Phosphatidylinositol 4,5-Biphosphate (PIP2) Modulates Interaction of Syntaxin-1A with Sulfonylurea Receptor 1 to Regulate Pancreatic β-Cell ATP-sensitive Potassium Channels
Bibliographic record
Abstract
In β-cells, syntaxin (Syn)-1A interacts with SUR1 to inhibit ATP-sensitive potassium channels (KATP channels). PIP2 binds the Kir6.2 subunit to open KATP channels. PIP2 also modifies Syn-1A clustering in plasma membrane (PM) that may alter Syn-1A actions on PM proteins like SUR1. Here, we assessed whether the actions of PIP2 on activating KATP channels is contributed by sequestering Syn-1A from binding SUR1. In vitro binding showed that PIP2 dose-dependently disrupted Syn-1A·SUR1 complexes, corroborated by an in vivo Forster resonance energy transfer assay showing disruption of SUR1(-EGFP)/Syn-1A(-mCherry) interaction along with increased Syn-1A cluster formation. Electrophysiological studies of rat β-cells, INS-1, and SUR1/Kir6.2-expressing HEK293 cells showed that PIP2 dose-dependent activation of KATP currents was uniformly reduced by Syn-1A. To unequivocally distinguish between PIP2 actions on Syn-1A and Kir6.2, we employed several strategies. First, we showed that PIP2-insensitive Syn-1A-5RK/A mutant complex with SUR1 could not be disrupted by PIP2, consequently reducing PIP2 activation of KATP channels. Next, Syn-1A·SUR1 complex modulation of KATP channels could be observed at a physiologically low PIP2 concentration that did not disrupt the Syn-1A·SUR1 complex, compared with higher PIP2 concentrations acting directly on Kir6.2. These effects were specific to PIP2 and not observed with physiologic concentrations of other phospholipids. Finally, depleting endogenous PIP2 with polyphosphoinositide phosphatase synaptojanin-1, known to disperse Syn-1A clusters, freed Syn-1A from Syn-1A clusters to bind SUR1, causing inhibition of KATP channels that could no longer be further inhibited by exogenous Syn-1A. These results taken together indicate that PIP2 affects islet β-cell KATP channels not only by its actions on Kir6.2 but also by sequestering Syn-1A to modulate Syn-1A availability and its interactions with SUR1 on PM.
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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) | 0.001 | 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".