Striatal Pre-Enkephalin Overexpression Improves Huntington’s Disease Symptoms in the R6/2 Mouse Model of Huntington’s Disease
Bibliographic record
Abstract
The reduction of pre-enkephalin (pENK) mRNA expression might be an early sign of striatal neuronal dysfunction in Huntington's disease (HD), due to mutated huntingtin protein. Indeed, striatopallidal (pENK-containing) neurodegeneration occurs at earlier stage of the disease, compare to the loss of striatonigral neurons. However, no data are available about the functional role of striatal pENK in HD. According to the neuroprotective properties of opioids that have been recognized recently, the objective of this study was to investigate whether striatal overexpression of pENK at early stage of HD can improve motor dysfunction, and/or reduce striatal neuronal loss in the R6/2 transgenic mouse model of HD. To achieve this goal recombinant adeno-associated-virus (rAAV2)-containing green fluorescence protein (GFP)-pENK was injected bilaterally in the striatum of R6/2 mice at 5 weeks old to overexpress opioid peptide pENK. Striatal injection of rAAV2-GFP was used as a control. Different behavioral tests were carried out before and/or after striatal injections of rAAV2. The animals were euthanized at 10 weeks old. Our results demonstrate that striatal overexpression of pENK had beneficial effects on behavioral symptoms of HD in R6/2 by: delaying the onset of decline in muscular force; reduction of clasping; improvement of fast motor activity, short-term memory and recognition; as well as normalization of anxiety-like behavior. The improvement of behavioral dysfunction in R6/2 mice having received rAAV2-GFP-pENK associated with upregulation of striatal pENK mRNA; the increased level of enkephalin peptide in the striatum, globus pallidus and substantia nigra; as well as the slight increase in the number of striatal neurons compared with other groups of R6/2. Accordingly, we suggest that at early stage of HD upregulation of striatal enkephalin might play a key role at attenuating illness symptoms.
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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 | 0.000 |
| 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".