Effect of chronic upregulation of endocannabinoid signaling <i>in vivo</i> with JZL184 on striatal synaptic plasticity and motor learning in YAC128 Huntington disease mice
Bibliographic record
Abstract
ABSTRACT Synaptic dysfunction underlies early sensorimotor and cognitive deficits, and precedes neurodegeneration in a variety of disorders, including Alzheimer, Parkinson and Huntington disease (HD). A monogenic inherited disorder, HD manifests with cognitive, motor and mood disorders associated with progressive degeneration of striatal spiny projection neurons and cortical pyramidal neurons. Cortico-basal ganglia-thalamic loops regulate movement selection and motor learning, which are impaired early in HD. Skilled motor learning is mediated in part by plasticity at cortico-striatal synapses, including endocannabinoid-mediated, high-frequency stimulation induced long-term depression (HFS-LTD). Previously, we found impaired HFS-LTD in brain slice recordings from pre-manifest HD mouse models, which was corrected by JZL184, an inhibitor of endocannabinoid 2-arachidonoyl glycerol (2-AG) degradation. Here, we tested the effects of JZL184 administered in vivo to YAC128 HD model and wild-type (WT) littermate mice. JZL184, given orally daily over a 3-week period, significantly increased levels of 2-AG in striatal tissue. While JZL184 treatment had no impact on open field behavior which was similar for the two genotypes, the treatment improved motor learning on the rotarod task in YAC128 mice to the level observed in WT mice. Moreover, HFS-induced striatal plasticity measured by field potential recording in acute brain slice from YAC128 mice was normalized to WT levels after JZL184 treatment. These results suggest a novel target for mitigating early symptoms of HD, and support the need for clinical trials to test the efficacy of modulating the endocannabinoid system in treatment of HD.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".