Progressive Neurodegeneration or Endogenous Compensation in an Animal Model of Parkinson's Disease Produced by Decreasing Doses of Alpha-Synuclein
Bibliographic record
Abstract
The pathological hallmarks of Parkinson's disease (PD) are degeneration of dopamine (DA) neurons of the substantia nigra (SN) and the presence of alpha-synuclein (α-syn)-rich Lewy bodies in DA cells that remain. To model these aspects of the disease, we previously showed that high titer (5.1×10exp12 gp/ml) AAV1/2 driven expression of A53T α-syn in the SN of rats caused nigrostriatal pathology including a loss of DA neurons, but also with toxicity in the GFP control group. In the current study, we evaluate the effects of two lower titers by dilution of the vector (1∶3 [1.7×10exp12] and 1∶10 [5.1×10exp11]) to define a concentration that produced pathology specific for α-syn. In GFP and empty vector groups there were no behavioural or post-mortem changes at 3 or 6 weeks post-administration at either vector dose. Dilution of the AAV1/2 A53T α-syn (1:3) produced significant paw use asymmetry, reductions in striatal tyrosine hydroxylase (TH), and increases in DA turnover at 3 weeks in the absence of overt pathology. By 6 weeks greater evidence of pathology was observed and included, reductions in SN DA neurons, striatal DA, TH and DA-transporter, along with a sustained behavioural deficit. In contrast, the 1:10 AAV1/2 A53T α-syn treated animals showed normalization between 3 and 6 weeks in paw use asymmetry, reductions in striatal TH, and increased DA turnover. Progression of dopaminergic deficits using the 1:3 titer of AAV1/2 A53Tα-syn provides a platform for evaluating treatments directed at preventing and/or reversing synucleinopathy. Use of the 1:10 titer of AAV1/2 A53T α-syn provides an opportunity to study mechanisms of endogenous compensation. Furthermore, these data highlight the need to characterize the titer of vector being utilized, when using AAV to express pathogenic proteins and model disease process, to avoid producing non-specific effects.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".