The proteasome inhibitor MG132 reduces immobilization-induced skeletal muscle atrophy in mice
Bibliographic record
Abstract
BACKGROUND: Skeletal muscle atrophy is a serious concern for the rehabilitation of patients afflicted by prolonged limb restriction. This debilitating condition is associated with a marked activation of NFκB activity. The ubiquitin-proteasome pathway degrades the NFκB inhibitor IκBα, enabling NFκB to translocate to the nucleus and bind to the target genes that promote muscle atrophy. Although several studies showed that proteasome inhibitors are efficient to reduce atrophy, no studies have demonstrated the ability of these inhibitors to preserve muscle function under catabolic condition. METHODS: We recently developed a new hindlimb immobilization procedure that induces significant skeletal muscle atrophy and used it to show that an inflammatory process characterized by the up-regulation of TNFα, a known activator of the canonical NFκB pathway, is associated with the atrophy. Here, we used this model to investigate the effect of in vivo proteasome inhibition on the muscle integrity by histological approach. TNFα, IL-1, IL-6, MuRF-1 and Atrogin/MAFbx mRNA level were determined by qPCR. Also, a functional measurement of locomotors activity was performed to determine if the treatment can shorten the rehabilitation period following immobilization. RESULTS: In the present study, we showed that the proteasome inhibitor MG132 significantly inhibited IκBα degradation thus preventing NFκB activation in vitro. MG132 preserved muscle and myofiber cross-sectional area by downregulating the muscle-specific ubiquitin ligases atrogin-1/MAFbx and MuRF-1 mRNA in vivo. This effect resulted in a diminished rehabilitation period. CONCLUSION: These finding demonstrate that proteasome inhibitors show potential for the development of pharmacological therapies to prevent muscle atrophy and thus favor muscle rehabilitation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".