Regulated protein turnover is critical for cellular decision-making in all eukaryotes. The rate of degradation often acts as a pacemaker to coordinate responses within and between cells and is frequently dysregulated in human disease. Yet we know remarkably little about what controls substrate degradation kinetics. One possible reason is the lack of available models for high resolution structure-function analysis of turnover rates. The SCF class of E3 ubiquitin ligases is highly conserved among animals, plants and fungi. Within these complexes, F-boxes act as substrate-recognition subunits to add ubiquitin to diverse targets. We propose to use an SCF involved in auxin response, at the heart of nearly every aspect of plant biology, as a model to investigate general principles underlying E3 function. The small-molecule triggered degradation in the auxin pathway offers a unique advantage for these studies, and has facilitated our engineering of auxin-induced degradation in yeast. Using this system, we discovered that F-boxes greatly impact the rate of degradation and that previously uncharacterized sequences accelerated or decelerated degradation in a substrate-specific manner. Our central hypothesis is that the wide range of auxin-induced substrate degradation dynamics are critical for achieving the wide range of auxin-regulated cellular responses, and that these dynamics are encoded by distinct domains within F-boxes and substrates. To test this hypothesis, we propose to: (1) identify determinants of degradation rate in F-boxes and substrates. We have already identified two new substrate domains controlling degradation rates and several new F-box domains of interest. Structural studies are underway to investigate the functional relevance of these domains, and we are using a number of tools to quantify complex affinity to test the degree to which this parameter dictates degradation rates. (2) quantify the impact of varying degradation rate on transcription. We have extended our synthetic assays to include auxin- induced transcription. We can now quantify and mathematically model the relationship between substrate turnover and downstream responses. This technology enables our study of previously intractable problems like the impact of substrate dimerization on auxin responses. (3) quantify the impact of varying degradation rate on development. We have preliminary data from transgenic plants linking substrate degradation rate to the proper development of new organs. We will perform in-depth cellular and molecular phenotypic analysis of these plants to analyze how developmental progression is affected by timing of substrate turnover. Together, the proposed work will provide a mechanistic framework for E3 function in the auxin response, facilitate identification of network architectures impacting signal sensitivity and duration, and potentially provide insights into fundamental properties of E3: substrate interactions. These insights can inform our understanding of E3s associated with human disease, as well as guiding future design of synthetic circuits using auxin components for diagnostic or therapeutic applications.

Public Health Relevance

Ubiquitin-mediated protein degradation is key to cellular homeostasis and is often disrupted in human disease. The human genome encodes more E3s than protein kinases, and the specificity of E3: substrate interactions make them attractive drug targets. A structural and biochemical understanding of the dynamic nature of E3: substrate interactions and a readily-screened synthetic context might allow new breakthroughs in this area.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
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Membrane Biology and Protein Processing Study Section (MBPP)
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Gerratana, Barbara
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University of Washington
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de Lange, Orlando; Klavins, Eric; Nemhauser, Jennifer (2018) Synthetic genetic circuits in crop plants. Curr Opin Biotechnol 49:16-22
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Pierre-Jerome, Edith; Wright, R Clay; Nemhauser, Jennifer L (2017) Characterizing Auxin Response Circuits in Saccharomyces cerevisiae by Flow Cytometry. Methods Mol Biol 1497:271-281
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