Mutant <i>GBA1</i> Expression and Synucleinopathy Risk: First Insights from Cellular and Mouse Models
Bibliographic record
Abstract
Heterozygous mutations in the glucocerebrosidase gene (GBA1) are associated with increased risk for α-synuclein aggregation disorders ('synucleinopathies'), which include Parkinson's disease (PD) and dementia with Lewy bodies (DLB). Homozygous GBA1 mutations lead to reduced GBA1 lysosomal activity underlying three variants of Gaucher disease (GD). Despite the wealth of clinical and genetic evidence supporting the association between mutant genotypes and synucleinopathy risk, the precise mechanisms by which GBA1 mutations lead to PD and DLB remain unclear. Here, we summarize recent findings that highlight the complexity of this pathogenetic link. In neural cells, both gain and loss of function mechanisms, as conferred by mutant GBA1 expression and activity loss, respectively, seem to promote aberrant α-synuclein processing. In addition, we draw attention to recent insights gleaned from GD animal models regarding axonal pathology, brain inflammation and memory dysfunction. From a translational perspective, we discuss the concepts of neural enzyme replacement therapy and pharmacological agents as potential treatment strategies for GBA1-associated synucleinopathies. Finally, we touch on the issue whether aberrant α-synuclein species may coregulate GBA1 activity in the vertebrate brain, thereby providing a reverse link, i.e., between an important synucleinopathy risk factor and the enzyme's lysosomal function. In summary, several leads connecting GBA1 mutations with α-synuclein misprocessing have emerged as potential targets for the treatment of GBA1-related synucleinopathies, and possibly, for non-GBA1-associated neurodegenerative diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".