Adipocyte enhancer-binding protein 1 is a potential novel atherogenic factor involved in macrophage cholesterol homeostasis and inflammation
Bibliographic record
Abstract
Peroxisome proliferator-activated receptor gamma1 (PPARgamma1) and liver X receptor alpha (LXRalpha) play pivotal roles in macrophage cholesterol homeostasis and inflammation, key biological processes in atherogenesis. Herein we identify adipocyte enhancer-binding protein 1 (AEBP1) as a transcriptional repressor that impedes macrophage cholesterol efflux, promoting foam cell formation, via PPARgamma1 and LXRalpha down-regulation. Contrary to AEBP1 deficiency, AEBP1 overexpression in macrophages is accompanied by decreased expression of PPARgamma1, LXRalpha, and their target genes ATP-binding cassette A1, ATP-binding cassette G1, apolipoprotein E, and CD36, with concomitant elevation in IL-6, TNF-alpha, monocyte chemoattractant protein 1, and inducible NO synthase levels. AEBP1, but not the C-terminally truncated DNA-binding domain mutant (AEBP1DeltaSty), represses PPARgamma1 and LXRalpha in vitro. Expectedly, AEBP1-overexpressing transgenic (AEBP1TG) macrophages accumulate considerable amounts of lipids compared with AEBP1 nontransgenic macrophages, making them precursors for foam cells. Indeed, AEBP1-overexpressing transgenic macrophages exhibit diminished cholesterol efflux compared with AEBP1 nontransgenic macrophages, whereas AEBP1-knockout (AEBP1-/-) macrophages exhibit enhanced cholesterol efflux compared with wild-type (AEBP1+/+) macrophages. Our in vitro and ex vivo experimental data strongly suggest that AEBP1 plays critical regulatory roles in macrophage cholesterol homeostasis, foam cell formation, and proinflammation. Thereby, we speculate that AEBP1 may be critically implicated in the development of atherosclerosis, and it may serve as a molecular target toward developing antiinflammatory, antiatherogenic therapeutic approaches.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".