Macrophage SR-BI modulates autophagy via VPS34 complex and PPARα transcription of Tfeb in atherosclerosis
Bibliographic record
Abstract
Autophagy modulates lipid turnover, cell survival, inflammation, and atherogenesis. Scavenger receptor class B type I (SR-BI) plays a crucial role in lysosome function. Here, we demonstrate that SR-BI regulates autophagy in atherosclerosis. SR-BI deletion attenuated lipid-induced expression of autophagy mediators in macrophages and atherosclerotic aortas. Consequently, SR-BI deletion resulted in 1.8- and 2.5-fold increases in foam cell formation and apoptosis, respectively, and increased oxidized LDL-induced inflammatory cytokine expression. Pharmacological activation of autophagy failed to reduce lipid content or apoptosis in Sr-b1-/- macrophages. SR-BI deletion reduced both basal and inducible levels of transcription factor EB (TFEB), a master regulator of autophagy, causing decreased expression of autophagy genes encoding VPS34 and Beclin-1. Notably, SR-BI regulated Tfeb expression by enhancing PPARα activation. Moreover, intracellular macrophage SR-BI localized to autophagosomes, where it formed cholesterol domains resulting in enhanced association of Barkor and recruitment of the VPS34-Beclin-1 complex. Thus, SR-BI deficiency led to lower VPS34 activity in macrophages and in atherosclerotic aortic tissues. Overexpression of Tfeb or Vps34 rescued the defective autophagy in Sr-b1-/- macrophages. Taken together, our results show that macrophage SR-BI regulates autophagy via Tfeb expression and recruitment of the VPS34-Beclin-1 complex, thus identifying previously unrecognized roles for SR-BI and potentially novel targets for the treatment of 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 | 0.000 |
| 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".