ABCG2 Is Upregulated in Alzheimer's Brain with Cerebral Amyloid Angiopathy and May Act as a Gatekeeper at the Blood–Brain Barrier for Aβ<sub>1–40</sub>Peptides
Bibliographic record
Abstract
Alzheimer's disease (AD) is characterized by accumulation and deposition of Abeta peptides in the brain. Abeta deposition in cerebrovessels occurs in many AD patients and results in cerebral amyloid angiopathy (AD/CAA). Since Abeta can be transported across blood-brain barrier (BBB), aberrant Abeta trafficking across BBB may contribute to Abeta accumulation in the brain and CAA development. Expression analyses of 273 BBB-related genes performed in this study showed that the drug transporter, ABCG2, was significantly upregulated in the brains of AD/CAA compared with age-matched controls. Increased ABCG2 expression was confirmed by Q-PCR, Western blot, and immunohistochemistry. Abcg2 was also increased in mouse AD models, Tg-SwDI and 3XTg. Abeta alone or in combination with hypoxia/ischemia failed to stimulate ABCG2 expression in BBB endothelial cells; however, conditioned media from Abeta-activated microglia strongly induced ABCG2 expression. ABCG2 protein in AD/CAA brains interacted and coimmunoprecipitated with Abeta. Overexpression of hABCG2 reduced drug uptake in cells; however, interaction of Abeta(1-40) with ABCG2 impaired ABCG2-mediated drug efflux. The role of Abcg2 in Abeta transport at the BBB was investigated in Abcg2-null and wild-type mice after intravenous injection of Cy5.5-labeled Abeta(1-40) or scrambled Abeta(40-1). Optical imaging analyses of live animals and their brains showed that Abcg2-null mice accumulated significantly more Abeta in their brains than wild-type mice. The finding was confirmed by immunohistochemistry. These results suggest that ABCG2 may act as a gatekeeper at the BBB to prevent blood Abeta from entering into brain. ABCG2 upregulation may serve as a biomarker of CAA vascular pathology in AD patients.
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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 | 0.001 |
| 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) | 0.002 | 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".