Role of autophagy in sepsis-induced skeletal muscle dysfunction, whole-body metabolism, and survival
Bibliographic record
Abstract
Abstract Septic patients frequently develop skeletal muscle wasting and weakness, resulting in severe clinical consequences and adverse outcomes. Autophagy is a stress-induced degradative process essential to cell survival. Recent studies have demonstrated that sepsis triggers sustained induction of autophagy in skeletal muscles, although the impact of this enhanced autophagy on sepsis-induced muscle dysfunction remains unclear. Atg7 is an autophagy gene that plays a major role in autophagosome formation. Using an inducible and muscle-specific Atg7 knockout mouse model (Atg7 iSkM-KO ), we investigated the functional importance of skeletal muscle autophagy in sepsis. Sepsis was induced using cecal ligation and perforation (CLP) with a sham operation serving as a control. Atg7 iSkM-KO mice exhibited a more severe phenotype in response to sepsis, marked by severe muscle wasting and contractile dysfunction, hypoglycemia, higher ketone levels and a decreased in survival as compared to mice with intact Atg7 . Several genes that encode 26S proteasome subunits were upregulated, suggesting that activation of the ubiquitin-proteasome system is responsible for the severe muscle atrophy that was seen in these mice. Sepsis and Atg7 deletion resulted in the accumulation of mitochondrial dysfunction, although sepsis did not further worsen mitochondrial dysfunction in Atg7 iSkM-KO mice. Overall, our study demonstrates that autophagy inactivation in skeletal muscles triggers significant worsening of sepsis-induced contractile and metabolic dysfunctions and negatively impacts survival. Induction of autophagy in skeletal muscles in response to sepsis thus represents a protective mechanism.
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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 | 0.000 |
| 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".