Gαo Represses Insulin Secretion by Reducing Vesicular Docking in Pancreatic β-Cells
Bibliographic record
Abstract
OBJECTIVE: Pertussis toxin uncoupling-based studies have shown that Gαi and Gαo can inhibit insulin secretion in pancreatic β-cells. Yet it is unclear whether Gαi and Gαo operate through identical mechanisms and how these G-protein-mediated signals inhibit insulin secretion in vivo. Our objective is to examine whether/how Gαo regulates islet development and insulin secretion in β-cells. RESEARCH DESIGN AND METHODS: Immunoassays were used to analyze the Gαo expression in mouse pancreatic cells. Gαo was specifically inactivated in pancreatic progenitor cells by pancreatic cell-specific gene deletion. Hormone expression and insulin secretion in response to different stimuli were assayed in vivo and in vitro. Electron microscope and total internal reflection fluorescence-based assays were used to evaluate how Gαo regulates insulin vesicle docking and secretion in response to glucose stimulation. RESULTS: Islet cells differentiate properly in Gαo(-/-) mutant mice. Gαo inactivation significantly enhances insulin secretion both in vivo and in isolation. Gαo nullizygous β-cells contain an increased number of insulin granules docked on the cell plasma membrane, although the total number of vesicles per β-cell remains unchanged. CONCLUSIONS: Gαo is not required for endocrine islet cell differentiation, but it regulates the number of insulin vesicles docked on the β-cell membrane.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Molecular physiology of G-protein regulation of insulin secretion.
The study examines insulin secretion and pancreatic biology, not research practice.
Cell biology of G-protein regulation of insulin secretion, domain biomedical science.
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) | 0.001 | 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".