Upregulation of endocannabinoid signaling in vivo restores striatal synaptic plasticity and motor performance in Huntington's disease mice
Bibliographic record
Abstract
Background Synaptic dysfunction underlies early sensorimotor and cognitive deficits in Huntington's disease (HD) and precedes the degeneration of striatal spiny projection neurons and cortical pyramidal neurons. Movement selection and motor learning, which are impaired early in HD, are regulated by connections between the motor cortex, basal ganglia and thalamus. In particular, plasticity at corticostriatal synapses, including endocannabinoid-mediated long-term depression (LTD), is critical for motor learning. Previously, we found impaired endocannabinoid-mediated LTD, induced by high frequency stimulation (HFS) at corticostriatal synapses 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. Objective Determine the effects of in vivo JZL184 administration on YAC128 HD model and wild-type (WT) littermate mice. Methods JZL184 was administered to mice orally over a 3-week period and their motor function was assessed using several behavioral tasks. In addition, brain tissue was collected from mice in order to quantify changes in endocannabinoid levels and measure HFS-induced plasticity at corticostriatal synapses. Results Oral administration of JZL184 significantly increased levels of 2-AG in striatal tissue. While JZL184 treatment had no impact on open field behavior, the treatment eliminated the difference in motor learning on the rotarod task between YAC128 and WT mice. Moreover, HFS-induced striatal plasticity in YAC128 mice was normalized to WT levels after JZL184 treatment. Conclusions These results suggest a novel target for mitigating early symptoms of HD and support the need for clinical trials of therapies that modulate the endocannabinoid system.
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 | 0.001 | 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) | 0.002 | 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".