A Targeted RNAi Screen Identifies Endocytic Trafficking Factors That Control GLP-1 Receptor Signaling in Pancreatic β-Cells
Bibliographic record
Abstract
The glucagon-like peptide 1 (GLP-1) receptor (GLP-1R) is a key target for type 2 diabetes (T2D) treatment. Because endocytic trafficking of agonist-bound receptors is one of the most important routes for regulation of receptor signaling, a better understanding of this process may facilitate the development of new T2D therapeutic strategies. Here, we screened 29 proteins with known functions in G protein-coupled receptor trafficking for their role in GLP-1R potentiation of insulin secretion in pancreatic β-cells. We identify five (clathrin, dynamin1, AP2, sorting nexins [SNX] SNX27, and SNX1) that increase and four (huntingtin-interacting protein 1 [HIP1], HIP14, GASP-1, and Nedd4) that decrease insulin secretion from murine insulinoma MIN6B1 cells in response to the GLP-1 analog exendin-4. The roles of HIP1 and the endosomal SNX1 and SNX27 were further characterized in mouse and human β-cell lines and human islets. While HIP1 was required for the coupling of cell surface GLP-1R activation with clathrin-dependent endocytosis, the SNXs were found to control the balance between GLP-1R plasma membrane recycling and lysosomal degradation and, in doing so, determine the overall β-cell incretin responses. We thus identify key modulators of GLP-1R trafficking and signaling that might provide novel targets to enhance insulin secretion in T2D.
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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 | 0.001 | 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 | 0.001 |
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".