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. 2011 Jul 29;333(6042):596-601.
doi: 10.1126/science.1203659.

Independently evolved virulence effectors converge onto hubs in a plant immune system network

M Shahid Mukhtar  1 , Anne-Ruxandra Carvunis, Matija Dreze, Petra Epple, Jens Steinbrenner, Jonathan Moore, Murat Tasan, Mary Galli, Tong Hao, Marc T Nishimura, Samuel J Pevzner, Susan E Donovan, Lila Ghamsari, Balaji Santhanam, Viviana Romero, Matthew M Poulin, Fana Gebreab, Bryan J Gutierrez, Stanley Tam, Dario Monachello, Mike Boxem, Christopher J Harbort, Nathan McDonald, Lantian Gai, Huaming Chen, Yijian He; European Union Effectoromics Consortium; Jean Vandenhaute, Frederick P Roth, David E Hill, Joseph R Ecker, Marc Vidal, Jim Beynon, Pascal Braun, Jeffery L Dangl
Affiliations

Independently evolved virulence effectors converge onto hubs in a plant immune system network

M Shahid Mukhtar et al. Science. .

Abstract

Plants generate effective responses to infection by recognizing both conserved and variable pathogen-encoded molecules. Pathogens deploy virulence effector proteins into host cells, where they interact physically with host proteins to modulate defense. We generated an interaction network of plant-pathogen effectors from two pathogens spanning the eukaryote-eubacteria divergence, three classes of Arabidopsis immune system proteins, and ~8000 other Arabidopsis proteins. We noted convergence of effectors onto highly interconnected host proteins and indirect, rather than direct, connections between effectors and plant immune receptors. We demonstrated plant immune system functions for 15 of 17 tested host proteins that interact with effectors from both pathogens. Thus, pathogens from different kingdoms deploy independently evolved virulence proteins that interact with a limited set of highly connected cellular hubs to facilitate their diverse life-cycle strategies.

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Figures

Fig. 1
Fig. 1. Construction of a high-quality plant-pathogen immune network, version 1 (PPIN-1)
(A). Experimentally determined plant-pathogen immune network. Proteins (nodes) are color coded as P. syringae effectors (Psy; gold), H. arabidopsidis effectors (Hpa; purple), plant proteins including literature-curated defense proteins (blue), N-terminal domains of NB-LRR immune receptors (red), cytoplasmic domains of LRR-containing receptors like kinase (RLK), a subclass of pattern recognition receptors (pink) and “immune interactors” (grey). Grey edges represent protein-protein interactions. Interactions that are not connected to the network involving Hpa or Psy effectors are indicated next to their relevant protein categories in the first and second layers. Grid at left denotes individual interactions involving immune proteins. (B) PPIN-1 proteins evolve faster than those of AI-1. Distribution of dN/dS ratios computed between Arabidopsis proteins and their Papaya orthologs for all AI-1MAIN proteins and for immune interactors (from A) present in AI-1MAIN. Inset is rescaled on the Y-axis to make the higher dN/dS categories more apparent. The X-axis remains the same for the inset. P-value: Kolmogorov-Smirnov test.
Fig. 2
Fig. 2. Effector proteins converge onto interconnected cellular hubs
(A) Significance of the convergence of effectors onto a limited set of targets. Distribution of the total number of effector targets (left panel) and of the proportion of shared targets (right panel) in 1,000 simulations (10). The red arrows represent the observed number of effector targets in PPIN-1 (left panel; Fig. 1A, Fig. 3A, fig. S7, fig. S8) and the observed proportion of shared targets in PPIN-1 (right panel; Fig. 1A, Fig. 3A, fig. S7 fig. S8,). (B) Significance of the connectivity among effector targets. Distribution of the number of direct connections between effector targets in 15,000 simulations (10). The red arrows represent the observed number of interactions between effector targets in AI-1MAIN. (C) PPIN-1 proteins display a high connectivity in AI-1MAIN. The average degree (number of interactors) in AI-1MAIN of PPIN-1 proteins groups (Fig. 1A) was compared to proteins in AI-1MAIN that are not in PPIN-1. All groups of proteins from PPIN-1 have a significantly higher degree than non-PPIN-1 proteins in AI-1MAIN (**P < 0.0001, Mann-Whitney U-test). Receptors include both NB-LRRs and RLKs. Error bars: standard error of the mean. (D) Five hubs50 are targeted by significantly more effectors than expected given their degree in AI-1MAIN. Each dot represents a hub50 targeted by at least one effector in PPIN-1, graphed as a function of both its degree in AI-1MAIN (X axis) and of the number of interactions it has with effectors in PPIN-1 (Y axis). Dots colored red correspond to hubs50 that are targeted by significantly more effectors than expected given their degree (P < 0.05, empirical P-value from degree preserving random simulations (10)). (E) Relative frequency of degree in AI-1MAIN of: (i) the 632 PPIN-1 proteins present in AI-1MAIN (pink); and (ii) the remaining 2,029 proteins in AI-1MAIN (black). Group (i) shows a significantly higher degree distribution than group (ii); Mann-Whitney U-test (P = 1.9 × 10−103). The vertical line corresponds to a degree of 50.
Fig. 3
Fig. 3. Combinatorial modules in PPIN-1
(A) The PPIN-1 sub-network of pathogen effector proteins and their Arabidopsis targets. Proteins (nodes) are color coded as in (Fig. 1A, fig. S1). Grey edges: experimental interactions from Fig. 1A. Green edges: added interactions from AI-1 and LCI (fig. S2). From the total of 165 effector targets, 105 interact with at least one other target, while 41 and 19 interact only with Hpa or Psy effectors, respectively. (B) Schematic representation of combinatorial modules involving effectors and effector targets in PPIN-1 (data extracted from A). Number of proteins (top) and number of combinatorial modules (bottom) are indicated for each category. (C) Schematic representation of novel combinatorial modules involving immune receptors. The numbers for each category are listed on top.
Fig. 4
Fig. 4. Functional validation of host proteins interacting with effectors from both pathogens
(A) Nine host proteins interacting with effectors from both pathogens are required for full immune system function. 12 day old seedlings were inoculated with the avirulent Hpa isolates Emwa1 (E1) or Emoy2 (E2). Infection classes were defined by the number of asexual sporangiophores (Sp) per cotyledon, determined at 5 days post-inoculation (dpi) and displayed as a heat map from green (more resistant) to red (more susceptible); with the mean number of Emwa1 (black) or Emoy2 (red) sporangiophores per cotyledon noted above each bar. Col-0 and rpp4 are resistant and susceptible controls for both Hpa isolates, respectively. The eds16 mutant is a control for compromised MTI (34). See table S10 for means +/− two times standard error, sample size, additional alleles and independent repetitions. (B) At least six host proteins targeted by effectors from both pathogens are required for maximal pathogen colonization. Experiment as in (A), but using spores from the virulent Hpa isolate Noco2 and counting the number of sporangiophores (Sp) per cotyledon at 4 dpi. Ws and Col-0 represent the resistant and susceptible controls, respectively. For means +/− two times standard error, sample size, additional alleles and independent repetitions see table S10. Seven unrelated mutant lines inoculated with Hpa isolate Emwa1 did not exhibit altered disease resistance (means and +/− two times standard error of the means: Col-0 = 1.3 +/− 0.2; seven mutant lines = 0.8–1.8 +/− 0.3; rpp4 = 16.1 +/− 0.7). (C) The csn5a-2 cul3a double mutant exhibits enhanced resistance to Hpa isolate Emco5. Number of asexual sporangiophores (Sp) was counted at 5 dpi for each of the indicated genotypes. Col-0 and Ler were susceptible and resistant controls, respectively. (D) Bacterial growth (colony forming unit – CFU/cm2, expressed on a log scale) following flg22 (right) or mock treatment (water, left) of leaves of the indicated genotypes followed 24 hours later by infection with Pto DC3000. Bacterial growth was assessed at 3 dpi. Error bars: two standard errors of the mean (n = 4).
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