The Regulation of SERCA‐Type Pumps by Phospholamban and Sarcolipin
Bibliographic record
Abstract
Both sarcolipin (SLN) and phospholamban (PLN) lower the apparent affinity of either SERCA1a or SERCA2a for Ca(2+). Since SLN and PLN are coexpressed in the heart, interactions among these three proteins were investigated. When SERCA1a or SERCA2a were coexpressed in HEK-293 cells with both SLN and PLN, superinhibition resulted. The ability of SLN to elevate the content of PLN monomers accounts, at least in part, for the superinhibitory effects of SLN in the presence of PLN. To evaluate the role of SLN in skeletal muscle, SLN cDNA was injected directly into rat soleus muscle and force characteristics were analyzed. Overexpression of SLN resulted in significant reductions in both twitch and tetanic peak force amplitude and maximal rates of contraction and relaxation and increased fatigability with repeated electrical stimulation. Ca(2+) uptake in muscle homogenates was impaired, suggesting that overexpression of SLN may reduce the sarcoplasmic reticulum Ca(2+) store. SLN and PLN appear to bind to the same regulatory site in SERCA. However, in a ternary complex, PLN occupies the regulatory site and SLN binds to the exposed side of PLN and to SERCA.
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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".