Age-Dependent Regulation of the Blood-Brain Barrier Influx/Efflux Equilibrium of Amyloid-β Peptide in a Mouse Model of Alzheimer’s Disease (3xTg-AD)
Bibliographic record
Abstract
The involvement of transporters located at the blood-brain barrier (BBB) has been suggested in the control of cerebral Aβ levels, and thereby in Alzheimer's disease (AD). However, little is known about the regulation of these transporters at the BBB in animal models of AD. In this study, we investigated the BBB expression of Aβ influx (Rage) and efflux (Abcb1-Abcg2-Abcg4-Lrp-1) transporters and cholesterol transporter (Abca1) in 3-18-month-old 3xTg-AD and control mice. The age-dependent effect of BBB transporters regulation on the brain uptake clearance (Clup) of [3H]cholesterol and [3H]Aβ1 - 40 was then evaluated in these mice, using the in situ brain perfusion technique. Our data suggest that transgenes expression led to the BBB increase in Aβ influx receptor (Rage) and decrease in efflux receptor (Lrp-1). Our data also indicate that mice have mechanisms counteracting this increased net influx. Indeed, Abcg4 and Abca1 are up regulated in 3- and 3/6-month-old 3xTg-AD mice, respectively. Our data show that the balance between the BBB influx and efflux of Aβ is maintained in 3 and 6-month-old 3xTg-AD mice, suggesting that Abcg4 and Abca1 control the efflux of Aβ through the BBB by a direct (Abcg4) or indirect (Abca1) mechanism. At 18 months, the BBB Aβ efflux is significantly increased in 3xTg-AD mice compared to controls. This could result from the significant up-regulation of both Abcg2 and Abcb1 in 3xTg-AD mice compared to control mice. Thus, age-dependent regulation of several Aβ and cholesterol transporters at the BBB could ultimately limit the brain accumulation of Aβ.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".