Structure, expression and regulation of the cannabinoid receptor gene (<i>CB1</i>) in Huntington's disease transgenic mice
Bibliographic record
Abstract
Loss of cannabinoid receptors (CB1) occurs prior to neurodegeneration in Huntington's disease (HD). The levels and distribution of CB1 RNA were equivalent in 3-week-old mice regardless of genotype demonstrating that the specific factors and appropriate chromatin structure that lead to the transcription of CB1 were present in the striatum of young R6/2 and R6/1 transgenic HD mice. The expression of the mutant HD transgene led progressively to decreased steady-state levels of CB1 mRNA in neurons of the lateral striatum, which was dependent on the size of the CAG repeat and relative expression of the gene encoding mutant huntingtin (HD). Although it is known that the coding region of CB1 is contained within a single exon in mice, rats and humans, the 5'-untranslated region of the mouse gene remained to be defined. CB1 mRNA is encoded by two exons separated by an 18.4-kb intron. Transcription of CB1 occurred at multiple sites within a GC-rich promoter region upstream of exon 1 encoding the 5'-UTR of CB1. There was no difference in the selection of specific transcription initiation sites associated with higher levels of CB1 expression in the striatum compared to the cortex or between the striata of wild-type and HD transgenic mice. The progressive decline in CB1 mRNA levels in R6 compared to wild-type mice was due to decreased transcription, which is consistent with the hypothesis that mutant huntingtin exerts its effects by altering transcription factor activity. The cell-specific conditions that allow for increased transcription of CB1 in the lateral striatum compared to other forebrain regions from all transcription start sites were affected by the expression of mutant huntingtin in a time-dependent manner.
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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 | 0.001 |
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".