Sarcolipin Overexpression in Rat Slow Twitch Muscle Inhibits Sarcoplasmic Reticulum Ca2+ Uptake and Impairs Contractile Function
Bibliographic record
Abstract
Sarcolipin (SLN) is an inhibitor of sarco(endo)plasmic reticulum Ca(2+)-ATPases (SERCAs) in vitro, but its function in vivo has not been defined. NF-SLN cDNA (SLN tagged N-terminally with a FLAG epitope) was introduced into rat soleus muscle in one hindlimb by plasmid injection and electrotransfer. Western blotting showed expression and co-immunoprecipitation showed physical interaction between NF-SLN and SERCA2a. Contractile properties and SERCA2a function were assessed and compared with vector-injected contralateral soleus muscles. NF-SLN reduced both peak twitch force (P(t)) (123.9 +/- 12.5 versus 69.8 +/- 8.9 millinewtons) and tetanic force (P(o)) (562.3 +/- 51.0 versus 300.7 +/- 56.9 millinewtons) and reduced both twitch and tetanic rates of contraction (+dF/dt) and relaxation (-dF/dt) significantly. Repetitive stimulation (750-ms trains at 50 Hz once every 2 s for 3 min) showed that NF-SLN increased susceptibility to fatigue. These changes in contractile function were observed in the absence of endogenous phospholamban, and NF-SLN had no effect on either SERCA2a or SERCA1a expression levels. NF-SLN also decreased maximal Ca(2+) transport activity at pCa 5 by 31% with no significant change in apparent Ca(2+) affinity (6.36 +/- 0.07 versus 6.39 +/- 0.08 pCa units). These results show that NF-SLN expression impairs muscle contractile function by inhibiting SERCA function and diminishing sarcoplasmic reticulum Ca(2+) stores.
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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".