Seminal fluid gene expression and reproductive fitness in Drosophila melanogaster
Bibliographic record
Abstract
BACKGROUND: The rapid evolution of seminal fluid proteins (SFPs) has been suggested to be driven by adaptations to postcopulatory sexual selection (e.g. sperm competition). However, we have recently shown that most SFPs evolve rapidly under relaxed selective pressures. Given the role of SFPs in competition for fertilization phenotypes, like the ability to transfer and store sperm and the modulation of female receptivity and ovulation, the prevalence of selectively relaxed SFPs appears as a conundrum. One possible explanation is that selection on SFPs might be relaxed in terms of protein amino acid content, but adjustments of expression are essential for post-mating function. Interestingly, there is a general lack of systematic implementation of gene expression perturbation assays to monitor their effect on phenotypes related to sperm competition. RESULTS: We successfully manipulated the expression of 16 SFP encoding genes using tissue-specific knockdowns (KDs) and determined the effect of these genes' perturbation on three important post-mating phenotypes: female refractoriness to remating, defensive (P1), and offensive (P2) sperm competitive abilities in Drosophila melanogaster. Our analyses show that KDs of tested SFP genes do not affect female refractoriness to remating and P2, however, most gene KDs significantly decreased P1. Moreover, KDs of SFP genes that are selectively constrained in terms of protein-coding sequence evolution have lower P1 than KDs of genes evolving under relaxed selection. CONCLUSIONS: Our results suggest a more predominant role, than previously acknowledged, of variation in gene expression than coding sequence changes on sperm competitive ability in D. melanogaster.
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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 | 0.000 | 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) | 0.001 | 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".