Association of Bassoon (BSN) Gene Mutations with Gait and Motor Impairments in Parkinson’s Disease
Bibliographic record
Abstract
Abstract Introduction Parkinson’s Disease (PD) features debilitating motor symptoms, particularly gait and balance impairments inadequately managed by current therapies. Bassoon ( BSN), a presynaptic active-zone organizer, has been implicated in various neurological disorders. Here, we evaluate the impact of rare BSN mutations on motor symptoms in PD patients. Methods Our study included 110 PD patients carrying BSN mutations and 558 PD controls from a South Asian early-onset PD cohort (onset <50 years). Variants with mean allele frequency (MAF) <0.1% were classified as “rare” (n=44). Clinical motor features were compared between variant carriers and non-carriers. Computational tools (CADD, PolyPhen-2, I-Mutant2.0, ConSurf) predicted deleteriousness, while GeneMANIA and STRING elucidated Bassoon’s functional interactions. Results Patients carrying BSN variants exhibited significantly increased freezing of gait (FOG, p=0.026, Carmer’s V=0.118), shuffling gait (SG, p=0.041, Carmer’s V=0.111), and falls (p=0.028, Carmer’s V=0.117). Rare BSN mutations clustered in the Bassoon C-terminal region (aa 3500– 3800), threefold above expected frequency. Computational predictions identified seven likely pathogenic variants (P171L, A852T, P988A, R1015H, R2561H, R3400W, L3561P), with highest confidence for P171L (confirmed by AlphaMissense). Functional analyses implicated Bassoon in axonal transport, presynaptic proteostasis, and neurotransmitter release in dopaminergic/cholinergic neurons. Conclusion Our findings identify BSN mutations as a genetic risk factor for PD-related gait and balance dysfunction, highlighting Bassoon’s role in neurotransmission. The link with Progressive Supranuclear Palsy phenotypes suggests Bassoon dysfunction could represent a convergence point between synucleinopathies and tauopathies.
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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 | 0.001 |
| 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) | 0.003 | 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".