Gene duplication plays a major role in gene co-option: Studies into the evolution of the motilin/ghrelin family and their receptors
Bibliographic record
Abstract
Extant genes can be modified, or ‘tinkered with’, to provide new roles or new characteristics of these genes. At the genetic level, this often involves gene duplication and specialization of the resulting genes into particular functions. We investigate how ligand-receptor partnerships evolve after gene duplication. While significant work has been conducted in this area, the examination of additional models should help us better understand the proposed models and potentially reveal novel evolutionary patterns and dynamics. We use bioinformatics, comparative genomics and phylogenetic analyses to show that preproghrelin and prepromotilin descended from a common ancestor and that a gene duplication generated these two genes shortly after the divergence of amphibians and amniotes. The evolutionary history of the receptor family differs from that of their cognate ligands. GPR39 diverges first, and an ancestral receptor gives rise to receptors classified as fish-specific clade A, GHSR and MLNR by successive gene duplications occurring before the divergence of tetrapods and ray-finned fish. The ghrelin/GHSR system is maintained and functionally conserved from fish to mammals. Motilin- MLNR specificity must have arisen by ligand-receptor coevolution after the MLN hormone gene diverged from the GHRL gene in the amniote lineage. Conserved molecular machinery can give rise to new neuroendocrine response mechanisms by the co-option of duplicated genes. Gene duplication is both parsimonious and creative in producing elements for evolutionary tinkering and plays a major role in gene co-option, thus aiding the evolution of greater biological complexity.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".