BURNS INDUCE ALTERATIONS IN THE ACYL PROTEOME OF MICE AND HUMANS
Bibliographic record
Abstract
ABSTRACT: Hypermetabolic reprogramming triggered by thermal injury causes substantial morbidity and mortality. Despite the therapeutic potential of targeting this response, the underlying mechanisms remain poorly understood. Interestingly, protein S-acylation is a reversible posttranslational modification induced by metabolic alterations via DHHC acyltransferases. While this modification aids in the regulation of cellular functions, deregulated S-acylation contributes to various diseases by altering protein structure, stability, and localization. However, whether and how S-acylation may impact morbidity and mortality during postburn hypermetabolism is unknown. In this study, we discovered that alterations in the acyl proteome play a key role in mediating adverse outcomes that occur after burn injury. Using a murine model, we show that burn injury induces profound changes in the expression of various DHHC isoforms in metabolic organs central to regulating postburn hypermetabolism, the adipose tissue, and liver. This was accompanied by increased levels of S-acylated proteins in several pathways involved in mediating the adverse hypermetabolic response, including ER stress, lipolysis, and browning. In fact, similar results were also observed in adipose tissue from severely burned patients, as reflected by increased S-acylation of ERK1/2, eIF2a, ATGL, FGF21, and UCP1 relative to nonburn controls. Importantly, pharmacologically targeting this posttranslational modification using a nonselective DHHC inhibitor effectively attenuated burn-induced ER stress, lipolysis, and browning induction in an ex vivo explant model. Together, these findings suggest that S-acylation may facilitate the protein activation profile that drives burn-induced hypermetabolism and that targeting it could potentially be an effective strategy to restore metabolic function and improve outcomes after injury.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".