Deep Plasma Proteomics Coupled with Functional Genomics Reveals Drivers of Parkinson’s Disease Progression and Levodopa Response

Abstract Parkinson’s disease (PD) is a progressive neurodegenerative disorder lacking disease-modifying therapies, and its management is limited by the absence of accessible biomarkers for disease progression and treatment response. We implemented an ultra-deep plasma proteomics workflow integrating Mag-Net extracellular vesicle enrichment with Orbitrap Astral mass spectrometry to profile longitudinal samples from PD patients. This approach quantified 6,481 plasma proteins, an unprecedented depth in PD studies, revealing distinct signatures associated with disease duration and dopaminergic therapy exposure. Candidate biomarkers were validated in an independent cohort using ELISA, demonstrating predictive utility in AI-driven models. To uncover mechanistic drivers, we intersected proteomic data with our new genome-wide overexpression screens for regulators of alpha-synuclein pre-formed fibril uptake, identifying MFN2, PSMD4, and EIF4G1 as major hubs that link systemic proteomic changes to mitochondrial dynamics and proteostasis. Additionally, a meta-analysis of brain transcriptomes responsive to Levodopa (L-DOPA) treatment identified 42 candidate genes, including NDUFS4, GNAS, TSC1, and NTS, some of which are targets of approved therapeutics. Finally, an integrative network analysis revealed that key pathological hubs, such as CD14, IFNG, and PLAT, are targets of approved pharmacological agents. Overall, these findings provide a systems-level resource for PD biomarker discovery and reveal druggable pathways for precision medicine strategies aimed at improving therapeutic outcomes.