Sarcolipin Ablation Increases Ca <sup>2+</sup> Pump Efficiency in Mouse Skeletal Muscle
Bibliographic record
Abstract
Reconstitution experiments have shown that sarcolipin (SLN) uncouples ATP hydrolysis from Ca 2+ transport by SR Ca 2+ pumps and increases the amount of heat released per mol of ATP hydrolyzed by causing an increased rate of “slippage” on Ca 2+ pumps. In this study, we analyzed skeletal muscle Ca 2+ pump activity in the presence and absence of the Ca 2+ ionophore A23187 (iono) and Ca 2+ uptake without oxalate in SLN null mice (KO) to determine whether SLN uncouples ATP hydrolysis from Ca 2+ transport in vivo. In 3 muscles analyzed (soleus (Sol), EDL, gastrocnemius (G)), there were no differences in maximal Ca 2+ pump activity measured in homogenates (hom) with iono between KO and wildtype mice (WT). In Sol, but not EDL or G, there was an increased Ca 2+ pump affinity for Ca 2+ in KO as demonstrated by a leftward shift in the Ca 2+ pump activity‐pCa curves. Ca 2+ pump activity measured in the absence of iono was ~15–25% lower in all KO muscles compared with WT which is consistent with the idea that SLN increases “slippage” and reduces the extent of back‐inhibition on Ca 2+ pumps. In the only muscle analyzed (Sol), Ca 2+ uptake measured in hom without oxalate was not different between KO and WT which means Ca 2+ transport efficiency in KO was increased ~19%. These results show that at a physiological SLN:Ca 2+ pump ratio, SLN uncouples ATP hydrolysis from SR Ca 2+ uptake in skeletal muscle. Supported by NSERC (ART), HSFO (DHM, PHB, AOG) and CIHR (DHM, PHB).
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".