Plant Immune Signaling Network

A major type of plant immunity against pathogens is inducible immunity: immune responses are turned on upon recognition of pathogen attack. The immune signaling network mediating signal transduction for inducible immunity needs to be highly resilient because pathogens that can evolve much faster than plants interfere with components of the network. Essentially, resilience of the plant immune signaling network hides the underlying mechanisms from pathogen evolution. At the same time, since immune responses are expensive, unnecessary immune responses cost plant fitness. Since the reliability of pathogen attack signals varies, outputs of the immune signaling network need to be tunable through selection according to the reliability of pathogen attack signals. A main focus of our research is to understand how the network properties, such as resilience and tunability, emerge from the structure and the dynamics of the plant immune signaling network.

In a resilient network, a deficiency in part of the network does not strongly affect the network function because some other parts of the network compensate the deficiency. Thus, a conventional genetic approach of comparing a single gene mutant phenotype to the wild-type phenotype does not provide much information about the mechanism. We developed an approach called network reconstitution to overcome this problem: first, multiple genes, each of which is important for different parts of the network (different subnetworks), are simultaneously mutated to almost completely break the network function; second, the subnetworks are restored to the broken network, one by one or in combinations, to learn the function of each subnetwork and interactions between subnetworks. Thus, the network reconstitution approach enables mechanistic understanding of a resilient network.

We have been using the model plant Arabidopsis thaliana to apply the network reconstitution approach to the immune signaling network. We have been collecting high-dimensional data, such as mRNA profiles by RNA-seq, from a set of plants with comprehensive reconstituted versions of the network, through the time course after treatment with various immune elicitors. In collaboration with Chad Myers’ group (UMN, Dept of Computer Science and Engineering), we are currently applying differential equation-based models to these data to discover the quantitative rules of mechanistic interactions among the subnetworks during the dynamic process of response to the immune elicitors. Once we learn most of such rules, we will be able to simulate the network dynamics in silico to investigate emergence of the network properties in detail.

The source of complex network behaviors is convergence of multiple signals at some network components. We are biologically studying such signal converging points at the molecular level. For example, we have experimentally discovered a subnetwork in which a signal for one mode of immunity inhibits that for another mode of immunity. This discovery appears to represent a mechanism to limit unnecessary immune responses when one type of immunity is effective.

We are also interested in how such a resilient network evolved during evolution of land plants. We do not know how but generally know when. Major components of the immune signaling network emerged or started specializing about the time of Seed Plant divergence. Thus, the network properties we have observed in Arabidopsis are likely very different in Non-Seed Plants (e.g., mosses and ferns). All Angiosperms (flowering plants) have almost complete sets of the network components we observed in Arabidopsis. In one particular case, we found that three subfamilies of a protein family with overlapping or opposing signaling functions have been evolving very fast, yet evolution of the three subfamilies has been strongly influencing one another in a plant-lineage-specific manner. This coevolution across subfamilies can confer resilience to the immune signaling module.

Having undergraduate researchers involved is another focus of our research program. Every semester we have a team of undergraduate students working together to discover Arabidopsis immunity genes. The current team project takes advantage of our knowledge gained through network reconstitution studies. It started with a plant line with three subnetworks removed, allowing deep genetic interrogation of the remaining single subnetwork.

Tsuda, K., Sato, M., Stoddard, T., Glazebrook, J., and Katagiri, F. (2009) “Network properties of robust immunity in plants” PLoS Genet 5(12), e1000772. https://doi.org/10.1371/journal.pgen.1000772 2.

Sato, M., Tsuda, K., Wang, L., Coller, J., Watanabe, Y., Glazebrook, J., and Katagiri, F. (2010) “Network modeling reveals prevalent negative regulatory relationships between signaling sectors in Arabidopsis immune signaling” PLoS Pathog 6(7), e1001011. https://doi.org/10.1371/journal.ppat.1001011 3.

Qi, Y., Tsuda, K., Glazebrook, J., and Katagiri, F. (2011) “Physical association of pattern-triggered immunity (PTI) and effector-triggered immunity (ETI) immune receptors in Arabidopsis” Mol Plant Pathol 12, 702-708 https://doi.org/10.1111/j.1364-3703.2010.00704.x 4.

Qi, Y., Tsuda, K., Nguyen, L. V., Wang, X., Lin, J., Murphy, A. S., Glazebrook, J., Thordal-Christensen, H., and Katagiri, F. (2011) “Physical association of Arabidopsis hypersensitive induced reaction proteins (HIRs) with the immune receptor RPS2” J Biol Chem 286, 31297-31307. https://doi.org/10.1074/jbc.M110.211615 5.

Igarashi, D., Tsuda, K., and Katagiri, F. (2012) “The Peptide Growth Factor, Phytosulfokine, Attenuates Pattern-Triggered Immunity” Plant J. 71, 194-204. https://doi.org/10.1111/j.1365-313X.2012.04950.x 6.

Tsuda, K., Mine, A., Bethke, G., Igarashi, D., Botanga, C. J., Tsuda, Y., Glazebrook, J., Sato, M., and Katagiri, F. (2013) “Dual regulation of gene expression mediated by extended MAPK activation and salicylic acid contributes to robust innate immunity in Arabidopsis.” PLoS Genet 9, e1004015. https://doi.org/10.1371/journal.pgen.1004015 7.

Kim, Y., Tsuda, K., Igarashi, D., Hillmer, R. A., Sakakibara, H., Myers, C. L, and Katagiri, F. (2014) “Signaling mechanisms underlying the robustness and tunability of the plant immune network” Cell Host Microbe 15, 84-94. https://doi.org/10.1016/j.chom.2013.12.002 8.

Katagiri, F., Canelon-Suarez, D., Griffin, K., Petersen, J., Meyer, R. K., Siegle, M., and Mase, K. (2015) “Design and Construction of an Inexpensive Homemade Plant Growth Chamber” PLOS ONE 10, e0126826. https://doi.org/10.1371/journal.pone.0126826 9.

Hillmer, R. A., Tsuda, K., Rallapalli, G., Asai, S., Truman, W., Papke, M. D., Sakakibara, H., Jones, J. D. G., Myers, C. L., and Katagiri, F. (2017) “The Highly Buffered Arabidopsis Immune Signaling Network Conceals the Functions of its Components” PLOS Genet 13, e1006639. https://doi.org/10.1371/journal.pgen.1006639 10.

Hatsugai, N., Igarashi, D., Mase, K., Lu, Y., Tsuda, Y., Chakravarthy, S., Wei, H.-L., Foley, J. W., Collmer, A., Glazebrook, J., and Katagiri, F. (2017) “A plant effector-triggered immunity signaling sector is inhibited by pattern-triggered immunity” EMBO J 36, 2758-2769. https://doi.org/10.15252/embj.201796529 11.

Lu, Y., Truman, W., Liu, X., Bethke, G., Zhou, M., Myers, C. L., Katagiri, F., and Glazebrook, J. (2018) “Different modes of negative regulation of plant immunity by calmodulin related genes” Plant Physiol 176, 3046-3061. https://doi.org/10.1104/pp.17.01209 12.

Katagiri F. (2018) “Review: Plant immune signaling from a network perspective” Plant Sci 276 14-21. https://doi.org/10.1016/j.plantsci.2018.07.013.

Ph.D., Rockefeller University, 1991 
M.S., Kyoto University, Japan, 1986 
B.S., Kyoto University, Japan, 1984