Coexpression of the Type 2 Diabetes Susceptibility Gene Variants <i>KCNJ11</i> E23K and <i>ABCC8</i> S1369A Alter the ATP and Sulfonylurea Sensitivities of the ATP-Sensitive K+ Channel
Bibliographic record
Abstract
OBJECTIVE: In the pancreatic beta-cell, ATP-sensitive K(+) (K(ATP)) channels couple metabolism with excitability and consist of Kir6.2 and SUR1 subunits encoded by KCNJ11 and ABCC8, respectively. Sulfonylureas, which inhibit the K(ATP) channel, are used to treat type 2 diabetes. Rare activating mutations cause neonatal diabetes, whereas the common variants, E23K in KCNJ11 and S1369A in ABCC8, are in strong linkage disequilibrium, constituting a haplotype that predisposes to type 2 diabetes. To date it has not been possible to establish which of these represents the etiological variant, and functional studies are inconsistent. Furthermore, there have been no studies of the S1369A variant or the combined effect of the two on K(ATP) channel function. RESEARCH DESIGN AND METHODS: The patch-clamp technique was used to study the nucleotide sensitivity and sulfonylurea inhibition of recombinant human K(ATP) channels containing either the K23/A1369 or E23/S1369 variants. RESULTS: ATP sensitivity of the K(ATP) channel was decreased in the K23/A1369 variant (half-maximal inhibitory concentration [IC(50)] = 8.0 vs. 2.5 mumol/l for the E23/S1369 variant), although there was no difference in ADP sensitivity. The K23/A1369 variant also displayed increased inhibition by gliclazide, an A-site sulfonylurea drug (IC(50) = 52.7 vs. 188.7 nmol/l for the E23/S1369 variant), but not by glibenclamide (AB site) or repaglinide (B site). CONCLUSIONS: Our findings indicate that the common K23/A1369 variant K(ATP) channel displays decreased ATP inhibition that may contribute to the observed increased risk for type 2 diabetes. Moreover, the increased sensitivity of the K23/A1369 variant to the A-site sulfonylurea drug gliclazide may provide a pharmacogenomic therapeutic approach for patients with type 2 diabetes who are homozygous for both risk alleles.
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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) | 0.003 | 0.001 |
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".