Transcriptional Regulatory Network Analysis of Diabetic Cardiomyopathy

A 1.1 MB DOCX file presents a curated transcriptional regulatory subnetwork for diabetic cardiomyopathy (DCM). The analysis identifies six functional modules and ranks transcription factors like FOS, JUN, STAT3, MYC, and ESR1 by network centrality. It was authored by Fernanda Fredericksen and last updated in March 2026.

Use Cases

• Identify high-centrality transcription factor hubs like FOS, JUN, and ESR1 for candidate regulator prioritization in DCM studies.
• Analyze the six functional modules (metabolic, inflammatory, hypoxic, fibrotic, hormonal) to understand pathogenic process coordination.
• Validate cross-species regulatory signatures by comparing network predictions, such as ESR1 and STAT6 expression, with experimental models.

Strengths

• Analysis is based on differential expression data from human hiPSC-derived cardiomyocytes and literature-curated transcription factor-target interactions.
• The reconstructed network is functionally characterized, identifying six distinct regulatory modules and hierarchical organization.
• Selected network predictions, including ESR1 and TRPS1 regulation, were experimentally validated in a type 2 diabetes mouse model.

Limitations

• The core data is a 1.1 MB DOCX document, not a structured, machine-readable dataset (e.g., CSV, JSON) for direct computational analysis.
• The underlying row/column structure, gene expression values, and the full interaction network are not provided as separate data files.
• The analysis focuses on a specific subnetwork for DCM, limiting generalizability to other cardiac or metabolic diseases without further study.

Provenance

Source

Fernanda Fredericksen via figshare.

Collection Method

Integrative analysis combining differential expression data from hiPSC-derived cardiomyocytes with literature-curated transcription factor-target interactions.

Freshness

Last updated on 2026-03-24.