Differential regulation of protein degradation pathways in the ventilatory and limb muscles in response to sepsis
Bibliographic record
Abstract
Background & Aims: Sepsis triggers skeletal muscle weakness and atrophy. Muscle atrophy has been attributed to increased proteolysis. In this study, we evaluated the differential regulation of the autophagy and proteasome proteolytic pathway in the ventilatory and limb muscles of septic mice. Methods: Adult male C57/Bl6 mice underwent cecal ligation and perforation (CLP group) or sham procedures (Sham group). The diaphragm (DIA) and tibialis anterior (TA) muscles were examined after 24, 48 and 96h of the surgical procedure. Fiber diameter, autophagy and proteasome pathways were evaluated using immunostaining, immunoblotting, qPCR and electron microscopy. Results: Sepsis triggered transient atrophy (24h) in the DIA and sustained (24, 48 and 96h) atrophy in the TA and significantly induced autophagy in the two muscles with peak values detected after 24 and 48h of sepsis in the DIA and TA, respectively. Upregulation of ULK1, PI3KC3, BNIP3, PARKIN and SQSTM1 proteins were also observed in muscles of septic animals. Biphasic (24 and 96h) upregulation of Atrogin-1, MuRF-1 and Musa1 mRNA was observed in muscles of septic animals. Sepsis also decreased AKT and mTOR activation and upregulated AMPK phosphorylation. Conclusions: We conclude that sepsis triggers transient atrophy in the DIA and more sustained atrophy in the TA muscle and that this atrophy is mediated by enhanced autophagy and proteasome proteolytic activities. We also conclude that sepsis-induced activation of the autophagy pathway is mediated through inhibition of the AKT and mTOR pathways and activation of the AMPK pathway.
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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".