Reduction of bitter taste receptor gene family in folivorous colobine primates relative to omnivorous cercopithecine primates
Bibliographic record
Abstract
Bitter taste perception is important in preventing animals from ingesting potentially toxic compounds. Whole-genome assembly (WGA) data have revealed that bitter taste receptor genes (TAS2Rs) comprise a multigene family with dozens of intact and disrupted genes in primates. However, publicly available WGA data are often incomplete, especially for multigene families. In this study, we employed a targeted capture (TC) approach specifically probing TAS2Rs for ten species of cercopithecid primates with diverse diets, including eight omnivorous cercopithecine species and two folivorous colobine species. We designed RNA probes for all TAS2Rs that we modeled to be intact in the common ancestor of cercopithecids ("ancestral-cercopithecid TAS2R gene set"). The TC was followed by short-read and high-depth massive-parallel sequencing. TC retrieved more intact TAS2R genes than found in WGA databases. We confirmed a large number of gene "births" at the common ancestor of cercopithecids and found that the colobine common ancestor and the cercopithecine common ancestor had contrasting trajectories: four gene "deaths" and three gene births, respectively. The number of intact TAS2R genes was markedly reduced in colobines (25-28 detected via TC and 20-26 detected via WGA analysis) as compared with cercopithecines (27-36 via TC and 19-30 via WGA). Birth or death events occurred at almost every phylogenetic-tree branch, making the composition of intact genes variable among species. These results show that evolutionary change in intact TAS2R genes is a complex process, refute a simple general prediction that herbivory favors more TAS2R genes, and have implications for understanding dietary adaptations and the evolution of detoxification abilities.
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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".