The AMPK activator A-769662 inhibits human TASK3 potassium channels in an AMPK-independent manner
Bibliographic record
Abstract
ABSTRACT Heteromeric TASK1/3 channels play a fundamental role in oxygen-sensing by carotid body type 1 cells, where hypoxia-induced inhibition of TASK3 and/or TASK1/3 potassium currents leads to depolarisation, voltage-gated calcium entry, exocytotic transmitter release and increases in carotid body afferent input responses that initiate corrective changes in breathing patterns. However, the mechanism by which hypoxia leads to TASK-1/3 channel inhibition is still debated. It had been proposed that the AMP-activated protein kinase (AMPK) might directly phosphorylate and inhibit TASK channels, in particular TASK-3, although subsequent studies on rat type I cells argued against this view. Here we report on the effects of novel, highly selective AMPK activators on recombinant human TASK-3 potassium channels. Sequence alignment identified an AMPK recognition motif in TASK-3, but not TASK-1, with Ser 55 representing a potential site for AMPK-dependent phosphorylation in TASK-3. However, neither of the AMPK activators, AICAR or MK-8722, caused a significant reduction of human TASK-3 current amplitude. By contrast, high concentrations of the AMPK activator A-769662 (100-500 µM) inhibited human TASK-3 currents in a concentration-dependent manner. Importantly, A-769662 (300 µM) also inhibited human TASK-3 channels in HEK293 cells that stably over-expressed an AMPK-β1 subunit mutant (S108A) that renders AMPK insensitive to activators binding the Allosteric Drug and Metabolite (ADaM) site, such as A-769662. We therefore identify A-769662 as a novel human TASK-3 channel inhibitor and provide conclusive evidence that AMPK does not regulate TASK-3 channel currents.
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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) | 0.002 | 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".