bioRxivGeneticsPreprint
Genome architecture shapes the evolutionary origins of redundant enhancers in fly and mouse
Abstract
Shadow enhancers are groups of DNA regulatory elements that control the same target gene and drive overlapping expression patterns. Large-scale surveys have found shadow enhancers control most developmental genes in animal genomes. The way in which shadow enhancers arise and how they subsequently evolve may further illuminate their regulatory logic and mechanisms of action. To investigate the evolutionary origins of shadow enhancers, we searched for sequence signatures of three birth mechanisms: duplication of existing enhancers, transposable element (TE) co-option, and TE-mediated splitting of ancestral regulatory elements in the Drosophila melanogaster and mouse genomes. Using 420 fly shadow enhancer sets and 9,051 mouse shadow enhancer sets, we found detectable duplication evidence in 18.3% of fly shadow enhancer sets and 33.9% of mouse sets. Duplication signatures were more frequent in larger shadow sets, suggesting that repeated duplication can expand regulatory landscapes. TE-derived enhancers were present in both species but were not enriched in shadow enhancers relative to single enhancers, suggesting that TE co-option contributes to enhancer evolution generally rather than preferentially generating redundant enhancer architectures. Finally, TE-mediated enhancer splitting was rare in both genomes. These results indicate that shadow enhancer birth is mechanistically heterogeneous, reflecting a mixture of duplication, TE co-option, and other mechanisms, whose contributions are shaped by genome architecture and evolutionary time. Therefore, we find that overlapping regulatory functions can arise through multiple evolutionary routes and that birth mechanisms can influence, but do not strictly determine, the regulatory logic of the resulting enhancer set.
- organism: Drosophila
- organism: mouse
- assay: DNA methylation
- assay: ChIP-seq
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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