Enzyme Activity-Based Genome-wide Screening for Modifiers of Lysosomal Glucocerebrosidase Uncovers Candidate Risk Factors for Parkinson’s Disease
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Mutations in GBA1, the gene encoding the lysosomal hydrolase glucocerebrosidase (GCase), are the strongest common genetic risk factor for Parkinson’s Disease (PD). However, these mutations are incompletely penetrant, which suggests that there are likely genetic modifiers of GCase function. To identify such genes, we implemented a live cell GCase activity-based CRISPR-platform to enable genome-wide screening for novel regulators of lysosomal GCase activity. Among the screening hits, we find significant enrichment of genes linked to development and progression of PD through genome-wide association studies (GWAS). Moreover, we identify two lysosomal lipid transporter genes, including those encoding the lysosphospholipid transporter SPNS1 and the cholesterol transporter NPC1, and find an allele of SPNS1 that is associated with increased risk of PD. We show that disruption of SPNS1 does not affect GCase protein levels but impairs its lysosomal function. Collectively, these data suggest that dysfunction of many PD-associated genes converge to impact lysosomal GCase activity and thereby contribute to disease pathogenesis. A better understanding of the impacts of these and the other GCase modulators identified here should help unravel the important, yet complex, relationship between GBA1 and PD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".