Marked differences in neurochemistry and aggregates despite similar behavioural and neuropathological features of Huntington disease in the full-length BACHD and YAC128 mice
Bibliographic record
Abstract
The development of animal models of Huntington disease (HD) has enabled studies that help define the molecular aberrations underlying the disease. The BACHD and YAC128 transgenic mouse models of HD harbor a full-length mutant huntingtin (mHTT) and recapitulate many of the behavioural and neuropathological features of the human condition. Here, we demonstrate that while BACHD and YAC128 animals exhibit similar deficits in motor learning and coordination, depressive-like symptoms, striatal volume loss and forebrain weight loss, they show obvious differences in key features characteristic of HD. While YAC128 mice exhibit significant and widespread accumulation of mHTT striatal aggregates, these mHTT aggregates are absent in BACHD mice. Furthermore, the levels of several striatally enriched mRNA for genes, such as DARPP-32, enkephalin, dopamine receptors D1 and D2 and cannabinoid receptor 1, are significantly decreased in YAC128 but not BACHD mice. These findings may reflect sequence differences in the human mHTT transgenes harboured by the BACHD and YAC128 mice, including both single nucleotide polymorphisms as well as differences in the nature of CAA interruptions of the CAG tract. Our findings highlight a similar profile of HD-like behavioural and neuropathological deficits and illuminate differences that inform the use of distinct endpoints in trials of therapeutic agents in the YAC128 and BACHD mice.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".