Structural Mapping and Functional Characterization of Zebrafish Class B G-Protein Coupled Receptor (GPCR) with Dual Ligand Selectivity towards GLP-1 and Glucagon
Bibliographic record
Abstract
GLP-1 and glucagon regulate glucose metabolism through a network of metabolic pathways initiated upon binding to their specific receptors that belong to class B G-protein coupled receptors (GPCRs). The therapeutic potential of glucagon is currently being evaluated, while GLP-1 is already used in the treatment of type 2 diabetes and obesity. Development of a second generation of GLP-1 based therapeutics depends on a molecular and structural understanding of the interactions between the GLP-1 receptor (GLP-1R) and its ligand GLP-1. There is considerable sequence conservation between GLP-1 and glucagon and between the hGLP-1R and human glucagon receptor (hGCGR), yet each receptor recognizes only its own specific ligand. Glucagon receptors in fish and frogs also exhibit ligand selectivity only towards glucagon and not GLP-1. Based on competitive binding experiments and assays of increase in intracellular cAMP, we demonstrate here that a GPCR in zebrafish (Danio rerio) exhibits dual ligand selectivity towards GLP-1 and glucagon, a characteristic not found in mammals. Further, many structural features found in hGLP-1R and hGCGR are also found in this zebrafish GPCR (zfGPCR). We show this by mapping of its sequence and structural features onto the hGLP-1R and hGCGR based on their partial and complementary crystal structures. Thus, we propose that zfGPCR represents a dual GLP-1R/GCGR. The main differences between the three receptors are in their stalk regions that connect their N-terminal extracellular domains (NECDs) with their transmembrane domains and the absence of loop 3 in the NECD in zfGLP-1R/GCGR. These observations suggest that the interactions between GLP-1 and glucagon with loop 3 and the stalk regions may induce different conformational changes in hGLP-1R and hGCGR upon ligand binding and activation that lead to selective recognition of their native ligands.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".