Multi-omics and AI-guided discovery and validation of sisomicin as a COPA-targeting agent for pathological scar inhibition.

Abstract

BACKGROUND: Pathological scars, including hypertrophic scars and keloids, are fibrotic skin disorders marked by excessive collagen deposition and persistent inflammation, yet effective treatments remain limited. METHODS: We integrated single-cell RNA sequencing, bulk transcriptomic datasets, and artificial intelligence (AI)-driven machine learning and deep learning to identify molecular drivers of scarring, and subsequently validated our findings using both cellular and animal models. RESULTS: High-dimensional weighted gene co-expression network analysis (hdWGCNA) of scar-associated macrophage subsets revealed COPI coat complex alpha subunit (COPA) as a hub gene correlated with macrophage and fibroblast infiltration. AI-guided drug screening and molecular docking identified sisomicin, an aminoglycoside antibiotic, as a potential COPA-targeting agent. Functional assays in human hypertrophic scar-derived fibroblasts (HHFs) and TGF-β1-stimulated NFs showed that sisomicin inhibited proliferation, migration, and collagen synthesis, while increasing ROS and apoptosis. In bleomycin- and traction-induced mouse models, sisomicin reduced dermal thickening, collagen deposition, and COPA expression. CONCLUSION: This multi-omics and AI-based framework uncovers COPA as a key regulator of pathological scars and highlights sisomicin as a promising therapeutic candidate, offering a broadly applicable strategy for anti-fibrotic drug discovery.