Selective histone deacetylase (HDAC) inhibition imparts beneficial effects in Huntington's disease mice: implications for the ubiquitin–proteasomal and autophagy systems
Bibliographic record
Abstract
We previously demonstrated that the histone deacetylase (HDAC) inhibitor, 4b, which preferentially targets HDAC1 and HDAC3, ameliorates Huntington's disease (HD)-related phenotypes in different HD model systems. In the current study, we investigated extensive behavioral and biological effects of 4b in N171-82Q transgenic mice and further explored potential molecular mechanisms of 4b action. We found that 4b significantly prevented body weight loss, improved several parameters of motor function and ameliorated Huntingtin (Htt)-elicited cognitive decline in N171-82Q transgenic mice. Pathways analysis of microarray data from the mouse brain revealed gene networks involving post-translational modification, including protein phosphorylation and ubiquitination pathways, associated with 4b drug treatment. Using real-time qPCR analysis, we validated differential regulation of several genes in these pathways by 4b, including Ube2K, Ubqln, Ube2e3, Usp28 and Sumo2, as well as several other related genes. Additionally, 4b elicited increases in the expression of genes encoding components of the inhibitor of kappaB kinase (IKK) complex. IKK activation has been linked to phosphorylation, acetylation and clearance of the Htt protein by the proteasome and the lysosome, and accordingly, we found elevated levels of phosphorylated endogenous wild-type (wt) Htt protein at serine 16 and threonine 3, and increased AcK9/pS13/pS16 immunoreactivity in cortical samples from 4b-treated mice. We further show that HDAC inhibitors prevent the formation of nuclear Htt aggregates in the brains of N171-82Q mice. Our findings suggest that one mechanism of 4b action is associated with the modulation of the ubiquitin-proteasomal and autophagy pathways, which could affect accumulation, stability and/or clearance of important disease-related proteins, such as Htt.
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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 | 0.001 | 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".