Diabetic Cardiomyopathy Transcriptional Regulatory Network Analysis

A 955.6 KB Excel file contains the reconstructed transcriptional regulatory subnetwork for diabetic cardiomyopathy (DCM). The analysis, by Fernanda Fredericksen, integrates differential expression data from human hiPSC-derived cardiomyocytes with literature-curated transcription factor-target interactions. It identifies six functional modules and ranks transcription factors like FOS, JUN, STAT3, MYC, and ESR1 by network centrality.

Use Cases

• Analyze the centrality rankings of transcription factors like FOS, JUN, STAT3, MYC, and ESR1 to identify key regulatory hubs in diabetic cardiomyopathy.
• Investigate the six functional modules (metabolic, inflammatory, hypoxic, fibrotic, hormonal) to understand coordinated pathogenic processes in DCM.
• Validate cross-species concordance by comparing network predictions, such as ESR1 and STAT6 downregulation, with experimental data from a type 2 diabetes mouse model.
• Examine the roles of locally acting regulators like TRPS1, HBP1, and NFIA within the multilayered regulatory architecture of the DCM network.

Strengths

• The network analysis is based on a curated integration of experimental differential expression data and literature-derived transcription factor-target interactions.
• Identifies six distinct functional modules, providing a structured view of transcriptional reprogramming in DCM.
• Highlights specific, experimentally validated regulators like ESR1, STAT6, TRPS1, and HBP1, demonstrating cross-species relevance.

Limitations

• The dataset is small in scale at 955.6 KB, indicating limited raw data volume for extensive computational analysis.
• The underlying sample size for the differential expression data is not specified, which may affect the statistical power of the network reconstruction.
• Network predictions are derived from a specific experimental model (hiPSC-derived cardiomyocytes), which may not fully capture in vivo cardiac tissue complexity.

Provenance

Source

Fernanda Fredericksen via figshare.

Collection Method

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

Freshness

Last updated on 2026-03-24.