Sexual selection and pseudogenization in primate fertilization
Bibliographic record
Abstract
Abstract The mouse sperm protein ZP3R interacts with proteins in the egg coat and mediates sperm–egg adhesion in a species-specific manner. Understanding the function and evolution of such genes has important implications for studies of speciation, reproductive success, and infertility. A recent analysis showed that (1) the human pseudogene C4BPAP1 is the ortholog of Zp3r , (2) ZP3R pseudogenization evolved independently in parallel among several primate lineages, and (3) functional ZP3R genes evolve under positive selection among other primate species. The causes of this pseudogenization of ZP3R are unknown. To explore one plausible cause (changes in sexual selection on males), we searched for ZP3R pseudogenes in recently published genomes, then compared sexually selected male traits among lineages with and without a functional ZP3R . We found that traits associated with more intense sexual selection on males (large male body size, larger male canines, larger testes) were associated with functional ZP3R expression, and suggest that a relaxation of sexual selection may be associated with selection for ZP3R pseudogenization. This proposed causal relationship implies an evolutionary cost to maintaining redundancy in the suite of primate fertilization genes. Lay summary In sexual interactions more is often assumed to be better. But the evolution of animal genomes suggests that sometimes less is more: the adaptive loss of genes that function in sex may be favored by selection. How could this happen? One surprising answer comes from analyzing humans and some other primate species that have turned off a key gene called ZP3R that helps sperm bind to eggs. The loss of that gene function in some primates is associated with male traits (smaller bodies, smaller canines, smaller testes) that often indicate less vigorous selection on males to compete for matings with females. That correlation implies that the same selection acting on male morphological traits may also act on sperm molecular traits. The correlation also implies that it’s expensive to keep some genes turned on, and that when they’re no longer helpful it’s adaptive to turn them off or allow them to become fallow. This economy of gene expression in sex is an under-explored area of research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".