Effects of sarcolipin deletion on skeletal muscle adaptive responses to functional overload and unload
Bibliographic record
Abstract
Overexpression of sarcolipin (SLN), a regulator of sarco(endo)plasmic reticulum Ca2+-ATPases (SERCAs), stimulates calcineurin signaling to enhance skeletal muscle oxidative capacity. Some studies have shown that calcineurin may also control skeletal muscle mass and remodeling in response to functional overload and unload stimuli by increasing myofiber size and the proportion of slow fibers. To examine whether SLN might mediate these adaptive responses, we performed soleus and gastrocnemius tenotomy in wild-type (WT) and Sln-null ( Sln−/−) mice and examined the overloaded plantaris and unloaded/tenotomized soleus muscles. In the WT overloaded plantaris, we observed ectopic expression of SLN, myofiber hypertrophy, increased fiber number, and a fast-to-slow fiber type shift, which were associated with increased calcineurin signaling (NFAT dephosphorylation and increased stabilin-2 protein content) and reduced SERCA activity. In the WT tenotomized soleus, we observed a 14-fold increase in SLN protein, myofiber atrophy, decreased fiber number, and a slow-to-fast fiber type shift, which were also associated with increased calcineurin signaling and reduced SERCA activity. Genetic deletion of Sln altered these physiological outcomes, with the overloaded plantaris myofibers failing to grow in size and number, and transition towards the slow fiber type, while the unloaded soleus muscles exhibited greater reductions in fiber size and number, and an accelerated slow-to-fast fiber type shift. In both the Sln−/− overloaded and unloaded muscles, these findings were associated with elevated SERCA activity and blunted calcineurin signaling. Thus, SLN plays an important role in adaptive muscle remodeling potentially through calcineurin stimulation, which could have important implications for other muscle diseases and conditions.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".