Effects of Arginine Vasopressin on Islet Cells in Pancreatic Tissue Slices: Glucose-Dependent Modulation of IP3 Receptor-Specific Responses
Bibliographic record
Abstract
Abstract Arginine vasopressin (AVP) is well known for regulating fluid volume, osmotic balance, and vascular tone. Its role in the regulation of pancreatic α and β cell function has been reported, yet its effects are not fully understood, particularly regarding its interaction with plasma glucose levels. The osmotic and volume challenges posed by hyper- and hypoglycaemia in diabetes can be a significant complication of effective hormonal regulation of metabolism. In this study, we investigated the effects of AVP and synthetic peptide receptor agonists and antagonists on α and β cells in pancreatic tissue slices using live confocal Ca2+ imaging. Our findings demonstrate that AVP exerts glucose-dependent effects on both cell types. At low glucose concentrations, AVP, in combination with physiologically or pharmacologically increased cAMP levels, selectively activated α cells without significantly affecting β cells. In contrast, at higher glucose concentrations and pharmacologically elevated cAMP levels, physiological levels of AVP enhanced β cell activity, leading to increased Ca2+ oscillations and insulin release. In both cell types, AVP displayed a bell-shaped concentration dependence, with lower AVP concentrations stimulating hormone release and higher concentrations leading to diminished responses, consistent with inositol trisphosphate receptor (IP3R) activation and inactivation properties. Furthermore, our results indicate that AVP acts primarily through V1b receptors in β cells, with no involvement of V1a, V2 or oxytocin receptors. These findings provide new insights into the modulation of glucose-dependent release of pancreatic hormones by AVP in the context of changed blood osmolality due to hyper- or hypoglycemia in diabetes. Importantly, these results emphasize the potential of targeting AVP signaling pathways as a therapeutic approach in diabetes research, aiming to improve hormone regulation and nutrient homeostasis.
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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) | 0.001 | 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 | 0.001 |
| 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".