Microglial adipose triglyceride lipase regulates neuroinflammatory and behavioural responses to LPS
Bibliographic record
Abstract
Abstract Adipose triglyceride lipase (ATGL), the enzyme that catalyses the rate-limiting step of triglyceride lipolysis, regulates inflammation in peripheral tissues. ATGL has been associated with both pro- and anti-inflammatory responses in different tissues suggesting its actions are dependent on cell type. Recent studies in microglia and macrophages suggest that lipid droplets (LD), a triglyceride storing organelle, and LD lipolysis via ATGL are important components of inflammatory responses. Here, we determined the impact of ATGL inhibition and microglia-specific ATGL loss-of-function on inflammatory and behavioural responses to acute pro-inflammatory insult. First, we evaluated the impact of lipolysis inhibition on lipopolysaccharide (LPS)-induced expression and secretion of cytokines in mouse primary microglia cultures. LPS led to LD accumulation in microglia and altered the expression of lipolysis regulators. The pan-lipase inhibitor ORlistat alleviated LPS-induced expression of IL-1β and IL-6. Specific inhibition of ATGL by ATGListatin had similar anti-inflammatory action on cytokines expression and secretion in both neonatal and adult microglia cultures. Second, targeted and untargeted lipidomic studies revealed that ATGL inhibition reduced LPS-induced generation of pro-inflammatory prostanoids and affected ceramide profile. Finally, the role of ATGL in neuroinflammation was assessed in a novel mouse model with inducible ATGL deletion specifically in microglia. Loss of microglial ATGL in adult male mice dampened LPS-induced expression of IL-6 and reduced LPS-induced sickness behaviour. Together, our results demonstrate that pharmacological inhibition or loss of ATGL-mediated triglyceride lipolysis reduces LPS-induced inflammation to suggest that inhibition of lipolysis plays a beneficial role in neuroinflammation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".