Divergent acute <i>versus</i> prolonged pharmacological GLP-1R responses in adult beta cell-selective β-arrestin 2 knockout mice
Bibliographic record
Abstract
Abstract The glucagon-like peptide-1 receptor (GLP-1R) is a major therapeutic target in type 2 diabetes (T2D) and obesity. Following activation, GLP-1Rs are rapidly desensitised by β-arrestins, scaffolding proteins that terminate G protein interactions but also act as independent signalling mediators. While GLP-1R interacts with β-arrestins 1 and 2, expression of the latter is greatly enhanced in beta cells, making this the most relevant isoform. Here, we have assessed in vivo glycaemic responses to the pharmacological GLP-1R agonist exendin-4 in adult beta cell-selective β-arrestin 2 knockout (KO) mice. Lean female and high-fat, high-sucrose-fed KO mice of both sexes displayed worse acute responses versus control littermates, an effect that was inverted 6 hours post-agonist injection, resulting in prolonged in vivo cell-cell connectivity in KO islets implanted in mouse eyes. Similar effects were observed for the clinically relevant semaglutide and tirzepatide but not with exendin-phe1, an agonist biased away from β-arrestin recruitment. Ex vivo acute cAMP was impaired, but overnight desensitisation was reduced in KO islets. The acute signalling defect was attributed to enhanced β-arrestin 1 and phosphodiesterase (PDE) 4 activity in the absence of β-arrestin 2, while the reduced desensitisation correlated with altered GLP-1R trafficking, involving impaired recycling and lysosomal targeting and increased trans-Golgi network (TGN) localisation and signalling, as well as reduced GLP-1R ubiquitination by the E3 ubiquitin ligase NEDD4. This study has unveiled fundamental aspects of the role of β-arrestin 2 in regulating pharmacological GLP-1R responses with direct application to the rational design of improved GLP-1R-targeting therapeutics.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".