Decomposition of dynamic transcriptomic responses during effector-triggered immunity reveals conserved responses in two distinct plant cell populations

Xiaotong Liu; Daisuke Igarashi; Rachel A Hillmer; Thomas Stoddard; You Lu; Kenichi Tsuda; Chad L Myers; Fumiaki Katagiri

doi:10.1016/j.xplc.2024.100882

Decomposition of dynamic transcriptomic responses during effector-triggered immunity reveals conserved responses in two distinct plant cell populations

Plant Commun. 2024 Mar 16:100882. doi: 10.1016/j.xplc.2024.100882. Online ahead of print.

Authors

Xiaotong Liu¹, Daisuke Igarashi², Rachel A Hillmer³, Thomas Stoddard³, You Lu³, Kenichi Tsuda⁴, Chad L Myers⁵, Fumiaki Katagiri⁶

Affiliations

¹ Department of Plant and Microbial Biology, University of Minnesota - Twin Cities, St Paul, MN 55108, USA; Department of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, MN 55455, USA; Bioinformatics and Computational Biology Graduate Program, University of Minnesota - Twin Cities, Minneapolis, MN 55455, USA.
² Department of Plant and Microbial Biology, University of Minnesota - Twin Cities, St Paul, MN 55108, USA; Institute for Innovation, Ajinomoto Co., Inc., Kawasaki, Japan.
³ Department of Plant and Microbial Biology, University of Minnesota - Twin Cities, St Paul, MN 55108, USA.
⁴ Department of Plant and Microbial Biology, University of Minnesota - Twin Cities, St Paul, MN 55108, USA; State Key Laboratory of Agricultural Microbiology, Hubei Hongshan Laboratory, Hubei Key Lab of Plant Pathology, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
⁵ Department of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, MN 55455, USA; Bioinformatics and Computational Biology Graduate Program, University of Minnesota - Twin Cities, Minneapolis, MN 55455, USA.
⁶ Department of Plant and Microbial Biology, University of Minnesota - Twin Cities, St Paul, MN 55108, USA; Bioinformatics and Computational Biology Graduate Program, University of Minnesota - Twin Cities, Minneapolis, MN 55455, USA. Electronic address: katagiri@umn.edu.

PMID: 38486453
DOI: 10.1016/j.xplc.2024.100882

Abstract

Rapid plant immune responses in the appropriate cells are needed for effective defense against pathogens. Although transcriptome analysis is often used to describe overall immune responses, collection of transcriptome data with sufficient resolution in both space and time is challenging. We reanalyzed public Arabidopsis time-course transcriptome data obtained after low-dose inoculation with a Pseudomonas syringae strain expressing the effector AvrRpt2, which induces effector-triggered immunity in Arabidopsis. Double-peak time-course patterns are prevalent among thousands of upregulated genes. We implemented a multi-compartment modeling approach to decompose the double-peak pattern into two single-peak patterns for each gene. The decomposed peaks reveal an "echoing" pattern: the peak times of the first and second peaks correlate well across most upregulated genes. We demonstrated that the two peaks likely represent responses of two distinct cell populations that respond either cell autonomously or indirectly to AvrRpt2. Thus, the peak decomposition has extracted spatial information from the time-course data. The echoing pattern also indicates a conserved transcriptome response with different initiation times between the two cell populations despite different elicitor types. A gene set highly overlapping with the conserved gene set is also upregulated with similar kinetics during pattern-triggered immunity. Activation of a WRKY network via different entry-point WRKYs can explain the similar but not identical transcriptome responses elicited by different elicitor types. We discuss potential benefits of the properties of the WRKY activation network as an immune signaling network in light of pressure from rapidly evolving pathogens.

Keywords: Arabidopsis; Pseudomonas syringae; WRKY transcription factors; multi-compartment model; plant immune response; resilient immune signaling network.