Alternatively Activated Macrophages Drive Browning of White Adipose Tissue in Burns
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to uncover the mediators and mechanistic events that facilitate the browning of white adipose tissue (WAT) in response to burns. BACKGROUND: In hypermetabolic patients (eg, burns, cancer), the browning of WAT has presented substantial clinical challenges related to cachexia, atherosclerosis, and poor clinical outcomes. Browning of the adipose tissue has recently been found to induce and sustain hypermetabolism. Although browning appears central in trauma-, burn-, or cancer-induced hypermetabolic catabolism, the mediators are essentially unknown. METHODS: WAT and blood samples were collected from patients admitted to the Ross Tilley Burn Centre at Sunnybrook Hospital. Wild type, CCR2 KO, and interleukin (IL)-6 KO male mice were purchased from Jax laboratories and subjected to a 30% total body surface area burn injury. WAT and serum collected were analyzed for browning markers, macrophages, and metabolic state via histology, gene expression, and mitochondrial respiration. RESULTS: In the present study, we show that burn-induced browning is associated with an increased macrophage infiltration, with a greater type 2 macrophage profile in the fat of burn patients. Similar to our clinical findings in burn patients, both an increase in macrophage recruitment and a type 2 macrophage profile were also observed in post burn mice. Genetic loss of the chemokine CCR2 responsible for macrophage migration to the adipose impairs burn-induced browning. Mechanistically, we show that macrophages recruited to burn-stressed subcutaneous WAT (sWAT) undergo alternative activation to induce tyrosine hydroxylase expression and catecholamine production mediated by IL-6, factors required for browning of sWAT. CONCLUSION: Together, our findings uncover macrophages as the key instigators and missing link in trauma-induced browning.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".