ATP Modulates Interaction of Syntaxin-1A with Sulfonylurea Receptor 1 to Regulate Pancreatic β-Cell KATP Channels
Bibliographic record
Abstract
ATP-sensitive potassium (K(ATP)) channels are regulated by a variety of cytosolic factors (adenine nucleotides, Mg(2+), phospholipids, and pH). We previously reported that K(ATP) channels are also regulated by endogenous membrane-bound SNARE protein syntaxin-1A (Syn-1A), which binds both nucleotide-binding folds of sulfonylurea receptor (SUR)1 and 2A, causing inhibition of K(ATP) channel activity in pancreatic islet β-cells and cardiac myocytes, respectively. In this study, we show that ATP dose-dependently inhibits Syn-1A binding to SUR1 at physiological concentrations, with the addition of Mg(2+) causing a decrease in the ATP-induced inhibitory effect. This ATP disruption of Syn-1A binding to SUR1 was confirmed by FRET analysis in living HEK293 cells. Electrophysiological studies in pancreatic β-cells demonstrated that reduced ATP concentrations increased K(ATP) channel sensitivity to Syn-1A inhibition. Depletion of endogenous Syn-1A in insulinoma cells by botulinum neurotoxin C1 proteolysis followed by rescue with exogenous Syn-1A showed that Syn-1A modulates K(ATP) channel sensitivity to ATP. Thus, our data indicate that although both ATP and Syn-1A independently inhibit β-cell K(ATP) channel gating, they could also influence the sensitivity of K(ATP) channels to each other. These findings provide new insight into an alternate mechanism by which ATP regulates pancreatic β-cell K(ATP) channel activity, not only by its direct actions on Kir6.2 pore subunit, but also via ATP modulation of Syn-1A binding to SUR1.
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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".