Back

Exosome-Derived Proteomic Signatures Highlight Pathogenic Mechanisms in Moyamoya Disease

Gupta, T.; Bharti, R.; Devi, V.; Kumar, M.; Aggarwal, A.; Maras, J. S.

2026-07-30 systems biology
10.64898/2026.07.30.741716 bioRxiv
Show abstract

BackgroundThe etiology and molecular mechanisms of Moyamoya disease (MMD) remain unclear. Exosomes, as carriers of bioactive molecules, may reflect disease-specific alterations and serve as potential biomarkers. This study aimed to investigate disease mechanisms using proteomic profiling of serum-derived exosomes (SDEs) in MMD. Materials and MethodsPeripheral blood each from 15 MMD patients and 15 healthy-controls were used to isolate SDEs via ultracentrifugation. Proteins from pooled SDEs were extracted, digested, and analyzed by LC-MS/MS. Differentially expressed proteins were examined using MetaboAnalyst, DAVID, Enrichr, STRING, and Cytoscape. Key targets were validated at transcript and protein levels using RT-qPCR and ELISA in independent cohorts. ResultsA total of 2,554 proteins were identified, with 213 showing differential expression (118 upregulated, 95 downregulated; p [≤] 0.05). Functional and pathway analyses revealed enrichment in angiogenesis, cytoskeletal remodeling, and endothelial signaling. PRKG2 and MYC were upregulated, while RHOA was downregulated, highlighting their involvement in focal adhesion and PI3K-AKT pathways. Validation confirmed these findings. ConclusionDysregulated proteins were linked to RHOA-ROCK and PI3K-Akt signaling, suggesting their role in driving VSMC phenotypic switching, contributing to vascular occlusion. These findings indicate that altered exosomal-proteins may participate in maladaptive vascular remodeling, although the initial trigger for VSMC transition remains unknown.

Matching journals

The top 13 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.