The evolution of seminal fluid gene expression and postmating reproductive isolation in <i>Drosophila</i>
Bibliographic record
Abstract
Seminal fluid protein (Sfp) genes show, in general, a higher rate of sequence divergence than genes from other categories, which is often attributed to forms of postcopulatory sexual selection or sexual conflict. Recently, the relaxation of selective constraints has been proposed as an alternative explanation for the rapid sequence evolution of Sfps and other genes with sex-limited expression. The expression of Sfp genes is a likely target of selection, but the evolution of differences in their expression levels is less understood. Here, we explore both polymorphism and divergence in Sfp gene expression between Drosophila melanogaster and Drosophila simulans, how selection might have influenced their expression, and whether changes in expression might trigger the evolution of reproductive isolating barriers. In our analysis, Sfp genes showed higher divergence, but not higher polymorphism, in expression than the average male reproductive glands gene. Sfp genes with reproductive-tissue-specific expression were enriched for both directional and stabilizing selection, while relaxed selection was the predominant mode of evolution among Sfp genes with any other nonreproductive tissue-specific or nontissue-specific expression. The knockdown of single genes known to affect intraspecific sperm competition, and with patterns of expression divergence and polymorphism suggestive of directional selection, was not enough to break down postmating reproductive isolation barriers between species. Our results identify the expression of male-specific Sfp genes as an enriched target of selection and suggest a complex molecular relationship between postcopulatory sexual selection on a single gene's expression and its effect on the onset of speciation.
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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 | 0.001 | 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) | 0.001 | 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".