Pulmonary inflammation-induced loss and subsequent recovery of skeletal muscle mass require functional poly-ubiquitin conjugation
Bibliographic record
Abstract
BACKGROUND: Pulmonary inflammation in response to respiratory infections can evoke muscle wasting. Increased activity of the ubiquitin (Ub)-proteasome system (UPS) and the autophagy lysosome pathway (ALP) have been implicated in inflammation-induced muscle atrophy. Since poly-Ub conjugation is required for UPS-mediated proteolysis and has been implicated in the ALP, we assessed the effect of impaired ubiquitin conjugation on muscle atrophy and recovery following pulmonary inflammation, and compared activation and suppression of these proteolytic systems to protein synthesis regulation. METHODS: Pulmonary inflammation was induced in mice by an intratracheal instillation of LPS. Proteolysis (UPS and ALP) and synthesis signaling were examined in gastrocnemius muscle homogenates. Ub-conjugation-dependency of muscle atrophy and recovery was addressed using Ub-K48R (K48R) mice with attenuated poly-ubiquitin conjugation, and compared to UBWT control mice. RESULTS: Pulmonary inflammation caused a decrease in skeletal muscle mass which was accompanied by a rapid increase in expression of UPS and ALP constituents and reduction in protein synthesis signaling acutely after LPS. Muscle atrophy was attenuated in K48R mice, while ALP and protein synthesis signaling were not affected. Muscle mass recovery starting 72 h post LPS, correlated with reduced expression of UPS and ALP constituents and restoration of protein synthesis signaling. K48R mice however displayed impaired recovery of muscle mass. CONCLUSION: Pulmonary inflammation-induced muscle atrophy is in part attributable to UPS-mediated proteolysis, as activation of ALP- and suppression of protein synthesis signaling occur independently of poly-Ub conjugation during muscle atrophy. Recovery of muscle mass following pulmonary inflammation involves inverse regulation of proteolysis and protein synthesis signaling, and requires a functional poly-Ub conjugation.
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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) | 0.001 | 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 | 0.001 |
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".