Genotype-by-environment interactions for seminal fluid expression and sperm competitive ability
Bibliographic record
Abstract
Abstract Sperm competition commonly occurs whenever females mate multiply, leading to variation in male paternity success. This can be due to variation in the various traits that might affect sperm competitive ability, which itself depends on both genetic and environmental factors, as well as on genotype-by-environment interactions (GEI). Seminal fluid is a major component of the male ejaculate that is often expected to mediate sperm competition, where different genotypes can differ in their seminal fluid expression as a response to different level of sperm competition (i.e., exhibit GEI). We therefore here focussed on testing for GEI in expression of two recently identified seminal fluid transcripts, suckless-1 and suckless-2 , which potentially modulate sperm competitive ability in the simultaneously hermaphroditic flatworm Macrostomum lignano via their effects on manipulating post-mating partner behaviour and ultimately the fate of transferred ejaculates. In addition, we sought to test for GEI in sperm competitive ability, to investigate the relationship between natural variation in the expression of these seminal fluid transcripts generated through GEI and relative paternity success. To do so, we manipulated social group size, which has been shown to successfully alter sperm competition level in M. lignano , in a set of inbred lines (genotypes) and then measured both the expression level of suckless-1 and suckless-2 in focal worms together with their relative paternity success in a standardised sperm competition ( P 1 & P 2 ) assay. We found GEI for the expression level of suckless-1 and suckless-2 , as well as for sperm competitive ability. Moreover, we found a positive relation between the expression of suckless-1 and paternity success. This suggests that natural variation in the expression of this seminal fluid transcript indeed can influence sperm competition outcomes in M. lignano .
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".