Inhibition of transglutaminase 2 mitigates transcriptional dysregulation in models of Huntington disease
Bibliographic record
Abstract
Caused by a polyglutamine expansion in the huntingtin protein, Huntington's disease leads to striatal degeneration via the transcriptional dysregulation of a number of genes, including those involved in mitochondrial biogenesis. Here we show that transglutaminase 2, which is upregulated in HD, exacerbates transcriptional dysregulation by acting as a selective corepressor of nuclear genes; transglutaminase 2 interacts directly with histone H3 in the nucleus. In a cellular model of HD, transglutaminase inhibition de-repressed two established regulators of mitochondrial function, PGC-1alpha and cytochrome c and reversed susceptibility of human HD cells to the mitochondrial toxin, 3-nitroproprionic acid; however, protection mediated by transglutaminase inhibition was not associated with improved mitochondrial bioenergetics. A gene microarray analysis indicated that transglutaminase inhibition normalized expression of not only mitochondrial genes but also 40% of genes that are dysregulated in HD striatal neurons, including chaperone and histone genes. Moreover, transglutaminase inhibition attenuated degeneration in a Drosophila model of HD and protected mouse HD striatal neurons from excitotoxicity. Altogether these findings demonstrate that selective TG inhibition broadly corrects transcriptional dysregulation in HD and defines a novel HDAC-independent epigenetic strategy for treating neurodegeneration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".