Free Cholesterol Accumulation in Macrophage Membranes Activates Toll-Like Receptors and p38 Mitogen-Activated Protein Kinase and Induces Cathepsin K
Bibliographic record
Abstract
The molecular events linking lipid accumulation in atherosclerotic plaques to complications such as aneurysm formation and plaque disruption are poorly understood. BALB/c-Apoe(-/-) mice bearing a null mutation in the Npc1 gene display prominent medial erosion and atherothrombosis, whereas their macrophages accumulate free cholesterol in late endosomes and show increased cathepsin K (Ctsk) expression. We now show increased cathepsin K immunostaining and increased cysteinyl proteinase activity using near infrared fluorescence imaging over proximal aortas of Apoe(-/-), Npc1(-/-) mice. In mechanistic studies, cholesterol loading of macrophage plasma membranes (cyclodextrin-cholesterol) or endosomal system (AcLDL+U18666A or Npc1 null mutation) activated Toll-like receptor (TLR) signaling, leading to sustained phosphorylation of p38 mitogen-activated protein kinase and induction of p38 targets, including Ctsk, S100a8, Mmp8, and Mmp14. Studies in macrophages from knockout mice showed major roles for TLR4, following plasma membrane cholesterol loading, and for TLR3, after late endosomal loading. TLR signaling via p38 led to phosphorylation and activation of the transcription factor Microphthalmia transcription factor, acting at E-box elements in the Ctsk promoter. These studies suggest that free cholesterol enrichment of either plasma or endosomal membranes in macrophages leads to activation of signaling via various TLRs, prolonged p38 mitogen-activated protein kinase activation, and induction of Mmps, Ctsk, and S100a8, potentially contributing to plaque complications.
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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 | 0.000 |
| 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".