Kv2.1 clusters on β-cell plasma membrane act as reservoirs that replenish pools of newcomer insulin granule through their interaction with syntaxin-3
Bibliographic record
Abstract
The voltage-dependent K + (K v ) channel K v 2.1 is a major delayed rectifier in many secretory cells, including pancreatic β cells. In addition, K v 2.1 has a direct role in exocytosis at an undefined step, involving SNARE proteins, that is independent of its ion-conducting pore function. Here, we elucidated the precise step in exocytosis. We previously reported that syntaxin-3 (Syn-3) is the key syntaxin that mediates exocytosis of newcomer secretory granules that spend minimal residence time on the plasma membrane before fusion. Using high-resolution total internal reflection fluorescence microscopy, we now show that K v 2.1 forms reservoir clusters on the β-cell plasma membrane and binds Syn-3 via its C-terminal C1b domain, which recruits newcomer insulin secretory granules into this large reservoir. Upon glucose stimulation, secretory granules were released from this reservoir to replenish the pool of newcomer secretory granules for subsequent fusion, occurring just adjacent to the plasma membrane K v 2.1 clusters. C1b deletion blocked the aforementioned K v 2.1-Syn-3–mediated events and reduced fusion of newcomer secretory granules. These insights have therapeutic implications, as K v 2.1 overexpression in type-2 diabetes rat islets restored biphasic insulin secretion.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".