ACAT1 gene ablation increases 24(S)-hydroxycholesterol content in the brain and ameliorates amyloid pathology in mice with AD
Bibliographic record
Abstract
Cholesterol metabolism has been implicated in the pathogenesis of several neurodegenerative diseases, including the abnormal accumulation of amyloid-beta, one of the pathological hallmarks of Alzheimer disease (AD). Acyl-CoA:cholesterol acyltransferases (ACAT1 and ACAT2) are two enzymes that convert free cholesterol to cholesteryl esters. ACAT inhibitors have recently emerged as promising drug candidates for AD therapy. However, how ACAT inhibitors act in the brain has so far remained unclear. Here we show that ACAT1 is the major functional isoenzyme in the mouse brain. ACAT1 gene ablation (A1-) in triple transgenic (i.e., 3XTg-AD) mice leads to more than 60% reduction in full-length human APPswe as well as its proteolytic fragments, and ameliorates cognitive deficits. At 4 months of age, A1- causes a 32% content increase in 24-hydroxycholesterol (24SOH), the major oxysterol in the brain. It also causes a 65% protein content decrease in HMG-CoA reductase (HMGR) and a 28% decrease in sterol synthesis rate in AD mouse brains. In hippocampal neurons, A1- causes an increase in the 24SOH synthesis rate; treating hippocampal neuronal cells with 24SOH causes rapid declines in hAPP and in HMGR protein levels. A model is provided to explain our findings: in neurons, A1- causes increases in cholesterol and 24SOH contents in the endoplasmic reticulum, which cause reductions in hAPP and HMGR protein contents and lead to amelioration of amyloid pathology. Our study supports the potential of ACAT1 as a therapeutic target for treating certain forms of AD.
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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 | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".