Autophagy is increased following either pharmacological or genetic silencing of mGluR5 signaling in Alzheimer’s disease mouse models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Alzheimer's disease (AD) is characterized by neurotoxicity mediated by the accumulation of beta amyloid (Aβ) oligomers, causing neuronal loss and progressive cognitive decline. Genetic deletion or chronic pharmacological inhibition of mGluR5 by the negative allosteric modulator CTEP, rescues cognitive function and reduces Aβ aggregation in both APPswe/PS1ΔE9 and 3xTg-AD mouse models of AD. In late onset neurodegenerative diseases, such as AD, defects arise at different stages of the autophagy pathway. Here, we show that mGluR5 cell surface expression is elevated in APPswe/PS1ΔE9 and 3xTg-AD mice. This is accompanied by reduced autophagy (accumulation of p62) as the consequence of increased ZBTB16 expression and reduced ULK1 activity, as we have previously observed in Huntington's disease (HD). The chronic (12 week) inhibition of mGluR5 with CTEP in APPswe/PS1ΔE9 and 3xTg-AD mice prevents the observed increase in mGluR5 surface expression. In addition, mGluR5 inactivation facilitates the loss of ZBTB16 expression and ULK1 activation as a consequence of ULK-Ser757 dephosphorylation, which promotes the loss of expression of the autophagy marker p62. Moreover, the genetic ablation of mGluR5 in APPswe/PS1ΔE9 mice activated autophagy via similar mechanisms to pharmacological blockade. This study provides further evidence that mGluR5 overactivation contributes to inhibition of autophagy and can result in impaired clearance of neurotoxic aggregates in multiple neurodegenerative diseases. Thus, it provides additional support for the potential of mGluR5 inhibition as a general therapeutic strategy for neurodegenerative diseases such as AD and HD.
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.
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) | 0.001 | 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 it