The Ubiquitin Ligase Nedd4-1 Participates in Denervation-Induced Skeletal Muscle Atrophy in Mice
Bibliographic record
Abstract
Skeletal muscle atrophy is a consequence of muscle inactivity resulting from denervation, unloading and immobility. It accompanies many chronic disease states and also occurs as a pathophysiologic consequence of normal aging. In all these conditions, ubiquitin-dependent proteolysis is a key regulator of the loss of muscle mass, and ubiquitin ligases confer specificity to this process by interacting with, and linking ubiquitin moieties to target substrates through protein:protein interaction domains. Our previous work suggested that the ubiquitin-protein ligase Nedd4-1 is a potential mediator of skeletal muscle atrophy associated with inactivity (denervation, unloading and immobility). Here we generated a novel tool, the Nedd4-1 skeletal muscle-specific knockout mouse (myo(Cre);Nedd4-1(flox/flox)) and subjected it to a well validated model of denervation induced skeletal muscle atrophy. The absence of Nedd4-1 resulted in increased weights and cross-sectional area of type II fast twitch fibres of denervated gastrocnemius muscle compared with wild type littermates controls, at seven and fourteen days following tibial nerve transection. These effects are not mediated by the Nedd4-1 substrates MTMR4, FGFR1 and Notch-1. These results demonstrate that Nedd4-1 plays an important role in mediating denervation-induced skeletal muscle atrophy in vivo.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".