Multicellular organisms must precisely control the growth and size of tissues and organs. For this, cells reproducibly and accurately interpret information conveyed by extrinsic signals and perceived by a large array of sensing machinery. Malfunction of these growth-regulatory pathways decreases organismal fitness and can lead to diseases like cancer. While several major pathways controlling organ and tissue growth have been well characterized, there are two very important areas that are not well understood. First, how are multiple, often conflicting growth regulatory extrinsic signals integrated to impact growth? Second, how does natural genetic variation impact these growth pathways and their responses to extrinsic signals? The root of the model plant Arabidopsis thaliana is ideal for studying the genetic and molecular bases for how organ growth is adjusted based on multiple signals, and how these responses are modulated by genotype. This is because plants, in particular their root systems, have evolved mechanisms for tightly coordinating all aspects of growth and development to environmental conditions. Moreover, it is possible to efficiently and quantitatively monitor root growth over long periods of time without sacrificing the organism (as is the case for mammals). Moreover, it is possible to monitor thousands of plants in parallel, enabling large-scale genetic approaches for assessing multiple environmental conditions. To date, 1135 isogenic strains of Arabidopsis have been fully sequenced and many of these strains respond in distinct ways to environmental signals, providing a platform for phenomics and genome-wide association studies to identify gene variants responsible for these contrasting growth responses. Taken together, the Arabidopsis root is a unique system for studying how organ growth is coordinated to multiple environmental signals, and how this coordination is modulated by genotype. It has recently been shown that root growth responses to low iron levels are largely determined by natural genetic variation within a group of receptor kinases and a protein kinase. At least two of these genes are also involved in responses to flagellin, a pathogen associated molecular pattern. Based on these data, a model has been formulated that this receptor kinase module integrates iron and defense cues (which promote and inhibit growth, respectively) to regulate root growth. In this proposal, experiments will test the hypotheses that protein- protein interactions and their dynamics within this receptor kinase module modulate root growth in response to iron and flagellin (Aim 1), and that allelic variation in the receptor kinase module determines growth sensitivities to iron and microbial signals, as well as the integration of these signals (Aim 2). Finally, mechanistic studies will be performed to determine the molecular processes by which root growth is regulated in response to iron levels (Aim 3). Overall, this project will provide insights into how multiple signals are integrated to regulate organ growth, and how each individual's genotype modulates this process.
Plant roots can sense a diversity of environmental signals (e.g., levels of water, nutrients, and pathogens in the soil) and adjust growth of root structure to maximize plant health. We have shown that a set of 3 protein receptor kinases and a protein kinase adjusts root growth in response to low levels of iron, that a subset of these proteins is involved in the response to the presence of the pathogen associated molecular pattern flagellin, and that natural genetic variation in genes encoding these proteins determines in large part why different plants respond differently to environmental challenges. Here we will take advantage of our automated, high-throughput root phenotyping system to characterize mechanisms by which roots integrate multiple, often conflicting signals to regulate cellular growth; results have implications for the regulation of organ growth/size in all multicellular organisms, and for designing personalized therapies based on a patient's specific genetic background.
