Convergent selection pressures drive the evolution of rhodopsin kinetics at high altitudes via nonparallel mechanisms
Bibliographic record
Abstract
Convergent evolution in response to similar selective pressures is a well-known phenomenon in evolutionary biology. Less well understood is how selection drives convergence in protein function, and the underlying mechanisms by which this can be achieved. Here, we investigate functional convergence in the visual system of two distantly related lineages of high-altitude adapted Andean and Himalayan catfishes. Statistical analyses revealed in the two high-altitude lineages, a parallel acceleration of evolutionary rates in rhodopsin, the dim-light visual pigment. However, the elevated rates were found to be accompanied by substitutions at different sites in the protein. Experiments substituting Andean- or Himalayan-specific residues significantly accelerated the kinetic rates of rhodopsin, destabilizing the ligand-bound forms. As found in cold-adapted enzymes, this phenotype likely compensates for a cold-induced decrease in kinetic rates, properties of rhodopsin mediating rod sensitivity and visual performance. Our study suggests that molecular convergence in protein function can be driven by parallel shifts in evolutionary rates but via nonparallel molecular mechanisms. Signatures of natural selection may therefore be a powerful guide for identifying complex instances of functional convergence across a wider range of protein systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".