Effects of peroxynitrite on sarcoplasmic reticulum Ca<sup>2+</sup> pump in pig coronary artery smooth muscle
Bibliographic record
Abstract
Peroxynitrite generated in arteries from superoxide and NO may damage Ca(2+) pumps. Here, we report the effects of peroxynitrite on ATP-dependent azide-insensitive uptake of Ca(2+) into pig coronary artery vesicular membrane fractions F2 [enriched in plasma membrane (PM)] and F3 [enriched in sarcoplasmic reticulum (SR)]. Membranes were pretreated with peroxynitrite and then with DTT to quench this agent. This pretreatment inhibited Ca(2+) uptake in a peroxynitrite concentration-dependent manner, but the effect was more severe in F3 than in F2. The inhibition was thus not overcome by excess DTT used to quench peroxynitrite and was not affected if catalase, SOD, or mannitol was added along with peroxynitrite. Such damage to the pump protein would be difficult to repair if produced during ischemia-reperfusion. The acylphosphates formed with ATP in F3 corresponded mainly to the SR Ca(2+) pump (110 kDa), but in F2 both PM (140 kDa) and 110-kDa bands were observed. Peroxynitrite treatment of F2 inhibited only the 110-kDa band. Inhibition of Ca(2+) uptake and acylphosphate formation from ATP correlated well in peroxynitrite-treated F3 samples. However, inhibition of acylphosphates from orthophosphate (reverse reaction of the pump) was slightly poorer. Peroxynitrite treatment also covalently cross-linked the pump protein, yielding no dimers but only larger oligomers. In contrast, cross-linking of the SR Ca(2+) pump in skeletal and cardiac muscles gives dimers as the first oligomers. Therefore, we speculate that SERCA2 has a different quaternary structure in the coronary artery smooth muscle.
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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 | 0.001 | 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".