Cardiac-specific overexpression of sarcolipin in phospholamban null mice impairs myocyte function that is restored by phosphorylation
Bibliographic record
Abstract
Sarcolipin (SLN) inhibits the cardiac sarco(endo)plasmic reticulum Ca2+ ATPase (SERCA2a) by direct binding and is superinhibitory if it binds as a binary complex with phospholamban (PLN). To demonstrate whether overexpression of SLN in the heart might impair cardiac function directly, transgenic (TG) mice with cardiac-specific overexpression of NF-SLN (SLN tagged at its N terminus with the FLAG epitope) were generated on a phospholamban (PLN) null (PLN KO) background. In NF-SLN TG/PLN KO cardiac microsomes, the apparent affinity of SERCA2a for Ca2+ was decreased compared with non-TG littermate PLN KO hearts. Analyses of isolated NF-SLN/PLN KO cardiomyocytes revealed impaired cardiac contractility, reduced calcium transient peak amplitude, and slower decay kinetics compared to PLN KO animals. In these cardiomyocytes, isoproterenol restored calcium dynamics to the levels seen in PLN KO. Invasive hemodynamic and echocardiographic analyses of NF-SLN/PLN KO mouse cardiac muscle in vivo showed no direct effects of NF-SLN overexpression when compared to PLN KO mice. A possible mechanism for the lack of effects in the whole heart may be a responsiveness to phosphorylation because we determined that NF-SLN can be phosphorylated in cardiomyocytes in response to isoproterenol, and we provide evidence that serine/threonine kinase 16 is a kinase that can phosphorylate NF-SLN. Site-directed mutagenesis showed that SLN Thr-5 is the target site for this kinase. These data show that overexpression of NF-SLN can inhibit SERCA2a in the absence of PLN and that the inhibition of SERCA2a is correlated with impairment of contractility and calcium cycling in cardiomyocytes.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".