How sex shapes transcriptome evolution in the songbird brain
Bibliographic record
Abstract
Sex differences have captivated scientists for a long time, yet the evolutionary rate of change in sex-biased gene expression has not been directly quantified. To address this issue, we leverage brain gene expression data from male and female songbirds. To do this, we introduce new options for unbounded Brownian motion and variable evolutionary rates among genes in the software package CAGEE (Computational Analysis of Gene Expression Evolution). We applied these new features to 10 focal songbird species, half of which have convergently evolved obligate cavity-nesting, an element of reproductive ecology linked to sex-specific changes in competition. We find that the degree of sex bias - measured as the male:female ratio in expression for each gene - evolves twice as fast on the Z chromosome vs. autosomes, but otherwise, Z gene expression does not evolve at different rates in males vs. females. Most Z-linked genes are male-biased in their expression, though some exhibit roughly equal patterns of expression. These sex-balanced genes are not skewed in their rate of evolution, contrary to the hypothesis that some genes experience selection for balance and therefore may evolve more slowly. Finally, the degree of sex bias in gene expression evolves more quickly along obligate-cavity nesting lineages, suggesting that changes in sex-specific ecological selection shape the evolution of brain sex differences, or lack thereof. Together, these tools and results provide new insights on the interplay between sex and gene expression evolution.
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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 | 0.001 | 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".