SVF mediates immunometabolic alterations in burn-induced hypermetabolic adipose tissue via mitochondrial transfer
Bibliographic record
Abstract
ABSTRACT: Adipose tissue (AT) browning promotes systemic alterations in energy expenditure as a response to catecholamine-induced hypermetabolism in severe burn trauma. The AT is composed of the stromal vascular fraction (SVF) and adipocytes. SVF contains a vast population of immune cells that maintain AT homeostasis. Despite evidence that local immune cell accumulation contributes to hypermetabolism, the underlying mechanism of persistent browning response is not known. Thus, we hypothesized that a specific cellular communication between adipocytes and SVF can mediate the severe metabolic alterations associated with hypermetabolism. Therefore, we used a murine burn model to show that postburn hypermetabolism compromises mitochondria respiration and alters the immune cell profile of the AT-SVF. We found that adipocyte-derived signals promote metabolic reprogramming and inflammatory responses by SVF after burns in both mice and humans. Interestingly, adipocytes transfer mitochondria to cells in the SVF including different immune cell (macrophages, T cells, B cells) uptake mitochondria from adipocytes. Such data were replicated in human samples as well. These results indicate that adipocytes play a major role in immunometabolic reprogramming following severe burns through crosstalk with the adipose immune cell population. Therefore, targeting immune cell metabolism restoration is a potential strategy to mitigate the detrimental effects of postburn hypermetabolism on systemic energy balance.
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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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".