Sarcolipin retention in the endoplasmic reticulum depends on its C-terminal RSYQY sequence and its interaction with sarco(endo)plasmic Ca <sup>2</sup> +-ATPases
Bibliographic record
Abstract
Sarcolipin (SLN) and phospholamban (PLN) are effective inhibitors of the sarco(endo)plasmic reticulum Ca(2+)-ATPase (SERCA). These homologous proteins differ at their N and C termini: the C-terminal Met-Leu-Leu in PLN is replaced by Arg-Ser-Tyr-Gln-Tyr in SLN. The role of the C-terminal sequence of SLN tagged N-terminally with the FLAG epitope (NF-SLN) in endoplasmic reticulum (ER) retention was investigated by transfecting human embryonic kidney-293 cells with cDNAs encoding NF-SLN or a series of NF-SLN mutants in which C-terminal amino acids were deleted progressively. Immunofluorescence and immunoblotting of transfected cells by using anti-FLAG antibodies indicated that NF-SLN and PLN tagged at its N terminus with the FLAG epitope, even when overexpressed, were restricted to the ER. However, C-terminal truncation deletions of SLN, which lacked RSYQY, were not localized to ER and did not inhibit Ca(2+)-dependent Ca2+ uptake by SERCA. The shortest deletion constructs, NF-SLN 1-22 and NF-SLN 1-23, did not express stable protein products. However, all NF-SLN cDNA constructs, including NF-SLN 1-22 and NF-SLN 1-23, were expressed stably and localized to the ER when they were coexpressed with SERCA2a. These results show that NF-SLN subcellular distribution depends on SERCA coexpression and on its luminal, C-terminal RSYQY sequence. By using immunoprecipitation and MS, glucose-regulated protein 78/BiP and glucose-regulated protein 94 were identified as proteins that interact with NF-SLN through the RSYQY sequence. Thus, in the absence of SERCA, retention of NF-SLN in the ER is mediated through its association with other components through the C-terminal RSYQY sequence.
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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 | 0.001 |
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".