A Novel GLP1 Receptor Interacting Protein ATP6ap2 Regulates Insulin Secretion in Pancreatic Beta Cells
Bibliographic record
Abstract
GLP1 activates its receptor, GLP1R, to enhance insulin secretion. The activation and transduction of GLP1R requires complex interactions with a host of accessory proteins, most of which remain largely unknown. In this study, we used membrane-based split ubiquitin yeast two-hybrid assays to identify novel GLP1R interactors in both mouse and human islets. Among these, ATP6ap2 (ATPase H(+)-transporting lysosomal accessory protein 2) was identified in both mouse and human islet screens. ATP6ap2 was shown to be abundant in islets including both alpha and beta cells. When GLP1R and ATP6ap2 were co-expressed in beta cells, GLP1R was shown to directly interact with ATP6ap2, as assessed by co-immunoprecipitation. In INS-1 cells, overexpression of ATP6ap2 did not affect insulin secretion; however, siRNA knockdown decreased both glucose-stimulated and GLP1-induced insulin secretion. Decreases in GLP1-induced insulin secretion were accompanied by attenuated GLP1 stimulated cAMP accumulation. Because ATP6ap2 is a subunit required for V-ATPase assembly of insulin granules, it has been reported to be involved in granule acidification. In accordance with this, we observed impaired insulin granule acidification upon ATP6ap2 knockdown but paradoxically increased proinsulin secretion. Importantly, as a GLP1R interactor, ATP6ap2 was required for GLP1-induced Ca(2+) influx, in part explaining decreased insulin secretion in ATP6ap2 knockdown cells. Taken together, our findings identify a group of proteins that interact with the GLP1R. We further show that one interactor, ATP6ap2, plays a novel dual role in beta cells, modulating both GLP1R signaling and insulin processing to affect insulin secretion.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".