medRxiv (Medicine)
2026-06-25
DOI: HASH:c65a6ff7e740de57ec6d24d67f987287
Background: Lysosomal dysfunction plays a crucial role in the pathogenesis of Parkinson's disease (PD), particularly among GBA1 mutation carriers. Beyond GBA1, genes such as TMEM175, SCARB2, and CTSB identified in genome-wide association studies (GWAS) are also implicated in lysosomal pathways contributing to PD risk, although their functional effects in patients remain unclear. Proteins encoded by these lysosome-related genes have been explored as potential therapeutic targets in experimental models. Biomarker profiles, including clinical measures, alpha-synuclein seeding activity, lysosomal proteins, and sphingolipids, may facilitate patient stratification and support therapeutic monitoring in future clinical trials. Aim: The aim of this study is to investigate the impact of genetic variants of three lysosomal-related genes (TMEM175, SCARB2, and CTSB) on biomarker profiles in PD with and without GBA1 variants. Cross-sectional data from two German cohorts: the Tuebingen Parkinson Cohort (TUEPAC) and the DESCRIBE PD cohort of the German Center for Neurodegenerative Diseases were used as explorative cohorts, and data from Accelerating Medicines Partnership Parkinson's Disease (AMP-PD) were used as a validation cohort. The ultimate goal is to provide new data for patient stratification based on genetics, which might serve as a readout for target engagement and treatment efficiency assessment. Methods: Three cohorts were analyzed: TUEPAC, DESCRIBE PD, and AMP-PD. TUEPAC and DESCRIBE PD were combined into a single German discovery cohort (TUEPAC-DESCRIBE-PD), while AMP-PD served as an independent validation cohort. Within each cohort, for subgroup analyses, PD patients were classified as the overall PD cohort (PDall), and further stratified by GBA1 mutation status into PD patients without GBA1 mutations (PDGBA1_wildtype), and PD patients carrying GBA1 mutations (PDGBA1). We evaluated cognitive and motor function, as well as depression using the Montreal Cognitive Assessment (MoCA), Unified Parkinson Disease Rating Scale-part III (UPDRS III), and Beck Depression Inventory-II(BDI-II) scales. Analyzed biomarkers included CSF -syn seeding activity using seed amplification assay (SAA), CSF lysosomal protein levels of lysosomal integral membrane protein 2 (LIMP2), also known as SCARB2, cathepsin B (CTSB) and lysosome-associated membrane protein 2 (LAMP2), blood-based enzyme activity of the lysosomal glucocerebrosidase (GCase), and CSF sphingolipid profiles. PD patients carrying risk alleles in TMEM175, SCARB2, and CTSB were compared to non-carriers. Results: Genotype-phenotype correlation analysis in TUEPAC-DESCRIBE-PD and AMP-PD revealed: (1) In PDall, the TMEM175 p.M393T risk variant was nominally associated with decreased cognitive function when adjusted for GBA1 mutation status in TUEPAC-DESCRIBE-PD; this association could not be replicated, although a similar trend was observed in the slightly smaller, but multicentric AMP-PD cohort; TMEM175 p.M393T was not significantly associated with BDI-II or UPDRS-III scores in either cohort. (2) In PDGBA1_wildtype, GCase activity was significantly lower in PD patients with SCARB2 rs6812193 risk allele in TUEPAC-DESCRIBE-PD, while a similar but non-significant trend was observed in AMP-PD; (3) In PDall, CSF levels of CTSB were nominally lower in carriers of CTSB rs1293298 risk allele compared to carriers of CTSB rs1293298 protective allele in TUEPAC-DESCRIBE-PD; in PDGBA1_wildtype, LAMP2 was significantly lower in carriers of CTSB rs1293298 risk allele compared to carriers of CTSB rs1293298 protective allele in TUEPAC-DESCRIBE-PD; (4) In PDall, TMEM175 p.M393T risk allele was nominally associated with altered sphingolipid profiles across both TUEPAC-DESCRIBE-PD and AMP-PD cohorts. Conclusion: These findings demonstrate that genetic variants in lysosomal-related genes (TMEM175, SCARB2, and CTSB) have a functional impact on biomarker profiles in PD patients. Integrating genetic characterization with biochemical profiling provides a framework for patient stratification and may serve as a translational strategy to monitor target engagement and evaluate treatment efficacy in future clinical trials.