Multiple Mechanisms Promote the Retained Expression of Gene Duplicates in the Tetraploid Frog Xenopus laevis
Bibliographic record
Abstract
Gene duplication provides a window of opportunity for biological variants to persist under the protection of a co-expressed copy with similar or redundant function. Duplication catalyzes innovation (neofunctionalization), subfunction degeneration (subfunctionalization), and genetic buffering (redundancy), and the genetic survival of each paralog is triggered by mechanisms that add, compromise, or do not alter protein function. We tested the applicability of three types of mechanisms for promoting the retained expression of duplicated genes in 290 expressed paralogs of the tetraploid clawed frog, Xenopus laevis. Tests were based on explicit expectations concerning the ka/ks ratio, and the number and location of nonsynonymous substitutions after duplication. Functional constraints on the majority of paralogs are not significantly different from a singleton ortholog. However, we recover strong support that some of them have an asymmetric rate of nonsynonymous substitution: 6% match predictions of the neofunctionalization hypothesis in that (1) each paralog accumulated nonsynonymous substitutions at a significantly different rate and (2) the one that evolves faster has a higher ka/ks ratio than the other paralog and than a singleton ortholog. Fewer paralogs (3%) exhibit a complementary pattern of substitution at the protein level that is predicted by enhancement or degradation of different functional domains, and the remaining 13% have a higher average ka/ks ratio in both paralogs that is consistent with altered functional constraints, diversifying selection, or activity-reducing mutations after duplication. We estimate that these paralogs have been retained since they originated by genome duplication between 21 and 41 million years ago. Multiple mechanisms operate to promote the retained expression of duplicates in the same genome, in genes in the same functional class, over the same period of time following duplication, and sometimes in the same pair of paralogs. None of these paralogs are superfluous; degradation or enhancement of different protein subfunctions and neofunctionalization are plausible hypotheses for the retained expression of some of them. Evolution of most X. laevis paralogs, however, is consistent with retained expression via mechanisms that do not radically alter functional constraints, such as selection to preserve post-duplication stoichiometry or temporal, quantitative, or spatial subfunctionalization.
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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 | 0.000 | 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) | 0.000 | 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".