Syntaxin-1A Inhibits Cardiac KATP Channels by Its Actions on Nucleotide Binding Folds 1 and 2 of Sulfonylurea Receptor 2A
Bibliographic record
Abstract
ATP-sensitive potassium (KATP) channels couple the metabolic status of the cell to its membrane potential to regulate a number of cell actions, including secretion (neurons and neuroendocrine cells) and muscle contractility (skeletal, cardiac, and vascular smooth muscle). KATP channels consist of regulatory sulfonylurea receptors (SUR) and pore-forming (Kir6.X) subunits. We recently reported (Pasyk, E. A., Kang, Y., Huang, X., Cui, N., Sheu, L., and Gaisano, H. Y. (2004) J. Biol. Chem. 279, 4234-4240) that syntaxin-1A (Syn-1A), known to mediate exocytotic fusion, was capable of binding the nucleotide binding folds (NBF1 and C-terminal NBF2) of SUR1 to inhibit the KATP channels in insulin-secreting pancreatic islet beta cells. This prompted us to examine whether Syn-1A might modulate cardiac SUR2A/KATP channels. Here, we show that Syn-1A is present in the plasma membrane of rat cardiac myocytes and binds the SUR2A protein (of rat brain, heart, and human embryonic kidney 293 cells expressing SUR2A/Kir6. 2) at its NBF1 and NBF2 domains to decrease KATP channel activation. Unlike islet beta cells, in which Syn-1A inhibition of the channel activity was apparently mediated only via NBF1 and not NBF2 of SUR1, both exogenous recombinant NBF1 and NBF2 of SUR2A were found to abolish the inhibitory actions of Syn-1A on K(ATP) channels in rat cardiac myocytes and HEK293 cells expressing SUR2A/Kir6.2. Together with our recent report, this study suggests that Syn-1A binds both NBFs of SUR1 and SUR2A but appears to exhibit distinct interactions with NBF2 of these SUR proteins in modulating the KATP channels in islet beta cells and cardiac myocytes.
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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 | 0.001 |
| 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".