Many plant defense reactions against pathogens are inducible. Plant recognition mechanisms detect pathogens and initiate signaling mechanisms that in turn activate plant defenses. One mode of recognition detects molecules that are common to particular classes of pathogens (called microbe-associated molecular patterns, MAMPs). The signal triggered by a MAMP is relayed through a complex network of signaling molecules, many of which are proteins. The goal of this research is to elucidate how the signaling network is organized and how the signal flows through the network during MAMP-induced disease resistance. The response of Arabidopsis thaliana to a bacterial pathogen will be analyzed using a combination of 3 approaches: 1) experimental deletion of specific combinations of the network components; 2) the collection of information about the signaling process from many points of the network, and 3) computer modeling of the signaling network. This approach is designed to broaden our understanding about the behavior of complex signaling networks in general and also allow exploration of the conditions that can substantially change the behavior of the plant defense network.
Broader impacts: This project will build an interdisciplinary research team working to model biological signaling networks. It will provide interdisciplinary training at the interface of biology and computer science to undergraduate students and to a post-doctoral fellow and a Ph.D. student. Outreach programs will engage faculty and students from undergraduate institutions to participate in the project. The project will also provide summer research experiences for high school teachers, through existing and newly-developed programs.
The plant immune signaling network is constantly targeted by plant pathogens that rapidly evolve to dampen the plant immunity. Signaling networks for other biological processes do not face the level of assault anywhere close to that the immune signaling network does. This is why the plant immune signaling network has evolved to be highly robust against attack to the components of the network. This robustness makes studying signaling mechanisms underlying the network behavior by conventional genetics approaches very difficult: compared to the intact network, disruption of a network component does not affect the network output much. Thus, we developed an opposite approach. We almost completely broke the network function by disrupting multiple important components simultaneously, and then we reconstituted the network one by one. In this way, we were able to clearly define the role of each component and the ways how combinations of the components work together. By extending this approach, we succeeded in building a computer model that simulates how signals flow in the network and predicts the behavior of the network well. The underlying signal flows are so complex that the potential of improving pathogen resistance in crops based on conventional knowledge is limited. With computer models that capture true roles of important signaling mechanisms, the potential can be substantially extended because they will allow a large number of virtual experiments in a short time. We also investigated several signal convergence points in the network at a molecular level. Signal convergence (but not tree-like signal branching per se) is the one that makes the behavior of the network complex. While models will guide in what way the network should be manipulated, this molecular knowledge provides tools that enable manipulations. Both guidance and enabling technologies are crucial in crop improvements as the world is facing a big challenge of increasing food production while minimizing environmental impacts. Whereas there are many researchers who are experts in biology and collaborate with computer scientists or the other way around, there are not many who are trained well in both fields and can integrate concepts of both fields effortlessly. We have trained individuals in a truly interdisciplinary manner at undergraduate, graduate, and postdoctoral levels.
