Thursday, November 21, 2024
11/21/2024
PROJECT SUMMARY/ABSTRACT The proposed work will investigate transcriptional interpretation of signaling through the ERK pathway, which plays critical roles in animal development and is commonly deregulated in human diseases. We will use Drosophila as an experimental model that offers unrivaled opportunities for dissecting gene regulation by ERK signaling at multiple levels of biological organization, from specific ERK substrates to the whole embryo. Aim 1 focuses on Capicua (Cic), a transcriptional repressor that was discovered in Drosophila and has recently emerged as a key sensor of ERK activation in developmental and pathological contexts. We will identify functionally significant phosphorylation sites in Cic and investigate their effects on the ERK-dependent control of Cic protein stability, nuclear localization, and DNA binding. Aim 2 is designed to bridge the gap between genetic studies, which commonly identify only a handful of ERK substrates, and omics-level studies, which suggest that ERK functions through large substrate cohorts. We will evaluate these two scenarios using an already working combination of acute optogenetic perturbations, quantitative phosphoproteomics, and live imaging of functionally significant transcriptional responses to ERK signaling. Finally, Aim 3 will study transcriptional effects of ERK signaling, which commonly works by simultaneously activating some cell fates and repressing others. We will use quantitative optogenetic perturbations and live imaging to test the hypothesis that activating and repressing effects of ERK signaling require different levels of ERK activation. Our experimental tests of this hypothesis will address a key issue in developmental ERK signaling and will provide quantitative data needed for predictive computational modeling. Feasibility of the proposed work is supported by preliminary data that include functional characterization of Cic phosphosites (Aim 1), a phosphoproteomics approach for the in vivo discovery of ERK substrates (Aim 2), and an optogenetic approach to data-driven design of predictive computational models (Aim 3).
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