Evolution of nonspectral rhodopsin function at high altitudes
Bibliographic record
Abstract
High-altitude environments present a range of biochemical and physiological challenges for organisms through decreases in oxygen, pressure, and temperature relative to lowland habitats. Protein-level adaptations to hypoxic high-altitude conditions have been identified in multiple terrestrial endotherms; however, comparable adaptations in aquatic ectotherms, such as fishes, have not been as extensively characterized. In enzyme proteins, cold adaptation is attained through functional trade-offs between stability and activity, often mediated by substitutions outside the active site. Little is known whether signaling proteins [e.g., G protein-coupled receptors (GPCRs)] exhibit natural variation in response to cold temperatures. Rhodopsin (RH1), the temperature-sensitive visual pigment mediating dim-light vision, offers an opportunity to enhance our understanding of thermal adaptation in a model GPCR. Here, we investigate the evolution of rhodopsin function in an Andean mountain catfish system spanning a range of elevations. Using molecular evolutionary analyses and site-directed mutagenesis experiments, we provide evidence for cold adaptation in RH1. We find that unique amino acid substitutions occur at sites under positive selection in high-altitude catfishes, located at opposite ends of the RH1 intramolecular hydrogen-bonding network. Natural high-altitude variants introduced into these sites via mutagenesis have limited effects on spectral tuning, yet decrease the stability of dark-state and light-activated rhodopsin, accelerating the decay of ligand-bound forms. As found in cold-adapted enzymes, this phenotype likely compensates for a cold-induced decrease in kinetic rates-properties of rhodopsin that mediate rod sensitivity and visual performance. Our results support a role for natural variation in enhancing the performance of GPCRs in response to cold temperatures.
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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 | 0.001 |
| 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".