Modulation of mTOR and CREB pathways following mGluR5 blockade contribute to improved Huntington’s pathology in zQ175 mice
Bibliographic record
Abstract
Huntington's disease (HD) is a neurodegenerative disorder caused by a genetic abnormality in the huntingtin gene that leads to a polyglutamine repeat expansion of the huntingtin protein. The cleaved polyglutamine expansion of mutant huntingtin (mHTT) protein can form aggregates strongly correlated with HD progression. We have previously shown that the inhibition of mGluR5 using CTEP, a selective negative allosteric mGluR5 modulator, can delay disease progression and reduce in mHTT aggregates in the zQ175 mouse model of HD. This was paralleled by enhanced catalytic activity of Unc-51-like kinase 1 (ULK1), a kinase modulated by mammalian target of rapamycin (mTOR) and key regulator of autophagy initiation. In the present study, we show that CTEP can correct aberrant phosphoinositide 3-kinase (PI3K)/Akt/mTOR signaling detected in zQ175 mice that may underlie the enhanced ULK1 activity and activation of autophagy. We also show that CTEP can facilitate cAMP response element-binding protein (CREB)-mediated expression of brain-derived neurotrophic factor (BDNF) to foster neuronal survival and reduce apoptosis. Taken together, our findings provide the molecular evidence for how targeting mGluR5 using a well-tolerated selective NAM can mitigate two critical mechanisms of neurodegeneration, autophagy and apoptosis.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".