Phospholamban and sarcolipin prevent thermal inactivation of sarco(endo)plasmic reticulum Ca2+-ATPases
Bibliographic record
Abstract
Na+-K+-ATPase from mice lacking the γ subunit exhibits decreased thermal stability. Phospholamban (PLN) and sarcolipin (SLN) are small homologous proteins that regulate sarco(endo)plasmic reticulum Ca2+-ATPases (SERCAs) with properties similar to the γ subunit, through physical interactions with SERCAs. Here, we tested the hypothesis that PLN and SLN may protect against thermal inactivation of SERCAs. HEK-293 cells were co-transfected with different combinations of cDNAs encoding SERCA2a, PLN, a PLN mutant (N34A) that cannot bind to SERCA2a, and SLN. One-half of the cells were heat stressed at 40°C for 1 h (HS), and one-half were maintained at 37°C (CTL) before harvesting the cells and isolating microsomes. Compared with CTL, maximal SERCA activity was reduced by 25-35% following HS in cells that expressed either SERCA2a alone or SERCA2a and mutant PLN (N34A) whereas no change in maximal SERCA2a activity was observed in cells that co-expressed SERCA2a and either PLN or SLN following HS. Increases in SERCA2a carbonyl group content and nitrotyrosine levels that were detected following HS in cells that expressed SERCA2a alone were prevented in cells co-expressing SERCA2a with PLN or SLN, whereas co-expression of SERCA2a with mutant PLN (N34A) only prevented carbonyl group formation. In other experiments using knock-out mice, we found that thermal inactivation of SERCA was increased in cardiac left ventricle samples from Pln-null mice and in diaphragm samples from Sln-null mice, compared with WT littermates. Our results show that both PLN and SLN form a protective interaction with SERCA pumps during HS, preventing nitrosylation and oxidation of SERCA and thus preserving its maximal activity.
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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 | 0.001 |
| 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 | 0.001 |
| 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".