Sarcoplasmic reticulum Ca<sup>2+</sup> pump mRNA stability in cardiac and smooth muscle: role of the 3′-untranslated region
Bibliographic record
Abstract
Stomach smooth muscle (SSM) and left ventricular muscle (LVM) express the sarco(endo)plasmic reticulum Ca(2+)-ATPase (SERCA) pump gene SERCA2. Alternative splicing yields two major isoforms, SERCA2a in LVM and slow twitch muscle and SERCA2b in SSM and most other tissues. The splices have different 3'-untranslated regions (UTR) and also encode proteins that differ slightly in their COOH-terminal domains. SERCA2 transcription rates are similar in the two tissues, yet LVM has a much higher level of SERCA2 mRNA than SSM. To understand the control of SERCA2 RNA expression, we inhibited transcription and showed that the half-life of SERCA2 mRNA is significantly longer (P < 0.05) in primary cultures of LVM cells than in SSM cells. Nuclear SERCA2 mRNA levels were also higher in LVM than in SSM. In vitro decay assays using synthetic RNA corresponding to the 3'-UTR of SERCA2a and -2b showed that nuclear extracts produced a faster decay of SERCA2 RNA than cytoplasmic extracts and that nuclear extracts produced a faster decay of SERCA2b than -2a. This was also true when the full-length native mRNA was used instead of the 3'-UTR RNA, and SERCA2b decay by cytoplasmic extracts was faster for LVM than for SSM. We propose that nuclear decay is an initial step in the control of SERCA2 RNA abundance and that this control is maintained or modulated in the cytoplasm. We discuss how these control mechanisms may be part of a control switch in cardiac development and pathophysiology.
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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".