Molecular Biology and EvolutionEvolutionary BiologyPeer-reviewed journal article
Repeated shifts in sociality are associated with fine-tuning of highly conserved and lineage-specific enhancers in a socially flexible bee
Beryl M. JonesiD, Andrew E. WebbiD, Scott M. GeibiD, Sheina B. SimiD, Rena M. SchweizeriD, Michael G. BranstetteriD, Jay Daniel EvansiD, Sarah D. KocheriD
Abstract
Comparative genomic studies of social insects suggest that changes in gene regulation are associated with evolutionary transitions in social behavior, but the activity of predicted regulatory regions has not been tested empirically. We used STARR-seq, a high-throughput enhancer discovery tool, to identify and measure the activity of enhancers in the socially variable sweat bee, Lasioglossum albipes. We identified over 36,000 enhancers in the L. albipes genome from three social and three solitary populations. Many enhancers were identified in only a subset of L. albipes populations, revealing rapid divergence in regulatory regions within this species. Population-specific enhancers were often proximal to the same genes across populations, suggesting compensatory gains and losses of regulatory regions may preserve gene activity. We also identified 1182 enhancers with significant differences in activity between social and solitary populations, some of which are conserved regulatory regions across species of bees. These results indicate that social trait variation in L. albipes is driven both by the fine-tuning of ancient enhancers as well as lineage-specific regulatory changes. Combining enhancer activity with population genetic data revealed variants associated with differences in enhancer activity and identified a subset of differential enhancers with signatures of selection associated with social behavior. Together, these results provide the first empirical map of enhancers in a socially flexible bee and highlight links between cis-regulatory variation and the evolution of social behavior.
- assay: ATAC-seq
- research focus: chromatin & epigenetic regulation
- research focus: gene regulatory network inference
- research focus: variant calling
- research focus: differential expression
- approach: deep learning
- approach: statistical model
- Citations
- 8
- FWCI
- 2.33
- Field percentile
- top 13%
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