Kv2.1 Clustering Contributes to Insulin Exocytosis and Rescues Human β-Cell Dysfunction
Bibliographic record
Abstract
Insulin exocytosis is regulated by ion channels that control excitability and Ca2+ influx. Channels also play an increasingly appreciated role in microdomain structure. In this study, we examine the mechanism by which the voltage-dependent K+ (Kv) channel Kv2.1 (KCNB1) facilitates depolarization-induced exocytosis in INS 832/13 cells and β-cells from human donors with and without type 2 diabetes (T2D). We find that Kv2.1, but not Kv2.2 (KCNB2), forms clusters of 6–12 tetrameric channels at the plasma membrane and facilitates insulin exocytosis. Knockdown of Kv2.1 expression reduces secretory granule targeting to the plasma membrane. Expression of the full-length channel (Kv2.1-wild-type) supports the glucose-dependent recruitment of secretory granules. However, a truncated channel (Kv2.1-ΔC318) that retains electrical function and syntaxin 1A binding, but lacks the ability to form clusters, does not enhance granule recruitment or exocytosis. Expression of KCNB1 appears reduced in T2D islets, and further knockdown of KCNB1 does not inhibit Kv current in T2D β-cells. Upregulation of Kv2.1-wild-type, but not Kv2.1-ΔC318, rescues the exocytotic phenotype in T2D β-cells and increases insulin secretion from T2D islets. Thus, the ability of Kv2.1 to directly facilitate insulin exocytosis depends on channel clustering. Loss of this structural role for the channel might contribute to impaired insulin secretion in diabetes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".