IL-33 Reduces Macrophage Foam Cell Formation
Bibliographic record
Abstract
The development of atherosclerosis, a chronic inflammatory disease characterized by the formation of arterial fibrotic plaques, has been shown to be reduced by IL-33 in vivo. However, whether IL-33 can directly affect macrophage foam cell formation, a key feature of atherosclerotic plaques, has not been determined. In this study, we investigated whether IL-33 reduces macrophage foam cell accumulation in vivo and if IL-33 reduces their formation in vitro using THP-1 and primary human monocyte-derived macrophages. In Apolipoprotein E(-/-) mice fed on a high fat diet, IL-33 treatment significantly reduced the accumulation of macrophage-derived foam cells in atherosclerotic plaques. IL-33 also reduced macrophage foam cell formation in vitro by decreasing acetylated and oxidized low-density lipoprotein uptake, reducing intracellular total and esterified cholesterol content and enhancing cholesterol efflux. These changes were associated with IL-33-mediated reduction in the expression of genes involved in modified low-density lipoprotein uptake, such as CD36, and simultaneous increase in genes involved in cholesterol efflux, including Apolipoprotein E, thereby providing a mechanism for such an action for this cytokine. IL-33 also decreased the expression of key genes implicated in cholesterol esterification and triglyceride storage, including Acyl-CoA:cholesterol acyltransferase 1 and Adipocyte differentiation-related protein. Furthermore, using bone marrow-derived macrophages from ST2(-/-) mice, we demonstrate that the IL-33 receptor, ST2, is integral to the action of IL-33 on macrophage foam cell formation. In conclusion, IL-33 has a protective role in atherosclerosis by reducing macrophage foam cell formation suggesting that IL-33 maybe a potential therapeutic agent against 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) | 0.002 | 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".