A Rosella-PLIN2 Knock-in Mouse Reveals Lipophagy and Immunometabolic Interplay in Atherosclerosis
Bibliographic record
Abstract
Abstract Cytosolic lipid droplets (LDs) regulate lipid homeostasis, with abnormal LD dynamics linked to metabolic diseases like atherosclerosis. In macrophage foam cells, LDs undergo autophagic degradation via lipophagy, but the extent of this process in vascular smooth muscle cell (VSMC) foam cells remains unclear. To track lipophagy in real time, we developed a Rosella-PLIN2 biosensor by tagging perilipin 2 (PLIN2) with the fluorescent pH-biosensor Rosella. We show that proatherogenic lipoproteins and autophagy activators stimulate lipophagy in human macrophages. Targeting LDs with an LC3 fusion protein or LD-autophagy tethering compounds (LD-ATTECs) selectively enhanced lipophagy, promoting foam cell LD clearance. In an atherosclerosis model, Rosella-PLIN2 accurately tracked lipophagy in arterial foam cells, revealing distinct PLIN2 expression patterns in macrophage and non-leukocyte foam cells. We identified a lipophagy deficiency in VSMC foam cells and demonstrate that enhancing lipophagy promotes LD catabolism in primary VSMC foam cells. TREM2 + macrophages exhibited high lipid content and low lipophagy flux, whereas TREM2 - macrophages had low lipid content and high lipophagy flux. Our findings highlight a cell-specific interplay between lipophagy and immunometabolism in arterial foam cells, unveiling novel therapeutic avenues for atherosclerosis. Additionally, the Rosella-PLIN2 model provides a powerful tool for studying LD metabolism, offering new insights into lipid homeostasis and disease mechanisms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".