The Identification of Novel Proteins That Interact With the GLP-1 Receptor and Restrain its Activity
Bibliographic record
Abstract
Glucagon-like peptide 1 receptor (GLP-1R) controls diverse physiological functions in tissues including the pancreatic islets, brain, and heart. To understand the mechanisms that control glucagon-like peptide 1 (GLP-1) signaling better, we sought to identify proteins that interact with the GLP-1R using a membrane-based split ubiquitin yeast two-hybrid (MYTH) assay. A screen of a human fetal brain cDNA prey library with an unliganded human GLP-1R as bait in yeast revealed 38 novel interactor protein candidates. These interactions were confirmed in mammalian Chinese hamster ovarian cells by coimmunoprecipitation. Immunofluorescence was used to show subcellular colocalization of the interactors with GLP-1R. Cluster analysis revealed that the interactors were primarily associated with signal transduction, metabolism, and cell development. When coexpressed with the GLP-1R in Chinese hamster ovarian cells, 15 interactors significantly altered GLP-1-induced cAMP accumulation. Surprisingly, all 15 proteins inhibited GLP-1-activated cAMP. Given GLP-1's prominent role as an incretin, we then focused on 3 novel interactors, SLC15A4, APLP1, and AP2M1, because they are highly expressed and localized to the membrane in mouse insulinoma β-cells. Small interfering RNA-mediated knockdown of each candidate gene significantly enhanced GLP-1-induced insulin secretion. In conclusion, we have generated a novel GLP-1R-protein interactome, identifying several interactors that suppress GLP-1R signaling. We suggest that the inhibition of these interactors may serve as a novel strategy to enhance GLP-1R activity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".