Identification and Characterization of a Glucagon Receptor from the Goldfish Carassius auratus: Implications for the Evolution of the Ligand Specificity of Glucagon Receptors in Vertebrates
Bibliographic record
Abstract
The structural basis of ligand selectivity of G protein-coupled receptors for metabolic hormones has been an area of intense investigation, and yet it remains unresolved. One approach to delineating the mechanism of ligand-receptor interactions is to compare the ligand specificities of receptors expressed in species that emerged at different times within vertebrate evolution. In this paper we describe the isolation, functional, and phylogenetic characterization of the glucagon receptor from the goldfish Carassius auratus (Teleostei, order Cypriniformes), and compare its ligand specificity with that of the homologous rat receptor. Goldfish (gf) glucagon stimulated glucose production in a dose-dependent manner from isolated goldfish hepatocytes, resulting in 5-fold increase at 1 microm. The goldfish glucagon receptor (gfGlucR) shares 56, 51, 50, and 52% amino acid identities with frog Rana tigrina regulosa, mouse, rat, and human glucagon receptors, respectively. In competitive binding experiments, the recombinant gfGlucR displays high affinity toward goldfish, zebrafish, and human glucagons (IC(50) = 0.6, 9, and 13 nm, respectively) but not toward goldfish glucagon-like peptide-1 or human glucagon-like peptide-1 (7-36) amide. Whereas both goldfish and human glucagons stimulated dose-dependent increases in intracellular cAMP through the recombinant gfGlucR, the recombinant rat GlucR interacted only with human glucagon, analogous to the specificity of the previously characterized glucagon receptor from the frog R. tigrina regulosa. Our results demonstrate that the binding pocket of gfGlucR can accommodate a broad range of glucagon structures and that in the frogs and mammals, there is a structural switch to a more restrictive conformation of glucagon receptors.
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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".