Glucocerebrosidase gene therapy prevents α-synucleinopathy of midbrain dopamine neurons
Bibliographic record
Abstract
Diminished lysosomal function can lead to abnormal cellular accumulation of specific proteins, including α-synuclein, contributing to disease pathogenesis of vulnerable neurons in Parkinson's disease (PD) and related α-synucleinopathies. GBA1 encodes for the lysosomal hydrolase glucocerebrosidase (GCase), and mutations in GBA1 are a prominent genetic risk factor for PD. Previous studies showed that in sporadic PD, and in normal aging, GCase brain activity is reduced and levels of corresponding glycolipid substrates are increased. The present study tested whether increasing GCase through AAV-GBA1 intra-cerebral gene delivery in two PD rodent models would reduce the accumulation of α-synuclein and protect midbrain dopamine neurons from α-synuclein-mediated neuronal damage. In the first model, transgenic mice overexpressing wildtype α-synuclein throughout the brain (ASO mice) were used, and in the second model, a rat model of selective dopamine neuron degeneration was induced by AAV-A53T mutant α-synuclein. In ASO mice, intra-cerebral AAV-GBA1 injections into several brain regions increased GCase activity and reduced the accumulation of α-synuclein in the substantia nigra and striatum. In rats, co-injection of AAV-GBA1 with AAV-A53T α-synuclein into the substantia nigra prevented α-synuclein-mediated degeneration of nigrostriatal dopamine neurons by 6 months. These neuroprotective effects were associated with altered protein expression of markers of autophagy. These experiments demonstrate, for the first time, the neuroprotective effects of increasing GCase against dopaminergic neuron degeneration, and support the development of therapeutics targeting GCase or other lysosomal genes to improve neuronal handling of α-synuclein.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".