NLRP3 inflammasome mediates white adipose tissue browning after burn
Bibliographic record
Abstract
A hallmark after burn is the stress and inflammatory-induced hypermetabolic response. Recently, we and others found that browning of white adipose tissue (WAT) is a critical component of this complex detrimental response. Although browning and inflammation have been independently delineated to occur after injury, their interaction is currently not well defined. One of the master regulators of inflammation and adipose tissue remodeling after burns is nucleotide-binding and oligomerization domain, leucine rich repeat and pyrin domain containing 3 (NLRP3) inflammasome. The aim of this this study was to determine whether NLRP3 modulates and activates WAT browning after burn. To obtain molecular and mechanistic insights, we used an NLRP3 knockout (NLRP3−/−) murine burn model. We demonstrated that genetic deletion of NLRP3 promoted persistent and augmented browning in adipocytes, evidenced by increased gene expression of peroxisome proliferator-activated receptor γ and CIDEA at 3 days (5.74 vs. 0.29, P < 0.05; 26.0 vs. 0.71, P < 0.05) and uncoupling protein 1 ( UCP1) and PGC1α at 7 days (7,406 vs. 3,894, P < 0.05; 20.6 vs. 2.52, P < 0.01) and enhanced UCP1 staining and multilocularity. Additionally, the main regulator of postburn WAT browning, IL-6, was elevated in the plasma acutely after burn in NLRP3−/− compared with wild-type counterparts (478.9 vs. 67.1 pg/mL, P < 0.05 at 3 days). These results suggest that NLRP3 has antibrowning effects and that blocking NLRP3 increases thermogenesis and augments browning via increased levels of IL-6. Our findings provide insights into targeting innate inflammatory systems for regulation of adaptive thermogenesis, a critical response after burns and other hypermetabolic conditions.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".