DGAT1-dependent lipid droplet synthesis in microglia attenuates neuroinflammatory responses to lipopolysaccharides
Bibliographic record
Abstract
ABSTRACT Lipid droplets (LD) are dynamic storage organelles for triglycerides (TG). LD act as a hub that modulates the availability of fatty acids to sustain metabolic needs and the generation of fatty-acid derived signals. Recent evidence demonstrates that LD metabolism regulates immune responses including in microglia, the resident immune cells of the central nervous system. We have previously shown that blocking LD lipolysis in microglia reduces acute pro-inflammatory responses to lipopolysaccharide (LPS) including cytokine and prostanoid synthesis. Here, we investigated the role of diacylglycerol O-acyltransferase 1 (DGAT1), a key enzyme catalyzing the final step of TG synthesis, in microglial LD biogenesis and inflammatory responses induced by LPS. We found that treatment with LPS downregulates specific enzymes in the TG synthesis pathway in primary microglia, including GPAT1, AGPAT3, AGPAT5, and DGAT1, while upregulating TMEM68, a non-canonical TG synthesizing enzyme. Pharmacological inhibition of DGAT1 significantly reduced LD formation in both oleate- and LPS-stimulated conditions, indicating that DGAT1 is essential for inflammation-induced LD synthesis. Moreover, DGAT1 inhibition selectively decreased expression of pro-inflammatory cytokines TNF-α and IL-1β, without affecting IL-6, CCL2, or the anti-inflammatory cytokine TGF-β. These findings show, that DGAT1-dependent LD formation acts as an important modulator of microglial inflammatory signaling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".