Glucagon-Like Peptide-1 Receptor Activation Antagonizes Voltage-Dependent Repolarizing K+ Currents in β-Cells
Bibliographic record
Abstract
Glucagon-like peptide-1 (GLP-1) acts through its G-protein-coupled receptor to enhance glucose-stimulated insulin secretion from pancreatic beta-cells. This is believed to result from modulation of at least two ion channels: ATP-sensitive K(+) (K(ATP)) channels and voltage-dependent Ca(2+) channels. Here, we report that GLP-1 receptor signaling also regulates the activity of beta-cell voltage-dependent K(+) (K(V)) channels, themselves potent glucose-dependent regulators of insulin secretion. GLP-1 receptor activation with exendin 4 (10(-8) mol/l) in rat beta-cells antagonized K(V) currents by 43.3 +/- 6.3%, whereas the GLP-1 receptor antagonist exendin 9-39 had no effect. The effect of GLP-1 receptor activation on K(V) currents could be replicated (current reduction of 55.7 +/- 6.0%) by G-protein activation with GMP-PNP (10 nmol/l). The cAMP pathway antagonist Rp-cAMPS (100 micro mol/l) prevented current inhibition by exendin 4, implicating cAMP signaling in GLP-1 receptor modulation of beta-cell K(V) currents. Finally, exendin 4 (10(-8) mol/l) increased the amplitude (130 +/- 5.7%) and duration (285 +/- 15.9%) of the beta-cell depolarization response to current injection, independent of any effect on K(ATP) or Ca(2+) channels. The present results demonstrate that GLP-1 receptor signaling can antagonize beta-cell repolarization by reducing voltage-dependent K(+) currents, an effect likely to contribute to GLP-1's glucose-dependent insulinotropic effect.
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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) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".