<i>Cis</i> -regulatory changes in locomotor genes are associated with the evolution of burrowing behavior
Bibliographic record
Abstract
Summary How evolution modifies complex, innate behaviors is largely unknown. Divergence in many morphological traits has been linked, at least in part, to cis -regulatory changes in gene expression, a pattern also observed in some behaviors of recently diverged populations. Given this, we compared the gene expression in the brains of two interfertile sister species of Peromyscus mice, including allele-specific expression (ASE) of their F 1 hybrids, that show large and heritable differences in burrowing behavior. Because cis -regulation may contribute to constitutive as well as activity-dependent gene expression, we also captured a molecular signature of burrowing circuit divergence by quantifying gene expression in mice shortly after burrowing. We found that several thousand genes were differentially expressed between the two sister species regardless of behavioral context, with several thousand more showing behavior-dependent differences. Allele-specific expression in F 1 hybrids showed a similar pattern, suggesting that much of the differential expression is driven by cis -regulatory divergence. Genes related to locomotor coordination showed the strongest signals of lineage-specific selection on burrowing-induced cis -regulatory changes. By comparing these candidate genes to independent quantitative trait locus (QTL) mapping data, we found that the closest QTL markers to these candidate genes are associated with variation in burrow shape, demonstrating an enrichment for candidate locomotor genes near segregating causal loci. Together, our results provide insight into how cis -regulated gene expression can depend on behavioral context as well as how this dynamic regulatory divergence between species can be integrated with forward genetics to enrich our understanding of the genetic basis of behavioral 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 | 0.000 | 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".