Abstract
Parkinson's disease (PD) is a neurodegenerative disorder characterized by dopaminergic neuron degeneration and α-synuclein (α-syn) aggregation. Lipid metabolism dysfunction may contribute to PD progression. This study aims to identify lipid metabolism-related genes (LMGs) associated with PD using an integrative transcriptomic analysis of microarray and single-cell RNA sequencing (scRNA-seq) datasets from patients with PD and healthy controls. Differentially expressed genes (DEGs) related to lipid metabolism were identified, and key genes were further filtered using weighted gene co-expression network analysis (WGCNA) and machine learning algorithms. Four LMGs, AGPAT2, ASAH2, FA2H, and MECR were identified, with MECR being notably downregulated in both bulk and single-cell transcriptomic analyses of PD patients. This downregulation was further validated in α-syn PFF-induced PD models. Virtual screening and molecular simulations identified potential allosteric modulators of MECR, which may offer a pathway for future therapeutic exploration. This study highlights MECR as a critical gene link between lipid metabolism dysfunction and PD, suggesting the need for further investigation into its therapeutic implications.