Apolipoprotein A1 Protects Against Necrotic Core Development in Atherosclerotic Plaques: PDZK1-Dependent High-Density Lipoprotein Suppression of Necroptosis in Macrophages
Bibliographic record
Abstract
BACKGROUND: Atherosclerosis is a chronic disease affecting artery wall and a major contributor to cardiovascular diseases. Large necrotic cores increase risk of plaque rupture leading to thrombus formation. Necrotic cores are rich in debris from dead macrophages. Programmed necrosis (necroptosis) contributes to necrotic core formation. HDL (high-density lipoprotein) exerts direct atheroprotective effects on different cells within atherosclerotic plaques. Some of these depend on the SR-B1 (scavenger receptor class B type I) and the adapter protein PDZK1 (postsynaptic density protein/Drosophila disc-large protein/Zonula occludens protein containing 1). However, a role for HDL in protecting against necroptosis and necrotic core formation in atherosclerosis is not completely understood. METHODS: Low-density lipoprotein receptor-deficient mice engineered to express different amounts of ApoA1 (apolipoprotein A1), or to lack PDZK1 were fed a high fat diet for 10 weeks. Atherosclerotic plaque areas, necrotic cores, and key necroptosis mediators, RIPK3 (receptor interacting protein kinase 3), and MLKL (mixed lineage kinase domain-like protein) were characterized. Cultured macrophages were treated with HDL to determine its effects, as well as the roles of SR-B1, PDZK1, and the PI3K (phosphoinositide 3-kinase) signaling pathway on necroptotic cell death. RESULTS: mice, increasing necrotic cores and phospho-MLKL; both of which were reversed by restoring PDZK1 in BM-derived cells. CONCLUSIONS: Our findings demonstrate that HDL in vitro and ApoA1, in vivo, protect against necroptosis in macrophages and necrotic core formation in atherosclerosis, suggesting a pathway that could be a target for the treatment of atherosclerosis.
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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".