AI-Guided Patient-Derived Endothelial Modeling Identifies a Reversible Moyamoya-Specific miRNA Deficiency Corrected...

Moyamoya disease (MMD) is a progressive cerebrovascular disorder characterized by abnormal vascular remodeling and impaired angiogenesis. We profiled plasma extracellular vesicle (EV)-derived microRNAs (miRNAs) from MMD patients and healthy controls and generated patient-specific induced pluripotent stem cell-derived endothelial cells (iPSC-ECs) to investigate disease-associated molecular alterations and therapeutic reversibility. Both plasma EVs and iPSC-ECs from MMD patients showed significant depletion of angiogenesis-related miRNAs. Small RNA sequencing data revealed mesenchymal stem cell-derived EVs (MSC-EVs) contain a miRNA repertoire that substantially overlaps with miRNAs deficient in MMD. Treatment of MMD iPSC-ECs with MSC-EVs restored the majority of the depleted miRNAs to normal levels, normalized predicted target mRNA expression, and rescued angiogenic function in a dose-dependent manner, as demonstrated by tube formation assays. To improve identification of biologically relevant miRNA-mRNA interactions, we developed an artificial intelligence-based framework integrating experimentally validated databases, endothelial-focused literature mining, and large language model (LLM)-assisted mechanistic scoring. Incorporation of LLM-derived contextual features modestly improved prediction performance compared with conventional sequence-based models (baseline AUROC ~ 0.51; LLM-enhanced AUROC ~ 0.72), suggesting that literature-informed biological context provides meaningful signal beyond structural target features alone. Furthermore, our findings demonstrate that MSC-EV therapy can substantially reverse molecular and functional endothelial deficits. Integrated profiling and AI-driven interaction mapping together establish a hypothesis-generating framework ("Exo-Courier") for biologically informed prioritization of EV-based therapeutic targets in vascular disorders.