Enhanced Ca<sup>2+</sup> transport and muscle relaxation in skeletal muscle from sarcolipin-null mice
Bibliographic record
Abstract
Sarcolipin (SLN) inhibits sarco(endo)plasmic reticulum Ca(2+)-ATPase (SERCA) pumps. To evaluate the physiological significance of SLN in skeletal muscle, we compared muscle contractility and SERCA activity between Sln-null and wild-type mice. SLN protein expression in wild-type mice was abundant in soleus and red gastrocnemius (RG), low in extensor digitorum longus (EDL), and absent from white gastrocnemius (WG). SERCA activity rates were increased in soleus and RG, but not in EDL or WG, from Sln-null muscles, compared with wild type. No differences were seen between wild-type and Sln-null EDL muscles in force-frequency curves or maximum rates of force development (+dF/dt). Maximum relaxation rates (-dF/dt) of EDL were higher in Sln-null than wild type across a range of submaximal stimulation frequencies, but not during a twitch or peak tetanic contraction. For soleus, no differences were seen between wild type and Sln-null in peak tetanic force or +dF/dt; however, force-frequency curves showed that peak force during a twitch and 10-Hz contraction was lower in Sln-null. Changes in the soleus force-frequency curve corresponded with faster rates of force relaxation at nearly all stimulation frequencies in Sln-null compared with wild type. Repeated tetanic stimulation of soleus caused increased (-dF/dt) in wild type, but not in Sln-null. No compensatory responses were detected in analysis of other Ca(2+) regulatory proteins using Western blotting and immunohistochemistry or myosin heavy chain expression using immunofluorescence. These results show that 1) SLN regulates Ca(2+)-ATPase activity thereby regulating contractile kinetics in at least some skeletal muscles, 2) the functional significance of SLN is graded to the endogenous SLN expression level, and 3) SLN inhibitory effects on SERCA function are relieved in response to repeated contractions thus enhancing relaxation rates.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".