Temporal, spatial and molecular pattern of dopaminergic neurodegeneration in the AAV-A53T α-synuclein rat model of Parkinson’s disease
Bibliographic record
Abstract
Degeneration of the nigrostriatal tract is a neuropathological hallmark of Parkinson's disease (PD). A differential intraneuronal vulnerability of dopaminergic neurons within the substantia nigra (SN) has been suggested, starting as an axonopathy followed by neuronal cell loss that is accompanied with motor deficits. To date, there is no therapy available to delay or halt this neurodegeneration. Nuclear factor (erythroid-derived 2)-like-2 factor (Nrf2) and histone deacetylase 1 (HDAC1) are crucial molecular regulators that undergo nucleo-cytoplasmic shuttling and are involved in regulation of axonal and perikarya degeneration of neurons under various pathologic conditions. We here aimed to analyze the time course of dopaminergic neurodegeneration in an AAV PD rat model overexpressing human mutated A53T α-synuclein (haSyn), differentially correlate striatal terminal and SN perikarya loss with behavioral deficits and investigate if nucleo-cytoplasmic Nrf2 and HDAC1 expression are altered in dopaminergic perikarya of the haSyn PD rat model. We observed impaired motor performance in haSyn PD rats assessed by the single pellet reaching task at four- and six-weeks post AAV injection (P < 0.05 each). However, only striatal terminal loss correlated significantly with motor deficits in haSyn PD rats, indicating that parkinsonian motor features reflect the striatal dopaminergic denervation, but cannot be taken as an indirect measure of neurodegeneration per se. Immunofluorescence staining demonstrated an upregulation of HDAC1 in the dopaminergic cell nucleus (P < 0.05) while no changes were observed for Nrf2. These data suggest a critical functional role of the axonopathy on motor behavior in haSyn PD rats and mechanistically point towards an impaired nucleo-cytoplasmic translocation of HDAC1 and thus a potential role of disturbed histone acetylation in neurodegeneration.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".