Burn Injury Triggers Distinct Transcriptomic Profiles in Adipose Tissue of Adult and Aged Mice
Bibliographic record
Abstract
Severe burns are a major global health concern, and are associated with long-term physical and psychological impairments, multi-organ dysfunction, and substantial morbidity and mortality. While burn injuries in adults trigger systemic immuno-metabolic alterations-characterized by white adipose tissue browning, elevated resting energy expenditure, widespread catabolism, and inflammation-these adaptive responses are considerably impaired in older adults, with molecular mechanisms behind these differences remaining largely unclear. As a key regulator of systemic metabolism, investigating the pathological role of adipose tissue (AT) postburn may reveal novel targets that could potentially improve patient outcomes. In this study, we conducted bulk mRNA sequencing and analysis of AT from adult and aged mice to elucidate the transcriptomic changes underlying the distinct postburn responses in these populations. After examining differentially expressed genes in the adult and aged burn mice, the top six upregulated genes in adults (Ucp1, Lgr6, Dio2, Lncbate10, Fabp3, Kng2) were primarily associated with thermogenesis, whereas those in the aged mice (Car6, Spata25, Gm128, Btbd16, Lipm, Abca13) were linked to inflammation, tissue repair, and lipid metabolism. Furthermore, our gene co-expression and enrichment map analysis identified burn-associated modules related to fatty acid oxidation, acetyl thioester CoA, and thermogenesis in adults, whereas leukocyte migration, tumor necrosis factor production, and sister chromatids were in aged mice. Notably, Ppara and Sfpi1 emerged as potential master regulators of co-expressed genes in burn AT of adult and aged mice, respectively. Our findings highlight age-specific differences in burn-induced AT responses and uncover potential molecular regulators that may inform targeted therapeutic strategies to mitigate the post-burn stress response.
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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 | 0.001 | 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) | 0.001 | 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".