Varying signals of the effects of natural selection during teleost growth hormone gene evolution
Bibliographic record
Abstract
The growth hormone (GH) gene of teleost fish exhibits a higher degree of variability compared with other vertebrate groups. However, the different selective constraints at the sequence level are not well understood. In this study, maximum-likelihood (ML) models of codon substitutions were used to investigate Darwinian adaptive evolution of the GH gene in teleost fishes. Complete GH gene sequences of 54 fish species were classified into 4 orders, and the variable nature of GH was examined by determining the dN and dS rate variation and the rates of molecular evolution for each teleost order. The results indicate that although the overall evolution rate for teleost GH is high ((1.15 +/- 0.01) x 10(-9) substitutions/(aa site x y)) compared with the "slow phases" in mammals ((0.21 to 0.28 +/- 0.05) x 10(-9)), the vital structure of this gene has been retained. While the majority of the amino acid changes appear to be due to relaxation of purifying selection, some positively selected sites were detected in regions with no specifically identified role in protein function. The positively selected regions observed in salmoniformes lineage suggests a possible role for positive selection driving functional divergence in paralogous forms of the GH gene after whole-genome duplication in this lineage.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".