Retinoic acid receptor-related orphan receptor α reduces lipid droplets by upregulating neutral cholesterol ester hydrolase 1 in macrophages
Bibliographic record
Abstract
BACKGROUND: Neutral cholesterol ester hydrolase 1 (NCEH1) catalyzes the hydrolysis of cholesterol ester (CE) in macrophages. Genetic ablation of NCEH1 promotes CE-laden macrophages and the development of atherosclerosis in mice. Dysregulation of NCEH1 levels is involved in the pathogenesis of multiple disorders including metabolic diseases and atherosclerosis; however, relatively little is known regarding the mechanisms regulating NCEH1. Retinoic acid receptor-related orphan receptor α (RORα)-deficient mice exhibit several phenotypes indicative of aberrant lipid metabolism, including dyslipidemia and increased susceptibility to atherosclerosis. RESULTS: In this study, inhibition of lipid droplet formation by RORα positively regulated NCEH1 expression in macrophages. In mammals, the NCEH1 promoter region was found to harbor putative RORα response elements (ROREs). Electrophoretic mobility shift, chromatin immunoprecipitation, and luciferase reporter assays showed that RORα binds and responds to ROREs in human NCEH1. Moreover, NCEH1 was upregulated through RORα via a phorbol myristate acetate-dependent mechanism during macrophage differentiation from THP1 cells. siRNA-mediated knockdown of RORα significantly downregulated NCEH1 expression and accumulated lipid droplets in human hepatoma cells. In contrast, NCEH1 expression and removal of lipid droplets were induced by RORα agonist treatments and RORα overexpression in macrophages. CONCLUSION: These data strongly suggested that NCEH1 is a direct RORα target, defining potential new roles for RORα in the inhibition of lipid droplet formation through NCEH1.
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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".