Proteomic differences in brain vessels of Alzheimer’s disease mice: Normalization by PPARγ agonist pioglitazone
Bibliographic record
Abstract
Cerebrovascular insufficiency appears years prior to clinical symptoms in Alzheimer's disease. The soluble, highly toxic amyloid-β species, generated from the amyloidogenic processing of amyloid precursor protein, are known instigators of the chronic cerebrovascular insufficiency observed in both Alzheimer's disease patients and transgenic mouse models. We have previously demonstrated that pioglitazone potently reverses cerebrovascular impairments in a mouse model of Alzheimer's disease overexpressing amyloid-β. In this study, we sought to characterize the effects of amyloid-β overproduction on the cerebrovascular proteome; determine how pioglitazone treatment affected the altered proteome; and analyze the relationship between normalized protein levels and recovery of cerebrovascular function. Three-month-old wildtype and amyloid precursor protein mice were treated with pioglitazone- (20 mg/kg/day, 14 weeks) or control-diet. Cerebral arteries were surgically isolated, and extracted proteins analyzed by gel-free and gel-based mass spectrometry. 193 cerebrovascular proteins were abnormally expressed in amyloid precursor protein mice. Pioglitazone treatment rescued a third of these proteins, mainly those associated with oxidative stress, promotion of cerebrovascular vasocontractile tone, and vascular compliance. Our results demonstrate that amyloid-β overproduction perturbs the cerebrovascular proteome. Recovery of cerebrovascular function with pioglitazone is associated with normalized levels of key proteins in brain vessel function, suggesting that pioglitazone-responsive cerebrovascular proteins could be early biomarkers of Alzheimer's 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".