IL-6 Signal From the Bone Marrow is Required for the Browning of White Adipose Tissue Post Burn Injury
Bibliographic record
Abstract
The hypermetabolic stress response after burn contributes to multi-organ failure, sepsis, morbidity, and mortality. The cytokine interleukin 6 (IL-6) has been hypothesized to mediate not only white adipose tissue (WAT) browning in burns, but also other hypermetabolic conditions. In addition to its inflammatory effects, IL-6 also acts as a metabolic mediator that affects metabolic tissues. Therefore, we sought to uncover the origin of circulating IL-6 post burn injury that regulates WAT browning. WAT and sera samples were collected from both adult burn patients admitted to the Ross Tilley Burn Centre at Sunnybrook Hospital and mice subjected to a burn injury. Collected tissues were analyzed for browning markers and metabolic state via histology, gene expression, and resting energy expenditure. Increased WAT browning was observed in burn patients as well as mice subjected to burn injury. Circulating IL-6 levels were significantly elevated post burn injury in mice (<0.05) and in burn patients (<0.05), the latter of which was positively correlated with elevated REE. Genetic loss of whole body IL-6 in mice prevented burn-induced WAT browning. Transplanting IL-6 knockout (KO) mice with bone marrow (BM) from wild-type (WT) mice, recovered the browning phenotype in these mice, as evaluated by increased uncoupling protein 1 (UCP1) expression (<0.05). Conversely, transplanting irradiated WT mice with BM from IL-6 KO mice impaired burn induced browning with no significant expression of UCP1. Together, our findings implicate BM derived IL-6 as the source controlling browning of WAT post burn injury. Thus, targeting IL-6 is a promising target for hypermetabolism in burns.
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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) | 0.001 | 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) | 0.002 | 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".