Regulation of SERCA Ca<sup>2+</sup> pump expression by cytoplasmic [Ca<sup>2+</sup>] in vascular smooth muscle cells
Bibliographic record
Abstract
Vascular smooth muscle cells (VSMC) express three isoforms of the sarcoplasmic or endoplasmic reticulum Ca2+-ATPase (SERCA) pump; SERCA2b predominates (91%), whereas SERCA2a (6%) and SERCA3 (3%) are present in much smaller amounts. Treatment with thapsigargin (Tg) or A-23187 increased the level of mRNA encoding SERCA2b four- to fivefold; SERCA3 increased about 10-fold, whereas SERCA2a was unchanged. Ca2+ chelation prevented the Tg-induced SERCA2b increase, whereas Ca2+ elevation itself increased SERCA2b expression. These responses were discordant with those of 78-kDa glucose-regulated protein/immunoglobulin-binding protein (grp78/BiP), an endoplasmic reticulum stress-response protein. SERCA2b mRNA elevation was much larger than could be accounted for by the observed increase in message stability. The induction of SERCA2b by Tg did not require protein synthesis, nor was it affected by inhibitors of calcineurin, protein kinase C, Ca2+/calmodulin-dependent protein kinase, or tyrosine protein kinases. Treatment with the nonselective protein kinase inhibitor H-7 prevented Tg-induced SERCA2b expression from occurring, whereas another nonselective inhibitor, staurosporine, was without effect. We conclude that changes in cytosolic Ca2+ control the expression of SERCA2b in VSMC via a mechanism involving a currently uncharacterized, H-7-sensitive but staurosporine-insensitive, protein kinase.
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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".