You are here

You are here

Fumiaki Katagiri



Lab website >>

Research Interests

I. Plant Immune Network

A major type of plant defense against pathogen is inducible defense: i.e., defense mechanisms are turned on upon recognition of pathogen attack. Research in my group is directed towards understanding (1) how plants recognize pathogen attack and (2) how this recognition leads to induction of coordinated responses in plants. We use Arabidopsis thaliana and its bacterial pathogen Pseudomonas syringae as a model to study these problems. We are interested in a network of molecules that enables inducible defense: how are the components and connections of the network organized?; how is the behavior of the network controlled?
Pattern-triggered immunity (PTI) and effector-triggered immunity (ETI) are two well defined modes of inducible defense. PTI is initiated by recognition of molecular patterns common among related microbes (microbe-associated molecular patterns, MAMPs) by pattern recognition receptors (PRRs). It is necessary for a potential pathogen to negate PTI sufficiently to be a real pathogen. For this purpose, real pathogens deliver effectors into the plant cell that interfere with PTI signaling. Plants may have receptors, resistance (R) proteins, which recognize some of the pathogen effectors and trigger ETI.

The plant immune signaling network that controls inducible defense is different from other plant signaling networks because pathogens not only initiate signaling events but also interfere with plant signaling. Microbial pathogens also evolve much faster than plants. Therefore, the plant immune signaling network must have properties that allow it to withstand perturbations from a wide variety of pathogens without heavily relying on evolutionary adaptation. Unnecessary immune responses carry negative impacts on plant fitness, further constraining possible network properties.

  1. How plants recognize pathogen attack. To gain insights in how R proteins recognize effectors and how this recognition initiates signaling, we identified a number of proteins that form complexes with R proteins (Qi and Katagiri, 2009 and others). This study revealed that at least some R proteins and PRRs form complexes together in microdomains in the plasma membrane, suggesting interactions between PTI and ETI at the very beginning of the processes (Qi et al., 2011a). We are currently studying the potential signaling interactions between PTI and ETI within the receptor complexes.
  2. How this recognition leads to induction of coordinated responses in plants. Our ultimate goal in this area is to understand the signaling mechanisms well enough, so that we will be able to build a quantitative model for this signaling network on computer. Signaling networks for PTI and ETI are overlapped and have highly interconnected architecture. In such a network, a conventional approach of reductionism, i.e., isolating a part based on an assumption of independence from the other parts, does not work well for the purpose of elucidate the behavior of the network as a whole.  We have demonstrated that two strategies are effective in studying such a complex network: perturb a part of the network and collect information from many different points of the network at once (Sato et al., 2010); perturb multiple parts of the network simultaneously so that large effects of perturbations can be observed, and then deconvolute the effects of and interactions among the perturbed parts (Tsuda et al., 2009). In this way, we found that at least some PTI and ETI extensively share the network components but use the network quite differently, that this network has a strong tendency to compensate loss of part of the network and that this compensation is achieved by rerouting signal flows in the network (a backup function instead of simple redundancy). We are combining these two strategies in a time-series context to elucidate the detailed organization and dynamics of the network.
  3. How the network properties influence the network evolution.  When different parts of a network strongly interact one another, such as those in the plant immune signaling network, evolutionary selections on such components cannot be understood as independent events. We are investigating the effect of the immune network properties on network evolution combining approaches of network biology and population genomics.
  4. Computational biology and Bioinformatics.   We are integrating computational approaches to experimental approaches to facilitate our study.   For example, we have applied a general linear model to assign signaling allocations in the immune signaling network (Tsuda et al., 2009) and nonlinear multivariate analysis to infer the signaling network topology (Sato et al., 2010). In collaboration with Chad Myers’ group (Dept of Computer Science and Engineering), we are building dynamic network models using dynamic Bayesian network approaches and others. We perform network analysis and analysis of population genomic data in collaboration with Myers’ group and Peter Morrell’s group (Dept of Agronomy and Plant Genetics).
Recent Publications (refereed only, since 2007)

Sato, M., Mitra, R. M., Coller, J., Wang, D., Spivey, N. W., Dewdney, J., Denoux, C., Glazebrook, J., and Katagiri, F. (2007) “A high performance, small-scale microarray for expression profiling of many samples in Arabidopsis-pathogen studies” Plant J. 49, 565-577.

van Leeuwen, H., Kliebenstein, D. J., West, M. A. L., Kim, K., van Poecke, R., Katagiri, F., Michelmore, R. W., Doerge, R. W., St. Clair, D. A. (2007) “Natural variation among Arabidopsis thaliana accessions for transcriptome response to exogenous salicylic acid.” Plant Cell 19, 2099-2110.

van Poecke, R. M. P., Sato, M., Lenarz-Wyatt, L., Weisberg, S., and Katagiri, F.  (2007) “Natural variation in RPS2-mediated resistance among Arabidopsis accessions: correlation between gene expression profiles and phenotypic responses.” Plant Cell 19, 4046-4060.

Tsuda, K., Sato, M., Glazebrook, J., Cohen, J. D., and Katagiri, F. (2008) “Interplay between MAMP-triggered and SA-mediated defense responses.” Plant J. 53, 763-775.

Wang, L., Mitra, R. M., Hasselmann, K. D., Sato, M., Lenarz-Wyatt, L. M., Cohen, J. D., Katagiri, F., and Glazebrook, J. (2008) “The Genetic Network Controlling the Arabidopsis Transcriptional Response to Pseudomonas syringae pv. maculicola: Roles of Major Regulators and the Phytotoxin Coronatine” Mol. Plant-Microbe Interaction 21, 1408-1420.

Foley, J. W. and Katagiri, F. (2008) “Unsupervised reduction of random noise in complex data by a row-specific, sorted principal component-guided method” BMC Bioinformatics 9, 508.

Qi, Y. and Katagiri, F. (2009) “Purification of low-abundance Arabidopsis plasma-membrane protein complexes and identification of their component candidates” Plant J., 57(5), 932-944.

Wang, L., Tsuda, K., Sato, M., Cohen, J.D., Katagiri, F., and Glazebrook, J.  (2009) “Arabidopsis CaM binding protein CBP60g contributes to MAMP-induced SA accumulation and is involved in disease resistance against Pseudomonas syringae.”  PLoS Pathog 5(2), e1000301.

Tsuda, K., Sato, M., Stoddard, T., Glazebrook, J., and Katagiri, F. (2009) “Network properties of robust immunity in plants” PLoS Genet 5(12), e1000772.

Kang, H.-G., Oh, C.-S., Sato, M., Katagiri, F., Glazebrook, J., Takahashi, H., Kachroo, P., Martin, G. B., and Klessig, D. F. (2010) “Endosome-associated CRT1 functions early in R (resistance) gene-mediated defense signaling” Plant Cell 22, 918-936.

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.

Qi, Y., Tsuda, K., Joe, A., Sato, M., Nguyen, L. V., Glazebrook, J., Alfano, J. R., Cohen, J. D., and Katagiri, F. (2010) “A putative RNA-binding protein positively regulates salicylic acid-mediated immunity in Arabidopsis” Mol. Plant-Microbe Interact. 23, 1573-1583.

Ballhorn, D. J., Schmitt, I., Fankhauser, J. D., Katagiri, F., and Pfanz, H. (2011) “CO2 mediated changes of plant traits and their effects on herbivores are determined by leaf age.” Ecol Entomol 36, 1-13.

Wen, Y., Wang, W., Feng, J., Luo, M. C., Tsuda, K., Katagiri, F., Bauchan, G., and Xiao, S. (2011) “Identification and utilization of a sow thistle powdery mildew as a nonhost pathogen to dissect post-invasion resistance mechanisms in Arabidopsis” J Exp Botany 62, 2117-2129.

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.

Wang, L., Tsuda, K., Truman, W., Sato, M., Nguyen, L. V., Katagiri, F., and Glazebrook, J. (2011) “CBP60g and SARD1 play partially redundant, critical roles in salicylic acid signaling” Plant J, Epub ahead of print, doi: 10.1111/j.1365-313X.2011.04655.x.

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, Epub ahead of print, doi:10.1074/jbc.M110.211615.



326 Cargill
1500 Gortner Avenue
St. Paul, MN 55108