CONDITION-DEPENDENCE OF THE SEXUALLY DIMORPHIC TRANSCRIPTOME IN DROSOPHILA MELANOGASTER
Bibliographic record
Abstract
Sexually dimorphic traits are by definition exaggerated in one sex, which may arise from a history of sex-specific selection-in males, females, or both. If this exaggeration comes at a cost, exaggeration is expected to be greater in higher condition individuals (condition-dependent). Although studies using small numbers of morphological traits are generally supportive, this prediction has not been examined at a larger scale. We test this prediction across the transcriptome by determining the condition-dependence of sex-biased (dimorphic) gene expression. We find that high-condition populations are more sexually dimorphic in transcription than low-condition populations. High-condition populations have more male-biased genes and more female-biased genes, and a greater degree of sexually dimorphic expression in these genes. Also, condition-dependence in male-biased genes was greater than in a set of unbiased genes. Interestingly, male-biased genes expressed in the testes were not more condition-dependent than those in the soma. By contrast, increased female-biased expression under high condition may have occurred because of the greater contribution of the ovary-specific transcripts to the entire mRNA pool. We did not find any genomic signatures distinguishing the condition-dependent sex-biased genes. The degree of condition-dependent sexual dimorphism (CDSD) did not differ between the autosomes and the X chromosome. There was only weak evidence that rates of evolution correlated with CDSD. We suggest that the sensitivity of both female-biased genes and male-biased genes to condition may be akin to the overall heightened sensitivity to condition that life-history and sexually selected traits tend to exhibit. Our results demonstrate that through condition-dependence, early life experience has dramatic effects on sexual dimorphism in the adult transcriptome.
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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".