Transcriptomic and Multi-scale Network Analyses Reveal Key Drivers of Cardiovascular Disease.

Area of Science:

• Molecular biology
• Bioinformatics
• Cardiovascular research

Background:

• Cardiovascular diseases (CVDs) involve molecular signaling, extracellular matrix (ECM), cytoskeleton, and immune responses.
• The fine-wire vascular injury model is used for neointimal hyperplasia but not typically for CVDs.

Purpose of the Study:

• To test the hypothesis that vascular injury induces CVD-like changes at transcriptome and protein levels.
• To analyze gene expression and molecular communication in a mouse vascular injury model.

Main Methods:

• Microarray analysis of injured vs. uninjured mouse femoral arteries.
• Construction of protein-protein interaction networks and identification of functional clusters.
• Machine learning-based disease pathway analysis.

Main Results:

• Identified 1,467 differentially expressed genes linked to CVDs like atherosclerosis and vaso-occlusion.
• Discovered seven distinct protein interaction clusters enriched in ECM, metabolism, cytoskeleton, and immune response.
• Revealed crosstalk between ECM remodeling, immune response, and cytoskeleton reorganization linked to various CVD pathologies.

Conclusions:

• Vascular injury model effectively replicates transcriptomic and molecular network changes seen in CVDs.
• The model's robustness supports its use for studying cardiovascular disease mechanisms.
• Highlights the interconnected roles of ECM, cytoskeleton, and immune response in vascular pathologies.