Calpain inhibition mediates autophagy-dependent protection against polyglutamine toxicity
Bibliographic record
Abstract
Over recent years, accumulated evidence suggests that autophagy induction is protective in animal models of a number of neurodegenerative diseases. Intense research in the field has elucidated different pathways through which autophagy can be upregulated and it is important to establish how modulation of these pathways impacts upon disease progression in vivo and therefore which, if any, may have further therapeutic relevance. In addition, it is important to understand how alterations in these target pathways may affect normal physiology when constitutively modulated over a long time period, as would be required for treatment of neurodegenerative diseases. Here we evaluate the potential protective effect of downregulation of calpains. We demonstrate, in Drosophila, that calpain knockdown protects against the aggregation and toxicity of proteins, like mutant huntingtin, in an autophagy-dependent fashion. Furthermore, we demonstrate that, overexpression of the calpain inhibitor, calpastatin, increases autophagosome levels and is protective in a mouse model of Huntington's disease, improving motor signs and delaying the onset of tremors. Importantly, long-term inhibition of calpains did not result in any overt deleterious phenotypes in mice. Thus, calpain inhibition, or activation of autophagy pathways downstream of calpains, may be suitable therapeutic targets for diseases like Huntington's disease.
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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 | 0.000 | 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) | 0.001 | 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".