Shavenbaby as a model to link phenotypic and gene regulatory changes across Drosophila evolution
Authors: Tatiana Gaitan 1; Kaelan Brennan 1; Julia Zeitlinger 1,2
Affiliations: 1) Stowers Institute for Medical Research, Kansas City, MO; 2) The University of Kansas Medical Center, Kansas City, KS
Keywords: d. evolution of gene expression; e. enhancers
Phenotypic variability resulting from changes in gene regulation is often mediated by changes at cis-regulatory DNA sequences, i.e., enhancers. However, how changes in DNA sequence at enhancers generate different phenotypic outputs across evolution is not well understood. Understanding how transcription factors (TFs) read out cis-regulatory information at enhancers has been challenging, in part due to technical limitations associated with mapping TF binding at their motifs with sufficiently high-resolution to study crucial facets of transcriptional regulation such as, motif arrangement (syntax) and cooperativity between TFs. In the Drosophila embryo, axis patterning signaling defines the expression pattern of Shavenbaby (Svb), a key regulator of epidermal cell shape, which then induces the expression of cellular effectors that specify actin-rich projections called trichomes. How Svb directs the transcriptional activation of its targets presents an excellent opportunity for evolutionary analysis of motif syntax because of the epidermal phenotypes that are strikingly different between species. Thus, to interrogate the syntax of Svb targets, we are collecting genome-wide binding information in late-stage D. melanogaster embryos and applying our high-resolution ChIP technique, ChIP-nexus, to map TF binding at base-resolution. With these data, we observe high-resolution footprints of Svb at its own motif within known epidermal enhancers. To obtain predictive rules for Svb binding that we can apply in an evolutionary context, we are combining Svb ChIP-nexus data with BPNet, a deep learning model that uses DNA sequences to predict ChIP-nexus binding information. BPNet successfully learns Svb ChIP-nexus data and identifies motifs predictive for Svb binding, which map to known Svb enhancers, and uncharacterized regions that represent new candidate enhancers. Next, we are identifying suitable Drosophilaspecies for evolutionary comparisons by performing cuticle preparations, Western blots, and immunostaining. With the strongest candidates, we aim to compare changes in motif syntax, and cooperativity with candidate TFs at target enhancers. Svb ChIP-nexus data together with BPNet provide an unprecedented opportunity to analyze neutral and functional changes in TF binding, and will further our understanding of how gene regulatory logic modulates phenotypic output across evolution.