Mechanisms underlying robustness and tunability in a plant immune signaling network

Yungil Kim; Kenichi Tsuda; Daisuke Igarashi; Rachel A Hillmer; Hitoshi Sakakibara; Chad L Myers; Fumiaki Katagiri

doi:10.1016/j.chom.2013.12.002

Mechanisms underlying robustness and tunability in a plant immune signaling network

Cell Host Microbe. 2014 Jan 15;15(1):84-94. doi: 10.1016/j.chom.2013.12.002.

Authors

Yungil Kim¹, Kenichi Tsuda², Daisuke Igarashi³, Rachel A Hillmer⁴, Hitoshi Sakakibara⁵, Chad L Myers⁶, Fumiaki Katagiri⁷

Affiliations

¹ Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA; Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
² Department of Plant Biology, Microbial and Plant Genomics Institute, University of Minnesota, St. Paul, MN 55108, USA; Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research, Cologne 50829, Germany.
³ Department of Plant Biology, Microbial and Plant Genomics Institute, University of Minnesota, St. Paul, MN 55108, USA; Institute for Innovation, Ajinomoto Co., Inc., Kawasaki 210-8681, Japan.
⁴ Department of Plant Biology, Microbial and Plant Genomics Institute, University of Minnesota, St. Paul, MN 55108, USA.
⁵ RIKEN Center for Sustainable Resource Science, Yokohama 230-0045, Japan.
⁶ Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
⁷ Department of Plant Biology, Microbial and Plant Genomics Institute, University of Minnesota, St. Paul, MN 55108, USA. Electronic address: [email protected].

Abstract

The plant immune signaling network needs to be robust against attack from fast-evolving pathogens and tunable to optimize immune responses. We investigated the basis of robustness and tunability in the signaling network controlling pattern-triggered immunity (PTI) in Arabidopsis. A dynamic network model containing four major signaling sectors, the jasmonate, ethylene, phytoalexin-deficient 4, and salicylate sectors, which together govern up to 80% of the PTI levels, was built using data for dynamic sector activities and PTI levels under exhaustive combinatorial sector perturbations. Our regularized multiple regression model had a high level of predictive power and captured known and unexpected signal flows in the network. The sole inhibitory sector in the model, the ethylene sector, contributed centrally to network robustness via its inhibition of the jasmonate sector. The model's multiple input sites linked specific signal input patterns varying in strength and timing to different network response patterns, indicating a mechanism enabling tunability.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Arabidopsis / genetics
Arabidopsis / immunology*
Arabidopsis / metabolism
Arabidopsis Proteins / genetics
Arabidopsis Proteins / immunology*
Arabidopsis Proteins / metabolism
Carboxylic Ester Hydrolases / genetics
Carboxylic Ester Hydrolases / immunology*
Carboxylic Ester Hydrolases / metabolism
Chitosan / immunology
Chitosan / metabolism
Cyclopentanes / immunology*
Cyclopentanes / metabolism
Ethylenes / immunology*
Ethylenes / metabolism
Gene Expression Regulation, Plant / immunology*
Models, Biological
Oxylipins / immunology*
Oxylipins / metabolism
Plant Diseases / genetics
Plant Diseases / immunology*
Plant Immunity
Protein Kinases / genetics
Protein Kinases / immunology
Pseudomonas syringae / growth & development
Regression Analysis
Salicylic Acid / immunology*
Salicylic Acid / metabolism
Signal Transduction

Substances

Arabidopsis Proteins
Cyclopentanes
Ethylenes
Oxylipins
jasmonic acid
Chitosan
ethylene
FRK1 protein, Arabidopsis
Protein Kinases
Carboxylic Ester Hydrolases
PAD4 protein, Arabidopsis
Salicylic Acid

Abstract

Publication types

MeSH terms

Substances

Grants and funding