Sarcolipin Inhibits Polymerization of Phospholamban to Induce Superinhibition of Sarco(endo)plasmic Reticulum Ca2+-ATPases (SERCAs)
Bibliographic record
Abstract
Sarcolipin (SLN), a regulator of the sarco(endo)plasmic reticulum Ca(2+)-ATPase of fast-twitch skeletal muscle (SERCA1a), is also expressed in cardiac and slow-twitch skeletal muscles where phospholamban (PLN) and SERCA2a are expressed. Co-expression in HEK-293 cells of SLN tagged N-terminally with a FLAG epitope (NF-SLN), PLN, and SERCAs followed by measurement of the Ca(2+) dependence of Ca(2+) transport activity in isolated microsomal fractions showed that NF-SLN can reduce the apparent Ca(2+) affinity of both SERCA1a (DeltaK(Ca) = -0.22 +/- 0.01 pCa units) and SERCA2a (DeltaK(Ca) = -0.37 +/- 0.04 pCa units). When SERCA1a or SERCA2a were co-expressed with both NF-SLN and PLN, inhibition was synergistic, reducing DeltaK(Ca) by about -1.0 pCa units. Co-immunoprecipitation showed that NF-SLN increased the binding of PLN to SERCA, whereas PLN did not increase the binding of NF-SLN to SERCA. Elevated Ca(2+) dissociates both PLN and NF-SLN from their complexes with both SERCA1a and SERCA2a, but NF-SLN induced resistance to Ca(2+) dissociation of the PLN.SERCA complex. Co-immunoprecipitation of PLN and NF-SLN without SERCA showed that NF-SLN binds directly to PLN and that NF-SLN inhibits the formation of PLN pentamers. Thus the ability of NF-SLN to elevate the content of PLN monomers can account, at least in part, for the superinhibitory effects of NF-SLN in the presence of PLN.
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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 | 0.001 |
| 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".