β-Amyloid Inhibits Protein Prenylation and Induces Cholesterol Sequestration by Impairing SREBP-2 Cleavage
Bibliographic record
Abstract
Accumulation of β-amyloid (Aβ) inside brain neurons is an early and crucial event in Alzheimer's disease (AD). Studies in brains of AD patients and mice models of AD suggested that cholesterol homeostasis is altered in neurons that accumulate Aβ. Here we directly investigated the role of intracellular oligomeric Aβ(42) (oAβ(42)) in neuronal cholesterol homeostasis. We report that oAβ(42) induces cholesterol sequestration without increasing cellular cholesterol mass. Several features of AD, such as endosomal abnormalities, brain accumulation of Aβ and neurofibrillary tangles, and influence of apolipoprotein E genotype, are also present in Niemann-Pick type C, a disease characterized by impairment of intracellular cholesterol trafficking. These common features and data presented here suggest that a pathological mechanism involving abnormal cholesterol trafficking could take place in AD. Cholesterol sequestration in Aβ-treated neurons results from impairment of intracellular cholesterol trafficking secondary to inhibition of protein prenylation. oAβ(42) reduces sterol regulatory element-binding protein-2 (SREBP-2) cleavage, causing decrease of protein prenylation. Inhibition of protein prenylation represents a mechanism of oAβ(42)-induced neuronal death. Supply of the isoprenoid geranylgeranyl pyrophosphate to oAβ(42)-treated neurons recovers normal protein prenylation, reduces cholesterol sequestration, and prevents Aβ-induced neurotoxicity. Significant to AD, reduced levels of protein prenylation are present in the cerebral cortex of the TgCRND8 mouse model. In conclusion, we demonstrate a significant inhibitory effect of Aβ on protein prenylation and identify SREBP-2 as a target of oAβ(42), directly linking Aβ to cholesterol homeostasis impairment.
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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) | 0.000 | 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".