Scavenger receptor CD36 mediates inhibition of cholesterol synthesis <i>via</i> activation of the PPARγ/PGC‐1α pathway and Insig1/2 expression in hepatocytes
Bibliographic record
Abstract
The scavenger receptor CD36 plays a central role in lipid metabolism by promoting macrophage cholesterol efflux with the potential to reduce atherosclerotic lesions. However, the effect of CD36 on de novo cholesterol synthesis is not known. Here, we describe the cellular mechanism by which CD36 activation induces cholesterol depletion in HepG2 cells. Using the CD36 ligand hexarelin, we found a rapid phosphorylation of HMG-CoA reductase Ser-872 in treated cells, resulting in inactivation of the rate-limiting enzyme in sterol synthesis. Degradation of HMG-CoA reductase by the ubiquitin-proteasome pathway was also enhanced by hexarelin, through an increased recruitment of the anchor proteins insulin-induced gene (Insig)-1 and Insig-2. Genes encoding key enzymes involved in cholesterol synthesis and under the control of transcription factor sterol regulatory element-binding protein (SREBP)-2 remained unresponsive to sterol depletion, due to retention of the SREBP-2 escort protein Scap by Insig-1/2. Insig1 and Insig2 gene expression was also increased through activation of nuclear receptor peroxisome-proliferator activating receptor γ (PPARγ) by CD36, which lifted the inhibitory effect of PPARγ1 Ser-84 phosphorylation. Recruitment of coactivator peroxisome proliferator-activated receptor-γ coactivator 1α (PGC1α) to activated AMPKα was also promoted, resulting in PGC-1α transcriptional activation through Sirt1-mediated deacetylation, increased recruitment of PPARγ, and up-regulation of Insig-1/2, revealing a regulatory role of CD36 on PGC-1α signaling. Our data identify CD36 as a novel regulator of HMG-CoA reductase function and Insig-1/2 expression, 2 critical steps regulating cholesterol synthesis in hepatocytes.
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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 | 0.000 |
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".