes exposed to diabetic conditions with literature-curated transcription factor-target interactions to reconstruct a comprehensive transcriptional regulatory subnetwork for DCM. Network topology and functional enrichment analyses were performed to identify regulatory modules and hierarchical organization. Selected network-derived predictions were experimentally evaluated in a type 2 diabetes mouse model.

Transcriptional reprogramming was organized into six functional modules encompassing metabolic, inflammatory, hypoxic, fibrotic, and hormonal pathways. Established DCM-associated transcription factors, including FOS, JUN, STAT3, and MYC, ranked among the highest-centrality hubs. Notably, ESR1, a key regulator of estrogen signaling, emerged as a previously unrecognized high-centrality node within the DCM network. In contrast, TRPS1, HBP1, and NFIA showed lower centrality and operated as locally acting regulators, consistent with a multilayered regulatory architecture. Experimental validation in diabetic mice demonstrated significant downregulation of ESR1 and STAT6, together with upregulation of TRPS1 and HBP1, supporting cross-species concordance of selected regulatory signatures.

These findings define the modular organization of a curated transcriptional regulatory subnetwork underlying DCM and highlight candidate regulators that warrant future functional perturbation studies and biomarker-oriented validation. This integrative network-based framework provides mechanistic insight into transcriptional coordination in diabetic cardiac disease.