Work

Multi-omics analysis of hypoxia-mitochondrial energy metabolism and programmed cell death profiling in Parkinson’s disease

Year: 2026

Type: article

Abstract: Hypoxia and mitochondrial energy metabolism (MEM) impairment are pivotal features of Parkinson’s disease (PD), yet the regulators linking hypoxic stress to mitochondrial failure remain elusive. Here we integrated multiple transcriptomic datasets from the substantia nigra to characterize the hypoxia and MEM landscape. Utilizing a tripartite machine learning framework combined with LASSO-logistic regression, we developed a robust seven-gene diagnostic model that achieved high predictive accuracy (AUC: 0.887 training; 0.821 validation). Two-sample Mendelian randomization (MR) analysis further identified seven causal genes (TTC19, SERPINA1, MPC2, DLK1, DDIT4, AUH, and ASL) associated with PD risk. By integrating programmed cell death (PCD) profiling and scRNA-seq, we discovered that core regulators MPC2 and AUH were uniquely associated with novel PCD modes, showing negative correlations with alkaliptosis and positive correlations with cuproptosis. Single-cell analysis localized primary hypoxia-MEM impairment to dopaminergic neurons (DaNs), where MPC2 expression was significantly depleted. In silico virtual knockout of MPC2 disrupted dopamine metabolism and synaptic vesicle cycles, while STAT3 and MEF2A were identified as potential upstream transcription factors linking metabolic dysfunction to neuroinflammation. Together, our findings identify MPC2 as a genetically supported causal regulator linking hypoxia-MEM failure to novel PCD in DaNs, offering potential biomarkers and therapeutic targets for precision intervention in PD.

Source: npj Parkinson s Disease

DOI: 10.1038/s41531-026-01576-5