Relaxation of selective constraint on the sweet-taste receptor gene TAS1R2 in lorisiform primates
Bibliographic record
Abstract
Recent research has revealed considerable evolutionary diversity in umami-taste (amino acids and nucleotides) and sweet-taste receptor TAS1R genes across vertebrate species. To contribute to a growing understanding of how diet shapes taste evolution, we studied TAS1R genes in non-anthropoid primates with highly diverse diets, including members of the Strepsirrhini, comprised of Lorisiformes (lorises) and Lemuriformes (lemurs), as well as members of the Tarsiiformes (tarsiers). We employed a targeted capture (TC) approach specifically probing all the three mammalian TAS1R genes, i.e., TAS1R1 (for sensing umami), TAS1R2 (sweet) and TAS1R3 (required for forming a heterodimer), followed by short-read massive-parallel sequencing for three lorisiform, four lemuriform, and one tarsiiform species. Analyzing together with publicly available whole-genome assemblies (WGAs) of non-anthropoids, we found that TAS1R1 and TAS1R2 of some lorisiform species were disrupted. The relative evolutionary rates in introns and synonymous sites of all the three TAS1R genes, as well as non-genic genome regions, of lorisiforms were higher than those of lemuriforms, a finding consistent with the higher genome-wide mutation rate of the lorisiforms. We found a similar pattern in the amino acid sequences and nonsynonymous sites of the sweet receptor TAS1R2 in lorisiforms. Evolutionary rates of amino acid sequences and nonsynonymous sites in TAS1R1 and TAS1R3 of lorisiforms were as slow as those of lemuriforms. This suggests that functional constraint on sweet sensing has been relaxed in lorisiform primates since their common ancestor. These results shed a new light on understanding evolutionary diversification of umami and sweet sensing in a diverse group of mammals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".