Lipoprotein Lipase Regulates Microglial Lipid Droplet Accumulation
Bibliographic record
Abstract
Microglia become increasingly dysfunctional with aging and contribute to the onset of neurodegenerative disease (NDs) through defective phagocytosis, attenuated cholesterol efflux, and excessive secretion of pro-inflammatory cytokines. Dysfunctional microglia also accumulate lipid droplets (LDs); however, the mechanism underlying increased LD load is unknown. We have previously shown that microglia lacking lipoprotein lipase (LPL KD) are polarized to a pro-inflammatory state and have impaired lipid uptake and reduced fatty acid oxidation (FAO). Here, we also show that LPL KD microglia show excessive accumulation of LD-like structures. Moreover, LPL KD microglia display a pro-inflammatory lipidomic profile, increased cholesterol ester (CE) content, and reduced cholesterol efflux at baseline. We also show reduced expression of genes within the canonical cholesterol efflux pathway. Importantly, PPAR agonists (rosiglitazone and bezafibrate) rescued the LD-associated phenotype in LPL KD microglia. These data suggest that microglial-LPL is associated with lipid uptake, which may drive PPAR signaling and cholesterol efflux to prevent inflammatory lipid distribution and LD accumulation. Moreover, PPAR agonists can reverse LD accumulation, and therefore may be beneficial in aging and in the treatment of NDs.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".