Rescue of aberrant huntingtin palmitoylation ameliorates mutant huntingtin-induced toxicity
Bibliographic record
Abstract
Huntington disease (HD) is a neurodegenerative disorder caused by a CAG expansion in the HTT gene that codes for an elongated polyglutamine tract in the huntingtin (HTT) protein. HTT is subject to multiple post-translational modifications (PTMs) that regulate its cellular function. Mutating specific PTM sites within mutant HTT (mHTT) in HD mouse models can modulate disease phenotypes, highlighting the key role of HTT PTMs in the pathogenesis of HD. These findings have led to increased interest in developing small molecules to modulate HTT PTMs in order to decrease mHTT toxicity. However, the therapeutic efficacy of pharmacological modulation of HTT PTMs in preclinical HD models remains largely unknown. HTT is palmitoylated at cysteine 214 by the huntingtin-interacting protein 14 (HIP14 or ZDHHC17) and 14-like (HIP14L or ZDHHC13) acyltransferases. Here, we assessed if HTT palmitoylation should be regarded as a therapeutic target to treat HD by (1) investigating palmitoylation dysregulation in rodent and human HD model systems, (2) measuring the impact of mHTT-lowering therapy on brain palmitoylation, and (3) evaluating if HTT palmitoylation can be pharmacologically modulated. We show that palmitoylation of mHTT and some HIP14/HIP14L-substrates is decreased early in multiple HD mouse models, and that mHTT palmitoylation decreases further with aging. Lowering mHTT in the brain of YAC128 mice is not sufficient to rescue aberrant palmitoylation. However, we demonstrate that mHTT palmitoylation can be normalized in COS-7 cells, in YAC128 cortico-striatal primary neurons and HD patient-derived lymphoblasts using an acyl-protein thioesterase (APT) inhibitor. Moreover, we show that modulating palmitoylation reduces mHTT aggregation and mHTT-induced cytotoxicity in COS-7 cells and YAC128 neurons.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".