Antisense Oligonucleotide-Mediated Correction of Transcriptional Dysregulation is Correlated with Behavioral Benefits in the YAC128 Mouse Model of Huntington's Disease
Bibliographic record
Abstract
BACKGROUND: Huntington's disease (HD) is a neurological disorder caused by mutations in the huntingtin (HTT) gene, the product of which leads to selective and progressive neuronal cell death in the striatum and cortex. Transcriptional dysregulation has emerged as a core pathologic feature in the CNS of human and animal models of HD. It is still unclear whether perturbations in gene expression are a consequence of the disease or importantly, contribute to the pathogenesis of HD. OBJECTIVE: To examine if transcriptional dysregulation can be ameliorated with antisense oligonucleotides that reduce levels of mutant Htt and provide therapeutic benefit in the YAC128 mouse model of HD. METHODS: Quantitative real-time PCR analysis was used to evaluate dysregulation of a subset of striatal genes in the YAC128 mouse model. Transcripts were then evaluated following ICV delivery of antisense oligonucleotides (ASO). Rota rod and Porsolt swim tests were used to evaluate phenotypic deficits in these mice following ASO treatment. RESULTS: Transcriptional dysregulation was detected in the YAC128 mouse model and appears to progress with age. ICV delivery of ASOs directed against mutant Htt resulted in reduction in mutant Htt levels and amelioration in behavioral deficits in the YAC128 mouse model. These improvements were correlated with improvements in the levels of several dysregulated striatal transcripts. CONCLUSIONS: The role of transcriptional dysregulation in the pathogenesis of Huntington's disease is not well understood, however, a wealth of evidence now strongly suggests that changes in transcriptional signatures are a prominent feature in the brains of both HD patients and animal models of the disease. Our study is the first to show that a therapeutic agent capable of improving an HD disease phenotype is concomitantly correlated with normalization of a subset of dysregulated striatal transcripts. Our data suggests that correction of these disease-altered transcripts may underlie, at least in part, the therapeutic efficacy shown associated with ASO-mediated correction of HD phenotypes and may provide a novel set of early biomarkers for evaluating future therapeutic concepts for HD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".