Cardiac-specific overexpression of sarcolipin inhibits sarco(endo)plasmic reticulum Ca <sup>2+</sup> ATPase (SERCA2a) activity and impairs cardiac function in mice
Bibliographic record
Abstract
Sarcolipin (SLN) inhibits the cardiac sarco(endo)plasmic reticulum Ca(2+) ATPase (SERCA2a) by direct binding and is superinhibitory if it binds through phospholamban (PLN). To determine whether overexpression of SLN in the heart might impair cardiac function, transgenic (TG) mice were generated with cardiac-specific overexpression of NF-SLN (SLN tagged at its N terminus with the FLAG epitope). The level of NF-SLN expression (the NF-SLN/PLN expression ratio) was equivalent to that which induces profound superinhibition when coexpressed with PLN and SERCA2a in HEK-293 cells. In TG hearts, the apparent affinity of SERCA2a for Ca(2+) was decreased compared with non-TG littermate control hearts. Invasive hemodynamic and echocardiographic analyses revealed impaired cardiac contractility and ventricular hypertrophy in TG mice. Basal PLN phosphorylation was reduced. In isolated papillary muscle subjected to isometric tension, peak amplitudes of Ca(2+) transients and peak tensions were reduced, whereas decay times of Ca(2+) transients and relaxation times of tension were increased in TG mice. Isoproterenol largely restored contractility in papillary muscle and stimulated PLN phosphorylation to wild-type levels in intact hearts. No compensatory changes in expression of SERCA2a, PLN, ryanodine receptor, and calsequestrin were observed in TG hearts. Coimmunoprecipitation indicated that overexpressed NF-SLN was bound to both SERCA2a and PLN, forming a ternary complex. These data suggest that NF-SLN overexpression inhibits SERCA2a through stabilization of SERCA2a-PLN interaction in the absence of PLN phosphorylation and through the inhibition of PLN phosphorylation. Inhibition of SERCA2a impairs contractility and calcium cycling, but responsiveness to beta-adrenergic agonists may prevent progression to heart failure.
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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) | 0.001 | 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".