The Cholesteryl Ester Transfer Protein (CETP) raises Cholesterol Levels in the Brain and affects Presenilin-mediated Gene Regulation
Bibliographic record
Abstract
Abstract The cholesteryl ester transfer protein (CETP) is a lipid transfer protein responsible for the exchange of cholesteryl esters and triglycerides between lipoproteins. Decreased CETP activity is associated with longevity, cardiovascular health, and maintenance of good cognitive performance. Interestingly, mice lack the CETP-encoding gene and have very low levels of low-density lipoprotein (LDL) particles compared to humans. To understand how CETP activity affects the brain, we utilised CETP transgenic (CETPtg) mice showing elevated LDL levels on a high cholesterol diet inducing CETP expression. We found that CETPtg mice had up to 25% higher cholesterol levels in the brain. Using a microarray on astrocyte-derived mRNA, we found that this cholesterol increase is likely not due to astrocytic-dependent de novo synthesis of cholesterol. Rather, several genes linked to Alzheimer’s disease were altered in CETPtg mice. Most interestingly, we found activation of the G-protein coupled receptor EP4 and γ-secretase as upstream regulators of these transcriptional changes. Further in vitro studies showed that CETP expression was sufficient to activate γ-secretase activity. The data suggest that CETP activity affects brain’s health through modulating cholesterol levels and Alzheimer’s-related pathways. Therefore, CETPtg mice constitute a valuable research tool to investigate the impact of the cholesterol metabolism on brain functions.
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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 | 0.001 |
| 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".