Nerve Activity-dependent Modulation of Calcineurin Signaling in Adult Fast and Slow Skeletal Muscle Fibers
Bibliographic record
Abstract
This study tested the hypothesis that calcineurin signaling is modulated in skeletal muscle cells by fluctuations in nerve-mediated activity. We show that dephosphorylation of NFATc1, MEF2A, and MEF2D transcription factors by calcineurin in all muscle types is dependent on nerve activity and positively correlated with muscle usage under normal weightbearing conditions. With increased nerve-mediated activity, calcineurin dephosphorylation of these targets was found to be potentiated in a way that paralleled the higher muscle activation profiles associated with functional overload or nerve electrical stimulation conditions. We also establish that muscle activity must be sustained above native levels for calcineurin-dependent dephosphorylation of MEF2A and MEF2D to be transduced into an increase in MEF2 transcriptional function, suggesting that calcineurin cooperates with other activity-linked events to signal via these proteins. Finally, examination of individual fiber responses to overload and nerve electrical stimulation revealed that calcineurin-MEF2 signaling occurs in all fiber types but most readily in fibers that are normally least active (i.e. those expressing IIx and IIb myosin heavy chain (MHC)), suggesting that signaling via this phosphatase is also dependent upon the activation history of the muscle cell.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".