Transcriptomic alterations uncover risk genes in Parkinson’s disease, indicating potential for targeted interventions.
Background/Objectives: Pathogenic variants in the GBA1 gene, which encodes the lysosomal enzyme β-glucocerebrosidase, cause Gaucher disease (GD) and represent one of the strongest genetic risk factors for Parkinson’s disease (PD). However, not all carriers develop PD, suggesting the involvement of additional modifiers. Transcriptomic alterations shared between GD and PD may reveal such modifiers and provide insights into the mechanisms linking GBA1 to PD. Methods: Eighteen transcriptomic datasets spanning GD, GBA1-associated PD, and sporadic PD were integrated to identify shared, directionally concordant differentially expressed genes, followed by pathway enrichment analysis. Causal relationships were assessed using two-sample Mendelian randomisation with whole-blood and brain genetic instruments and PD GWAS summary statistics. Diagnostic relevance was evaluated in independent datasets using machine learning, while metabolic implications were explored with a neuron-specific genome-scale metabolic model. Results: Shared DEGs were enriched in lysosomal, lipid, redox, and endocrine pathways. Mendelian randomisation prioritised 12 risk genes in whole blood and 5 in brain tissue, with 4 overlapping; risk-increasing effects were observed for GPNMB, MMP9, TRIM22, TESMIN, NFE2L3, FAM89A, METTL7A, PID1, NECAB2, and LPL, whereas GIPR and RASGRF2 showed protective effects, and AGT was brain-specific. Diagnostic signals were concentrated in a subset of genes, while metabolic modelling revealed convergent but subtype-specific perturbations across metabolic circuits. Conclusions: Convergent genetic, transcriptomic, and metabolic evidence supports at least two mechanistic routes to PD risk: a GBA1-sensitised lysosomal–lipid/redox axis, and a GBA1-independent neuronal–endocrine axis. These findings explain the variable risk among GBA1 carriers, identify candidate biomarkers, and highlight pathway-anchored targets for stratified intervention.
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Liu et al. (2025) studied this question.
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