Variation in the Evolution and Sequences of Proglucagon and the Receptors for Proglucagon-Derived Peptides in Mammals
Bibliographic record
Abstract
) encodes three glucagon like sequences, glucagon, glucagon-like peptide-1 (GLP-1), and glucagon-like peptide-2 that are of similar length and share sequence similarity, with these hormones having cell surface receptors, glucagon receptor (Gcgr), GLP-1 receptor (Glp1r), and GLP-2 receptor (Glp2r), respectively. Gcgr, Glp1r, and Glp2r are all class B1 G protein-coupled receptors (GPCRs). Despite their sequence and structural similarity, analyses of sequences from rodents have found differences in patterns of sequence conservation and evolution. To determine whether these were rodent-specific traits or general features of these genes in mammals I analyzed coding and protein sequences for proglucagon and the receptors for proglucagon-derived peptides from the genomes of 168 mammalian species. Single copy genes for each gene were found in almost all genomes. In addition to glucagon sequences within Hystricognath rodents (e.g., guinea pig), glucagon sequences from a few other groups (e.g., pangolins and some bats) as well as changes in the proteolytic processing of GLP-1 in some bats are suggested to have functional effects. GLP-2 sequences display increased variability but accepted few substitutions that are predicted to have functional consequences. In parallel, Glp2r sequences display the most rapid protein sequence evolution, and show greater variability in amino acids at sites involved in ligand interaction, however most were not predicted to have a functional consequence. These observations suggest that a greater diversity in biological functions for proglucagon-derived peptides might exist in mammals.
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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 | 0.001 | 0.001 |
| 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".