Syntaxin-1A Interacts with Distinct Domains within Nucleotide-binding Folds of Sulfonylurea Receptor 1 to Inhibit β-Cell ATP-sensitive Potassium Channels
Bibliographic record
Abstract
The ATP-sensitive potassium (K(ATP)) channel regulates pancreatic β-cell function by linking metabolic status to electrical activity. Syntaxin-1A (Syn-1A), a SNARE protein mediating exocytotic fusion, binds and inhibits the K(ATP) channel via the nucleotide-binding folds (NBFs) of its sulfonylurea receptor-1 (SUR1) regulatory subunit. In this study, we elucidated the precise regions within the NBFs required for Syn-1A-mediated K(ATP) inhibition, using in vitro binding assays, whole cell patch clamp and FRET assay. Specifically, NBF1 and NBF2 were each divided into three subregions, Walker A (W(A)), signature sequence linker, and Walker B (W(B)), to make GST fusion proteins. In vitro binding assays revealed that Syn-1A associates with W(A) and W(B) regions of both NBFs. Patch clamp recordings on INS-1 and primary rat β-cells showed that Syn-1A-mediated channel inhibition was reversed by co-addition of NBF1-W(B) (not NBF1-W(A)), NBF2-W(A), and NBF2-W(B). The findings were corroborated by FRET studies showing that these truncates disrupted Syn-1A interactions with full-length SUR1. To further identify the binding sites, series single-site mutations were made in the Walker motifs of the NBFs. Only NBF1-W(A) (K719M) or NBF2-W(A) (K1385M) mutant no longer bound to Syn-1A; K1385M failed to disrupt Syn-1A-mediated inhibition of K(ATP) channels. These data suggest that NBF1-W(A) (Lys-719) and NBF2-W(A) (Lys-1385) are critical for Syn-1A-K(ATP) channel interaction. Taken together, Syn-1A intimately and functionally associates with the SUR1-NBF1/2 dimer via direct interactions with W(A) motifs and sites adjacent to W(B) motifs of NBF1 and NBF2 but transduces its inhibitory actions on K(ATP) channel activity via some but not all of these NBF domains.
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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) | 0.000 | 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".