Septin5 deletion enhances β-cell exocytosis by releasing microtubule-tethered insulin granules onto plasma membrane
Bibliographic record
Abstract
Septin5 interacts with SNARE proteins to regulate exocytosis in neurons, but its role in pancreatic β-cells is unknown. Here, we report that Septin5 is abundant in rodent and human β-cells, deletion of which dramatically enhances biphasic glucose-stimulated insulin secretion, including in type 2 diabetes (T2D). Super-resolution imaging shows that Septin5 is preferentially assembled in microtubule-plasma membrane contact sites in a microtubule-dependent manner, which provides discrete harbor for secretory granule anchoring. By decreasing the stability of the cortical microtubule meshwork, Septin5 depletion increases insulin granule dynamics and access to the plasma membrane. Analysis of spatiotemporal coupling of fusion events and localized Ca2+ influx through L-type Ca2+ channels show that Septin5 depletion increases releasable granule pool clustering on Ca2+ channels, previously shown to be impaired in T2D, thus rectifying this T2D defect. Hence, inhibition of Septin5 can improve insulin secretion. Septins are the key component of the cytoskeleton. Here, the authors show that Septin5 is assembled in microtubule-plasma membrane contact sites and mediates insulin secretion in pancreatic islet β-cells through cortical microtubule remodeling.
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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) | 0.003 | 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".