Similar striatal gene expression profiles in the striatum of the YAC128 and HdhQ150 mouse models of Huntington’s disease are not reflected in mutant Huntingtin inclusion prevalence
Bibliographic record
Abstract
BACKGROUND: The YAC128 model of Huntington's disease (HD) shows substantial deficits in motor, learning and memory tasks and alterations in its transcriptional profile. We examined the changes in the transcriptional profile in the YAC128 mouse model of HD at 6, 12 and 18 months and compared these with those seen in other models and human HD caudate. RESULTS: Differential gene expression by genotype showed that genes related to neuronal function, projection outgrowth and cell adhesion were altered in expression. A Time-course ANOVA revealed that genes downregulated with increased age in wild-type striata were likely to be downregulated in the YAC128 striata. There was a substantial overlap of concordant gene expression changes in the YAC128 striata compared with those in human HD brain. Changes in gene expression over time showed fewer striatal YAC128 RNAs altered in abundance than in the HdhQ150 striata but there was a very marked overlap in transcriptional changes at all time points. Despite the similarities in striatal expression changes at 18 months the HdhQ150 mice showed widespread mHTT and ubiquitin positive inclusion staining in the striatum whereas this was absent in the YAC128 striatum. CONCLUSIONS: The gene expression changes in YAC128 striata show a very closely matched profile to that of HdhQ150 striata and are already significantly different between genotypes by six months of age, implying that the temporal molecular gene expression profiles of these models match very closely, despite differences in the prevalence of brain inclusion formation between the models. The YAC128 gene expression changes appear to correlate well with gene expression differences caused by ageing. A relatively small number of genes showed significant differences in expression between the striata of the two models and these could explain some of the phenotypic differences between the models.
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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 | 0.000 |
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".