Effects of peroxynitrite on sarco/endoplasmic reticulum Ca<sup>2+</sup> pump isoforms SERCA2b and SERCA3a
Bibliographic record
Abstract
Sarco(endo)plasmic reticulum Ca2+ (SERCA) pumps are important for cell signaling. Three different genes, SERCA1, 2, and 3, encode these pumps. Most tissues, including vascular smooth muscle, express a splice variant of SERCA2 (SERCA2b), whereas SERCA3a is widely distributed in tissues such as vascular endothelium, tracheal epithelium, mast cells, and lymphoid cells. SERCA2b protein is readily inactivated by peroxynitrite that may be formed during cardiac ischemia reperfusion or during immune response after infection. Here, we compared the peroxynitrite sensitivity of SERCA2b and SERCA3a by using microsomes prepared from HEK-293T cells overexpressing the pumps. We incubated the microsomes with different concentrations of peroxynitrite and determined Ca2+ uptake, Ca2+-Mg2+-ATPase, Ca2+-dependent formation of acylphosphate intermediate, and protein mobility in Western blots. Ca2+ uptake, Ca2+-Mg2+-ATPase, and Ca2+-dependent formation of acylphosphate intermediate were inactivated for both SERCA2b and SERCA3a, but the latter was more resistant to the inactivation. Western blots showed that SERCA2b and SERCA3a proteins oligomerized after treatment with peroxynitrite, but each with a slightly different pattern. Compared with monomers, the oligomers may be less efficient in forming the acylphosphate intermediate and in conducting the remainder of the steps in the reaction cycle. We conclude that the resistance of SERCA3a to peroxynitrite may aid the cells expressing them in functioning during exposure to oxidative stress.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".