NF-κB activation and polyubiquitin conjugation are required for pulmonary inflammation-induced diaphragm atrophy
Bibliographic record
Abstract
Loss of diaphragm muscle strength in inflammatory lung disease contributes to mortality and is associated with diaphragm fiber atrophy. Ubiquitin (Ub) 26S-proteasome system (UPS)-dependent protein breakdown, which mediates muscle atrophy in a number of physiological and pathological conditions, is elevated in diaphragm muscle of patients with chronic obstructive pulmonary disease. Nuclear factor kappa B (NF-κB), an essential regulator of many inflammatory processes, has been implicated in the regulation of poly-Ub conjugation of muscle proteins targeted for proteolysis by the UPS. Here, we test if NF-κB activation in diaphragm muscle and subsequent protein degradation by the UPS are required for pulmonary inflammation-induced diaphragm atrophy. Acute pulmonary inflammation was induced in mice by intratracheal lipopolysaccharide instillation. Fiber cross-sectional area, ex vivo tyrosine release, protein poly-Ub conjugation, and inflammatory signaling were determined in diaphragm muscle. The contribution of NF-κB or the UPS to diaphragm atrophy was assessed in mice with intact or genetically repressed NF-κB signaling or attenuated poly-Ub conjugation, respectively. Acute pulmonary inflammation resulted in diaphragm atrophy measured by reduced muscle fiber cross-sectional area. This was accompanied by diaphragm NF-κB activation, and proteolysis, measured by tyrosine release from the diaphragm. Poly-Ub conjugation was increased in diaphragm, as was the expression of muscle-specific E3 Ub ligases. Genetic suppression of poly-Ub conjugation prevented inflammation-induced diaphragm muscle atrophy, as did muscle-specific inhibition of NF-κB signaling. In conclusion, the present study is the first to demonstrate that diaphragm muscle atrophy, resulting from acute pulmonary inflammation, requires NF-κB activation and UPS-mediated protein degradation.
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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 | 0.001 |
| 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".