Gene age shapes functional and evolutionary properties of the <i>Drosophila</i> seminal fluid proteome
Bibliographic record
Abstract
Seminal fluid proteins (Sfps) are crucial for animal reproductive success, with Sfp-encoding genes believed to be evolutionarily young due to heightened rates of gene loss and gain and rapidly evolving at the sequence level relative to other genes. Using estimates of the phylogenetic origin of each Drosophila melanogaster Sfp gene, based on genomic resources that include outgroup species to the Drosophila genus, we examined the functional attributes and evolutionary characteristics of 357 Sfp genes relative to their evolutionary age. Contrary to the perception that many Sfp genes are evolutionarily young, 62% existed in the genome of the ancestor to this genus. These ancient genes have broader expression profiles, more expansive biological roles, and have more interactions with non-Sfp genes. This increased pleiotropy has imposed constraints on the rate of sequence evolution in ancient Sfp genes compared to younger ones. Notably, these younger Sfp genes evolve substantially faster as a result of both adaptive and nonadaptive evolutionary forces. Within the Sfp interactome, we identified a fast-evolving core subnetwork of younger genes with more restricted tissue expression and functions. Our systematic approach has uncovered a large set of ancient Sfp genes with distinct genomic, functional, and evolutionary characteristics compared to the younger, more commonly studied Sfp genes. With increased refined genomic and functional data acquisition across a wider variety of taxa, our approach and results serve as proof of the importance of systemic strategies applied to broadly defined gene sets based on their time of evolutionary origination.
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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".