A Druggable Genome Screen Identifies Modifiers of α-Synuclein Levels via a Tiered Cross-Species Validation Approach
Bibliographic record
Abstract
Accumulation of α-Synuclein (α-Syn) causes Parkinson's disease (PD) as well as other synucleopathies. α-Syn is the major component of Lewy bodies and Lewy neurites, the proteinaceous aggregates that are a hallmark of sporadic PD. In familial forms of PD, mutations or copy number variations in SNCA (the α-Syn gene) result in a net increase of its protein levels. Furthermore, common risk variants tied to PD are associated with small increases of wild-type α-Syn levels. These findings are further bolstered by animal studies which show that overexpression of α-Syn is sufficient to cause PD-like features. Thus, increased α-Syn levels are intrinsically tied to PD pathogenesis and underscore the importance of identifying the factors that regulate its levels. In this study, we establish a pooled RNAi screening approach and validation pipeline to probe the druggable genome for modifiers of α-Syn levels and identify 60 promising targets. Using a cross-species, tiered validation approach, we validate six strong candidates that modulate α-Syn levels and toxicity in cell lines, Drosophila , human neurons, and mouse brain of both sexes. More broadly, this genetic strategy and validation pipeline can be applied for the identification of therapeutic targets for disorders driven by dosage-sensitive proteins. SIGNIFICANCE STATEMENT We present a research strategy for the systematic identification and validation of genes modulating the levels of α-Synuclein, a protein involved in Parkinson's disease. A cell-based screen of the druggable genome (>7,500 genes that are potential therapeutic targets) yielded many modulators of α-Synuclein that were subsequently confirmed and validated in Drosophila , human neurons, and mouse brain. This approach has broad applicability to the multitude of neurological diseases that are caused by mutations in genes whose dosage is critical for brain function.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".