The cholesterol transporter ABCG1 modulates the subcellular distribution and proteolytic processing of β-amyloid precursor protein
Bibliographic record
Abstract
Although intracellular cholesterol levels are known to influence the proteolysis of beta-amyloid precursor protein (APP), the effect of specific genes that regulate cholesterol metabolism on APP processing remains poorly understood. The cholesterol transporter ABCG1 facilitates cholesterol efflux to HDL and is expressed in brain. Notably, the human ABCG1 gene maps to chromosome 21q22.3, and individuals with Down syndrome (DS) typically manifest with Alzheimer's disease (AD) neuropathology in their 30s. Here, we demonstrate that expression of ABCG1 enhances amyloid-beta protein (Abeta) production in transfected HEK cells in a manner that requires functional cholesterol transporter activity. ABCG1-expressing cells also exhibit increased secreted APP (sAPP)alpha and sAPPbeta secretion and display increased cell surface-associated APP. These results suggest that ABCG1 increases the availability of APP as a secretase substrate for both the amyloidogenic and nonamyloidogenic pathways. In vivo, ABCG1 mRNA levels are 2-fold more abundant in DS brain compared with age- and sex-matched normal controls. Finally, both Abeta and sAPPalpha levels are increased in DS cortex relative to normal controls. These findings suggest that altered cholesterol metabolism and APP trafficking mediated by ABCG1 may contribute to the accelerated onset of AD neuropathology in DS.
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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 | 0.000 | 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 | 0.000 |
| 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".