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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".