Global temporal dynamic landscape of pathogen-mediated subversion of Arabidopsis innate immunity

Abstract

The universal nature of networks' structural and physical properties across diverse systems offers a better prospect to elucidate the interplay between a system and its environment. In the last decade, several large-scale transcriptome and interactome studies were conducted to understand the complex and dynamic nature of interactions between Arabidopsis and its bacterial pathogen, Pseudomonas syringae pv. tomato DC3000. We took advantage of these publicly available datasets and performed "-omics"-based integrative, and network topology analyses to decipher the transcriptional and protein-protein interaction activities of effector targets. We demonstrated that effector targets exhibit shorter distance to differentially expressed genes (DEGs) and possess increased information centrality. Intriguingly, effector targets are differentially expressed in a sequential manner and make for 1% of the total DEGs at any time point of infection with virulent or defense-inducing DC3000 strains. We revealed that DC3000 significantly alters the expression levels of 71% effector targets and their downstream physical interacting proteins in Arabidopsis interactome. Our integrative "-omics"--based analyses identified dynamic complexes associated with MTI and disease susceptibility. Finally, we discovered five novel plant defense players using a systems biology-fueled top-to-bottom approach and demonstrated immune-related functions for them, further validating the power and resolution of our network analyses.

Figure 1

Measurement of diverse centrality parameters for effector targets. (a and b) Distribution of shortest path for pairs of effector targets and differentially expressed genes (DEGs) (magenta), and non effector targets and DEGs pairs (blue) for DC3000hrpA ⁻ (a) and DC3000 (b). (Chi-Square Test of Independence; P < 2.2 × 10⁻¹⁶). (c) Distribution of information centrality for effector targets (magenta) and non targets (blue) in AI-_MAIN. Frequency of occurrences of information centrality for both categories are depicted. (Student’s t-test P < 0.0001). (d) Average information centrality for effector targets (magenta) and non targets (blue) in AI-MAIN is displayed. (Student’s t-test P < 0.0001).

Figure 2

Temporal regulation of effector targets. (a and b) The histograms illustrate total number of differentially expressed genes (DEGs, grey) and effector targets that are differentially expressed (magenta) at the indicated time points for DC3000hrpA ⁻ (a) and DC3000 (b). Note that the value of total DEGs are recorded 100 times more than DEGs effector targets. (c and d) The histograms of cumulative DEG effectors and first gradient change in transcription for effector targets (DEGs effector target unique) are displayed at the indicated time points for DC3000hrpA ⁻ (c) and DC3000 (d). Up- and down-regulation of genes are shown in black (DEGs effector target cumulative) and red (DEGs effector target unique), white (DEGs effector target cumulative) and green (DEGs effector target unique), respectively.

Figure 3

Visualization of DC3000hrpA ⁻ and DC3000-regulated effector targets. Unique and shared effector targets that are differentially expressed by DC3000hrpA ⁻ and DC3000 in AI-_MAIN are illustrated in the first layer of the network. Second degree targets and remaining proteins make second and third layers in AI-_MAIN, respectively. Non DEGs are removed from AI-_MAIN for clarity.

Figure 4

qRT-PCR analyses of five selected genes upon treatments with diverse stimuli and bacterial pathogens. Accumulation transcripts of the indicated genes is revealed upon treatments with elf18 or control (a), flg22 (b), DC3000 (c), DC3118 (d) and Pseudomonas syringae expressing AvrRpm1 (e). Data represent the mean and standard error of two technical replicates.

Figure 5

Reactive oxygen species (ROS) burst in Arabidopsis leaves triggered by flg22. ROS profile of Col-0, SAIL_747_D04 (AT2G04030), SALK_114949 C (AT1G06460) and SALK_058117 C (AT4G33030) at the indicated time points are shown. Leaf samples were harvested from four-week-old Arabidopsis plants and ROS burst was measured by a luminol-based assay immediately after addition of 1 μM flg22. The data are shown as means ± SEs (standard error) from 8 leaf discs.

Figure 6

At2g04030 (CR88) and At1g06460 (ACD31.1) displayed increased susceptibility to DC3000hrcC ⁻. Growth of DC3000hrcC ⁻ in plants were quantified three days post infiltration (OD_600 nm = 0.0002). Results presented are average ± stand error. n = 5, *p < 0.05, asterisks indicate the difference is significant compared to Col-0 by two-tailed Student’s t-test.