Effects of two seminal fluid proteins on post-mating behavior in the simultaneously hermaphroditic flatworm <i>Macrostomum lignano</i>
Bibliographic record
Abstract
Abstract Along with sperm, in many taxa male ejaculates also contain a complex mixture of proteins, peptides and other substances found in seminal fluid. Once seminal fluid proteins (SFPs) are transferred to the mating partner, they play crucial roles in mediating post-mating sexual selection, since they can modulate the partner’s behavior and physiology in ways that influence the reproductive success of both partners. One way in which sperm donors can maximize their own reproductive success is by changing the partners’ (sperm recipient’s) postcopulatory behavior to prevent or delay re-mating, thereby decreasing the likelihood or intensity of sperm competition. We therefore adopted a quantitative genetic approach combining gene expression and behavioral data to identify candidates that could mediate such a response in the simultaneously hermaphroditic flatworm Macrostomum lignano . We identified two putative SFPs - Mlig-pro46 and Mlig-pro63 - that exhibit a negative genetic correlation between transcript expression and mating frequency. Importantly, however, in one of the two different group sizes, differing in their sperm competition level, in which we measured genetic correlations, these same two transcripts are also linked to a second post-mating behavior in M. lignano , namely the ‘suck’ behavior of recipients in which, upon ejaculate receipt, the worm places its pharynx over its female genital opening and appears to attempt to remove ejaculate components. To therefore investigate directly whether these two candidates manipulate partner behavior, and test whether this impacts on competitive fertilization success, we performed a manipulative experiment using RNA interference-induced knockdown to ask how loss of Mlig-pro46 and Mlig-pro63 expression, singly and in combination, affects mating frequency, partner suck propensity and both defensive and offensive sperm competitive ability ( P 1 and P 2 , respectively). None of the knock-down treatments impacted strongly on mating frequency or sperm competitive ability, but the knock-down of Mlig-pro63 resulted in a significantly decreased ‘suck’ propensity of mating partners. This suggests that Mlig-pro63 may normally act as a cue in the ejaculate to trigger recipient suck behavior, though the functional and adaptive significance of these two seminal proteins from a donor perspective remains enigmatic.
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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".