Evolution of gene expression across brain regions in behaviorally divergent deer mice
Bibliographic record
Abstract
Abstract The evolution of innate behaviors is ultimately due to genetic variation likely acting in the nervous system. Gene regulation may be particularly important because it can evolve in a modular brain-region specific fashion through the concerted action of cis - and trans -regulatory changes. Here, to investigate transcriptional variation and its regulatory basis across the brain, we perform RNA sequencing (RNA-Seq) on ten brain subregions in two sister species of deer mice ( Peromyscus maniculatus and P. polionotus ) – which differ in a range of innate behaviors, including their social system – and their F 1 hybrids. We find that most of the variation in gene expression distinguishes subregions, followed by species. Interspecific differential expression (DE) is pervasive (52–59% of expressed genes), whereas the number of DE genes between sexes is modest overall (∼3%). Interestingly, the identity of DE genes varies considerably across brain regions. Much of this modularity is due to cis -regulatory divergence, and while 43% of genes were consistently assigned to the same gene regulatory class across subregions (e.g., conserved, cis -, or trans -regulatory divergence), a similar number were assigned to two or more different gene regulatory classes. Together, these results highlight the modularity of gene expression differences and divergence in the brain, which may be key to explain how the evolution of brain gene expression can contribute to the astonishing diversity of animal behaviors.
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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 | 0.001 |
| 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".