Molecular evolution of non-visual opsin genes across environmental, developmental, and morphological adaptations in frogs
Bibliographic record
Abstract
Abstract Non-visual opsins are transmembrane proteins expressed in the eyes, skin, and brain of many animals. When paired with a light-sensitive chromophore, non-visual opsins form photopigment systems involved in various non-visual, light-detection functions, including circadian rhythm regulation, light-seeking behavior, and detection of seasonality. Previous research has primarily explored the diversity and function of non-visual opsins in model organisms, with few studies investigating their molecular evolution in non-model species. Here we explored molecular evolution of non-visual opsin genes in anuran amphibians (frogs and toads). With diverse lifestyles ranging from fossorial to aquatic, anurans inhabit a diverse array of light environments, which makes them a compelling system for studying the evolution of light detection mechanisms. Using whole-eye transcriptomes from 79 anuran species, as well as genomes and multi-tissue transcriptomes from an additional 15 species, we 1) identify which non-visual opsin genes are expressed in the eyes of anurans; 2) compare selective constraint (ω, or dN/dS) among non-visual opsin genes; and 3) test for potential adaptive evolution by comparing selection between discrete ecological classes in anurans (e.g. diurnal versus non-diurnal). We consistently recovered 14 non-visual opsin genes from anuran eye transcriptomes, compared to 18 genes that we recovered genome wide, and detected positive selection in a subset of these genes. We also found variation in selective constraint between discrete ecological and life-history classes, which may reflect functional adaptation in non-visual opsin genes. Although non-visual opsins remain poorly understood, these findings provide insight into their molecular evolution and set the stage for future research on their potential function across taxa with diverse environmental, developmental, and morphological adaptations.
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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".