Molecular determinants of ATP-sensitive potassium channel MgATPase activity: diabetes risk variants and diazoxide sensitivity
Bibliographic record
Abstract
ATP-sensitive K(+) (KATP) channels play an important role in insulin secretion. KATP channels possess intrinsic MgATPase activity that is important in regulating channel activity in response to metabolic changes, although the precise structural determinants are not clearly understood. Furthermore, the sulfonylurea receptor 1 (SUR1) S1369A diabetes risk variant increases MgATPase activity, but the molecular mechanisms remain to be determined. Therefore, we hypothesized that residue-residue interactions between 1369 and 1372, predicted from in silico modelling, influence MgATPase activity, as well as sensitivity to the clinically used drug diazoxide that is known to increase MgATPase activity. We employed a point mutagenic approach with patch-clamp and direct biochemical assays to determine interaction between residues 1369 and 1372. Mutations in residues 1369 and 1372 predicted to decrease the residue interaction elicited a significant increase in MgATPase activity, whereas mutations predicted to possess similar residue interactions to wild-type (WT) channels elicited no alterations in MgATPase activity. In contrast, mutations that were predicted to increase residue interactions resulted in significant decreases in MgATPase activity. We also determined that a single S1369K substitution in SUR1 caused MgATPase activity and diazoxide pharmacological profiles to resemble those of channels containing the SUR2A subunit isoform. Our results provide evidence, at the single residue level, for a molecular mechanism that may underlie the association of the S1369A variant with type 2 diabetes. We also show a single amino acid difference can account for the markedly different diazoxide sensitivities between channels containing either the SUR1 or SUR2A subunit isoforms.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".