ABCG2- and ABCG4-Mediated Efflux of Amyloid-β Peptide 1-40 at the Mouse Blood-Brain Barrier
Bibliographic record
Abstract
The accumulation of amyloid-β peptide (Aβ) in the brain is a critical hallmark of Alzheimer's disease. This high cerebral Aβ concentration may be partly caused by impaired clearance of Aβ across the blood-brain barrier (BBB). The low-density lipoprotein receptor-related protein-1 (LRP-1) and the ATP-binding cassette (ABC) protein ABCB1 (P-glycoprotein) are involved in the efflux of Aβ across the BBB. We hypothesized that other ABC proteins, such as members of the G subfamily, are also involved in the BBB clearance of Aβ. We therefore investigated the roles of ABCG2 (BCRP) and ABCG4 in the efflux of [3H] Aβ1-40 from HEK293 cells stably transfected with human ABCG2 or mouse abcg4. We showed that ABCG2 and Abcg4 mediate the cellular efflux of [3H] Aβ1-40. In addition, probucol fully inhibited the efflux of [3H] Aβ1-40 from HEK293-abcg4 cells. Using the in situ brain perfusion technique, we showed that GF120918 (dual inhibitor of Abcb1 and Abcg2) strongly enhanced the uptake (Clup, μl/g/s) of [3H] Aβ1-40 by the brains of Abcb1-deficient mice, but not by the brains of Abcb1/Abcg2-deficient mice, suggesting that Abcg2 is involved in the transport of Aβ at the mouse BBB. Perfusing the brains of Abcb1/Abcg2- and Abca1-deficient mice with [3H] Aβ1-40 plus probucol significantly increased the Clup of Aβ. This suggests that a probucol-sensitive transporter that is different from Abca1, Abcb1, and Abcg2 is involved in the brain efflux of Aβ. We suggest that this probucol-sensitive transporter is Abcg4. We conclude that Abcg4 acts in concert with Abcg2 to efflux Aβ from the brain across the BBB.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".