mGluR5 Contribution to Neuropathology in Alzheimer Mice Is Disease Stage-Dependent
Bibliographic record
Abstract
Alzheimer's disease (AD) is the most prevalent neurodegenerative disease and is characterized by a progressive cognitive decline in affected individuals. Current therapeutic strategies are limited in their efficacy and some have proven to be even less effective at later disease stages or after extended use. We previously demonstrated that chronic inhibition of mGluR5 signaling using the selective negative allosteric modulator (NAM) CTEP in APPswe/PS1ΔE9 mice can rescue cognitive function, activating the ZBTB16-mediated autophagy pathway to reduce Aβ, the principal neurotoxic species in AD brains. Here, we evaluated the efficacy of long-term treatment with CTEP in 6 month old APPswe/PS1ΔE9 mice for either 24 or 36 weeks. CTEP maintained its efficacy in reversing working and spatial memory deficits and mitigating neurogliosis in APPswe/PS1ΔE9 mice when administered for 24 weeks. This was paralleled by a significant reduction in Aβ oligomer and plaque load as a result of autophagy activation via ZBTB16 and mTOR-dependent pathways. However, further extension of CTEP treatment for 36 weeks was found ineffective in reversing memory deficit, neurogliosis, or Aβ-related pathology. We found that this loss in CTEP efficacy in 15 month old APPswe/PS1ΔE9 mice was due to the abolished contribution of ZBTB16 and mTOR-mediated signaling to AD neuropathology at this advanced disease stage. Our findings indicate that the contribution of pathological mGluR5-signaling to AD may shift as the disease progresses. Thus, we provide the first evidence that the underlying pathophysiological mechanism(s) of AD may unfold along the course of the disease and treatment strategies should be modified accordingly to ensure maximal therapeutic outcomes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".