Back

Enhancer architecture and chromatin accessibility constrain phenotypic space during development

Galupa, R.; Alvarez-Canales, G.; Borst, N. O.; Fuqua, T.; Gandara, L.; Misunou, N.; Richter, K.; Alves, M. R. P.; Karumbi, E.; Perkins, M. L.; Kocijan, T.; Rushlow, C. A.; Crocker, J.

2022-06-02 developmental biology
10.1101/2022.06.02.494376 bioRxiv
Show abstract

Developmental enhancers are DNA sequences that when bound to transcription factors dictate specific patterns of gene expression during development. It has been proposed that the evolution of such cis-regulatory elements is a major source of adaptive evolution; however, the regulatory and evolutionary potential of such elements remains little understood, masked by selective constraints, drift and contingency. Here, using mutation libraries in Drosophila melanogaster embryos, we observed that most mutations in classical developmental enhancers led to changes in gene expression levels but rarely resulted in novel expression outside of the native cell- and tissue-types. In contrast, random sequences often acted as developmental enhancers, driving expression across a range of levels and cell-types, in patterns consistent with transcription factor motifs therein; random sequences including motifs for transcription factors with pioneer activity acted as enhancers even more frequently and resulting in higher levels of expression. Together, our findings suggest that the adaptive phenotypic landscapes of developmental enhancers are constrained by both enhancer architecture and chromatin accessibility. We propose that the evolution of existing enhancers is limited in its capacity to generate novel phenotypes, whereas the activity of de novo elements is a primary source of phenotypic novelty. QUOTE"Chance and chance alone has a message for us." Milan Kundera, The Unbearable Lightness of Being

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.