Stathmin-2 Mediates Glucagon Secretion From Pancreatic α-Cells
Bibliographic record
Abstract
Inhibition of glucagon hypersecretion from pancreatic α-cells is an appealing strategy for the treatment of diabetes. Our hypothesis is that proteins that associate with glucagon within alpha cell secretory granules will regulate glucagon secretion, and may provide druggable targets for controlling abnormal glucagon secretion in diabetes. Recently, we identified a dynamic glucagon interactome within the secretory granules of the α cell line, αTC1-6, and showed that select proteins within the interactome could modulate glucagon secretion. In the present study, we show that one of these interactome proteins, the neuronal protein stathmin-2, is expressed in αTC1-6 cells and in mouse pancreatic alpha cells, and is a novel regulator of glucagon secretion. The secretion of both glucagon and Stmn2 was significantly enhanced in response to 55 mM K+, and immunofluorescence confocal microscopy showed co-localization of stathmin-2 with glucagon and the secretory granule markers chromogranin A and VAMP-2 in αTC1-6 cells. In mouse pancreatic islets, Stathmin-2 co-localized with glucagon, but not with insulin, and co-localized with secretory pathway markers. To show a function for stathmin-2 in regulating glucagon secretion, we showed that siRNA - mediated depletion of stathmin-2 in αTC1-6 cells caused glucagon secretion to become constitutive without any effect on proglucagon mRNA levels, while overexpression of stathmin-2 completely abolished both basal and K+-stimulated glucagon secretion. Overexpression of stathmin-2 increased the localization of glucagon into the endosomal-lysosomal compartment, while depletion of stathmin-2 reduced the endosomal localization of glucagon. Therefore, we describe stathmin-2 as having a novel role as an alpha cell secretory granule protein that modulates glucagon secretion via trafficking through the endosomal-lysosomal system. These findings describe a potential new pathway for the regulation of glucagon secretion, and may have implications for controlling glucagon hypersecretion in diabetes.
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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) | 0.001 | 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".