Sex‐biased mGluR5 pharmacology and pathophysiological signaling in Alzheimer's disease
Bibliographic record
Abstract
Sex is an important modifier of the prevalence and progression of Alzheimer's disease (AD). β‐Amyloid (Aβ) deposition is a pathological hallmark of AD and aberrant activation of metabotropic glutamate receptor 5 (mGluR5) by Aβ has been linked to AD progression. We find that mGluR5 exhibits distinct and unexpected sex‐selective pharmacological profiles. Specifically, the mGluR5 forms a ternary complex with Aβ oligomer and cellular prion protein (PrP C ) to elicit mGluR5‐dependent pathological signaling in male but not female mouse brain. The inability of mGluR5 to scaffold with PrP C abolishes the affinity of Aβ oligomers to mGluR5 in female mouse brain. The latter observation was validated in postmortem human brain indicating that this phenomenon is evolutionarily conserved. The sex‐specific differences in mGluR5 pharmacology translate into in vivo differences in mGluR5‐dependent pathological signaling between male and female APPswe/PS1ΔE9 mice. We show that mGluR5 inhibition using a selective negative allosteric modulator reverses cognitive decline and Aβ oligomer pathology in male, but not female, APPswe/PS1ΔE9 mice. The improved Aβ pathology in male APPswe/PS1ΔE9 mice was due to mGluR5‐mediated reactivation of a GSK3β/ZBTB16‐regulated autophagy mechanism. Surprisingly, GSK3β/ZBTB16‐regulated autophagy was not altered in female APPswe/PS1ΔE9. Thus, it is evident that, unlike male brain, mGluR5 does not contribute to Aβ pathology in female brain. This study highlights the complexity of mGluR5 pharmacology and Aβ oligomer‐activated signaling and emphasizes the need for novel targets for AD treatment in females.
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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".