Pharmacological inhibition of O-GlcNAcase (OGA) prevents cognitive decline and amyloid plaque formation in bigenic tau/APP mutant mice
Bibliographic record
Abstract
BACKGROUND: Amyloid plaques and neurofibrillary tangles (NFTs) are the defining pathological hallmarks of Alzheimer's disease (AD). Increasing the quantity of the O-linked N-acetylglucosamine (O-GlcNAc) post-translational modification of nuclear and cytoplasmic proteins slows neurodegeneration and blocks the formation of NFTs in a tauopathy mouse model. It remains unknown, however, if O-GlcNAc can influence the formation of amyloid plaques in the presence of tau pathology. RESULTS: We treated double transgenic TAPP mice, which express both mutant human tau and amyloid precursor protein (APP), with a highly selective orally bioavailable inhibitor of the enzyme responsible for removing O-GlcNAc (OGA) to increase O-GlcNAc in the brain. We find that increased O-GlcNAc levels block cognitive decline in the TAPP mice and this effect parallels decreased β-amyloid peptide levels and decreased levels of amyloid plaques. CONCLUSIONS: This study indicates that increased O-GlcNAc can influence β-amyloid pathology in the presence of tau pathology. The findings provide good support for OGA as a promising therapeutic target to alter disease progression in Alzheimer 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) | 0.001 | 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) | 0.001 | 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".