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Plant Cell. May 2012; 24(5): 1793–1814.
Published online May 29, 2012. doi:  10.1105/tpc.112.098335
PMCID: PMC3442570

Integrated Systems View on Networking by Hormones in Arabidopsis Immunity Reveals Multiple Crosstalk for Cytokinin[W]

Abstract

Phytohormones signal and combine to maintain the physiological equilibrium in the plant. Pathogens enhance host susceptibility by modulating the hormonal balance of the plant cell. Unlike other plant hormones, the detailed role of cytokinin in plant immunity remains to be fully elucidated. Here, extensive data mining, including of pathogenicity factors, host regulatory proteins, enzymes of hormone biosynthesis, and signaling components, established an integrated signaling network of 105 nodes and 163 edges. Dynamic modeling and system analysis identified multiple cytokinin-mediated regulatory interactions in plant disease networks. This includes specific synergism between cytokinin and salicylic acid pathways and previously undiscovered aspects of antagonism between cytokinin and auxin in plant immunity. Predicted interactions and hormonal effects on plant immunity are confirmed in subsequent experiments with Pseudomonas syringae pv tomato DC3000 and Arabidopsis thaliana. Our dynamic simulation is instrumental in predicting system effects of individual components in complex hormone disease networks and synergism or antagonism between pathways.

INTRODUCTION

Plant pathogen interactions are the consequence of a dynamic coevolution. Mounting resistance on the part of the host is balanced by altered virulence on the part of the pathogen. This monolayer paradigm, known as the gene-to-gene concept, turned into a multiphase zigzag logical model when sufficient data became available on the innate components of plant immunity (Jones and Dangl, 2006). Both pathogen-associated molecular patterns (PAMPs) and effectors are pathogenicity factors, which provoke immune triggers in the host (Schneider and Collmer, 2010). No matter whether these triggers lead to resistance or susceptibility, plant immune networks and the ultimate phenotype (Robert-Seilaniantz et al., 2011a) are often critically determined by small molecules with hormonal properties (salicylic acid [SA], jasmonic acid [JA], auxin, ethylene [ET], gibberellic acid [GA], abscisic acid [ABA], and various cytokinins). With the spotlight more on other phytohormones, the detailed effects of cytokinin in plant immunity in this context remain unclear. By modeling and experiments, we highlight the role of hormone molecules in plant immunity. In particular, we provide a new perspective on auxin-cytokinin antagonism and insights into cytokinin-SA crosstalk. Our validated dynamic model is a starting point in revealing hormonal implications for plant immunity.

In plants, pathogen attack causes hormonal perturbations both at the host-pathogen interface (local response; Melotto et al., 2006) and beyond (systemic response; Mishina and Zeier, 2007). Depending upon the nature of pathogen, either SA-mediated (biotrophs; Glazebrook 2005) or JA/ET-mediated (necrotrophs; Lai et al., 2011) defense pathways are operative in plants (Grant and Jones, 2009). Antagonism of JA to SA and synergism to ET has long been elucidated (reviewed in Robert-Seilaniantz et al., 2011b). ET-based synergism is not limited only to JA; rather, there exists a synergistic crosstalk between pathways of ET and SA as well (Pieterse et al., 2009). Recent investigations show that SA-JA/JA signaling pathways are interconnected, and if nodes are mutated or inhibited, the response is either shifted toward one or the other counterpart (Sato et al., 2010). Growth regulatory hormones, such as auxin, add to JA-mediated responses and suppress the SA pathway (Wang et al., 2007). Likewise, ABA antagonizes SA-dependent defense signaling both upstream and downstream of SA biosynthesis, whereas SA abolishes ABA responses (de Torres Zabala et al., 2009; Cao et al., 2011). On the contrary, GA expedites SA accumulation (Navarro et al., 2008; Alonso-Ramírez et al., 2009) and also promotes resistance against Pseudomonas syringae pv tomato DC3000 (hereafter referred to as Pst) by degrading DELLA proteins (Navarro et al., 2008). Taking this background carefully into account, we established an integrated network of hormonal interactions to perform dynamic simulations on various aspects of plant immunity.

Regarding immunity, it is the collective temporal dynamic of hormones during infection dictated by the ability of the host to mitigate (Bari and Jones, 2009) and the pathogen to propagate (Robert-Seilaniantz et al., 2007) that determines the outcome of the plant–microbe interactions. Successful infections propagate through multiple phases, the simplest deconstruction being entry (Melotto et al., 2008), suppression of basal defense (Boller and He, 2009), reconfiguration of host metabolism (Rico and Preston, 2008), and persistence in an equilibrium related to a metabolic balance between host and pathogen (Rico et al., 2011). However, it is not sufficient to collate a wealth of literature and infer processes from this. We hence advocate and develop a dynamic network model. By virtue of this, the zigzag model of immunity (Jones and Dangl, 2006) can be studied in the context of its hormonal dynamics, including semiquantitative information on activity and the temporal sequence of activated nodes. Moreover, hormonal poise is fine-tuned through various phases of infection, and all of the hormones mentioned above participate in this process. Thus, JA, ABA, ET, and GA are equally relevant in the infection process (Verhage et al., 2010) and can be analyzed within the context of our simulation (see Methods; see Supplemental Figures 1 and 2 and Supplemental Methods 1 online), but their detailed interplay is not our focus here (including, for example, their biochemical production as new output). We intensively analyzed our model with special reference to cytokinin and the impact cytokinin has on various components of plant immunity.

The potential role of cytokinin in plant immunity in general and a link to the SA-JA/ET backbone in particular is still unclear. Apart from their role in tumor and gall formation (Tzfira and Citovsky, 2006; Siemens et al., 2006) and generation of green islands around the infection of fungal biotrophs (Walters and McRoberts, 2006), cytokinin has not been assigned a con clusive role in plant pathogen interaction. Nevertheless, many biotrophic and hemibiotrophic pathogens pattern their in planta cytokinin secreting capability to increase uptake of nutrients and host cell cycle regulation for self-establishment (Walters and McRoberts, 2006; Pertry et al., 2009). Moreover, there are reports about enhanced plant resistance to viral pathogens in the context of increased cytokinin levels of the plant (Clarke et al., 1998; Fayza and Sabrey, 2006). It has been shown that cytokinin promotes resistance in Arabidopsis thaliana to the infection of Alternaria brassicicola as well as Pst (Choi et al., 2010). Moreover, increased resistance to infection by P. syringae pv tabaci in tobacco (Nicotiana tabacum; Grosskinsky et al., 2011) and reduction in the performance of herbivores in poplars (Populus spp; Dervinis et al., 2010) have been associated with higher levels of cytokinin. Recently, Argueso et al. (2012) demonstrated enhanced and reduced susceptibility against infection by Hyaloperonospora arabidopsidis (Hpa Noco 2) in Arabidopsis with lower and higher cytokinin levels, respectively. Therefore, itis plausible that in plant cellular circuitry cytokinin signaling has multiple interaction possibilities and that each interaction has its own dynamics in plant pathogen immune networks with responses that optimize plant defense against the respective pathogen.

Broadly, biological networks are mathematical representations of biological structure where nodes are connected via edges and thus constitute a graph (Albert, 2005). Based on the type of interacting nodes, the following networks can be distinguished: metabolic (Schuster et al., 2000), protein–protein interaction (Li et al., 2006), transcriptional regulation (Sato et al., 2010), and signaling networks (Liu et al., 2010). Depending upon the network, edges are either nondirectional or directed from one node to the other. Edges depict processes, which require time, context, and kinetics to occur (Pritchard and Birch, 2011). Nodes of maximum connectivity are called hubs. They are of different functional types, for instance “party” or “date” hubs accumulating general or specific interactions regarding time and type of interaction (Han et al., 2004) Depending on the exact case, they can be of central importance for network structure as well as biological function (Mukhtar et al., 2011). SA and DELLA proteins are examples of functionally important hub nodes in our network topology. Indicator nodes are densely connected, but unlike a hub, indicator nodes such as pathogenesis-related protein1 (PR-1) have minuscule impact on structural and functional orientation of the network but give an indication of the final outcome of input stimuli.

Network-associated complexity can sometimes be captured with parametric mathematical approaches, such as ordinary differential equation (ODE) models. However, these require detailed kinetic data and other parameters (Wangorsch et al., 2011). Alternatively, parameter-free qualitative approaches, such as Boolean networks, can also model complex dynamic behavior (Ay et al., 2009; Pomerance et al., 2009). “Boolean” refers to dynamic models in which each node is characterized by two qualitative states (often referred to as on or off) (Philippi et al., 2009). Boolean network models have an advantage over ODE-based kinetic models regarding complex networks including immune and pathogen responses (Wittmann et al., 2009). In contrast with ODE models, Boolean network models can also work when kinetic information is scarce and many nodes are involved (Schlatter et al., 2011). SQUAD (Standardized Qualitative Dynamical systems; Di Cara et al., 2007) is a powerful modeling package that combines Boolean and ODE models. This approach is an extension of Boolean modeling. It creates a system of exponential functions that allows interpolation between the step function of Boolean models according to the sum of activating and inhibitory input (Philippi et al., 2009). It allows qualitative modeling of networks with the added possibility of quantitative information. Using standardized qualitative dynamic modeling, we analyzed plant hormone disease networks and performed simulations on pathogen- mediated perturbations in Arabidopsis.

In this study, we use dynamic modeling to better understand the complex network interactions around plant immunity after pathogen attack. We model the interactions following infection by pathogen Pst in host plant Arabidopsis. Modeled responses are consistent with reported experimental data. Predictions are next extended to cytokinin and experimentally validated to show points where cytokinin enhances plant immunity. Further analysis shows that protection mediated by cytokinin is due to its promoting effect on SA signaling as well as inhibiting behavior to auxin. We predict and show experimentally that cytokinin does not influence the early events of recognition between host and pathogen. Furthermore, looking at the established molecular sequence of events, we study the promoting influence of cytokinin on camalexin accumulation. Taken together, a model and experiments highlight in detail the challenged hormonal balance for plant immune defense. This includes specific events around modulation by cytokinin and implications for crop protection. The model is instrumental in elucidating complex hormonal crosstalk in plant pathogen interactions.

RESULTS

Boolean Formalism, Network Topology, and Initial Conditions and Parameters for Model Simulations

Exploring their sequenced genomes, we modeled hormonal attributes of interactions between Arabidopsis and Pst (phenotype is shown in Figure 1A). Known pathogenicity factors of Pst, such as PAMPs and effectors (Boller and He, 2009), and their interactions (Schneider and Collmer, 2010) with cellular components resulted in a network of 105 nodes and 163 edges with logical (Boolean) connections (Figure 1B; see Supplemental Table 1 online). Biosynthetic pathways of phytohormones were integrated with defense signaling, immunologically important receptors, transcription factors, repressor molecules, and degradative complexes. The model was implemented in CellDesigner (Funahashi et al., 2008). We also considered the primary literature for each and every edge and thus defined the nature of interactions between nodes in our network topology (see Supplemental Table 1 online). The behavior of an individual node in our network is defined by a Boolean formalism, where node is specified by a variable x, which denotes its state of activation (x = 1) or inactivation (x = 0). However, the activation of each component x depends on its overall interactions and regulatory relationship within network cross-linking. Logical operators OR ([logical or]), AND (∧), and NOT (¬) in the SQUAD algorithm cumulate the effect of edges: either only activating or only inhibiting or both activating and/or inhibiting (see Methods and Supplemental Methods 1 online).

Figure 1.
Network Topology for Small-Molecule Hormone Disease Networks.

The static representation of the network was converted into a discrete dynamical system for steady state analysis by applying the SQUAD software package and then into a continuous dynamic system in the form of time-dependent ODEs (see Supplemental Figure 1 and Supplemental Methods 1 online). To assess the behavior of nodes across the network, we considered various conditions, such as presence or absence of the pathogen (Pst; Figure 2A), wild-type or mutant nature of Pst (see Supplemental Figure 2 online), presence or absence of a particular hormone (Figure 3A), full and partial activation (see below and Supplemental Figure 3 online), and so forth. Simulation results on the carefully refined network (Figure 1B) well reflected systems behavior according to literature (see Supplemental Table 1 online for nodes and type of interactions; see Supplemental Table 2 online for simulation validation). The SQUAD simulation approximates for this complex dynamic of system responses to stimuli in a simplified way: It looks at system equilibria and its changes. Simulation parameters were adjusted such that input stimuli (changes of equilibrium) were set to be fully active [for the input signal of virulent Pst infection: xt0 (Pst) = 1], while initial states of other nodes (nodei) were set to no activation [being in equilibrium: xt0 (nodei) = 0]. Moreover, SQUAD transforms the original discrete step function into a sigmoid response curve. The magnitude of the sigmoid function depends on the gain (h) and decay (gi) in exponential terms (see Methods and Supplemental Methods 1 online). Having fixed the initial conditions and parameters, a signal is injected as input node in the form of invading pathogen (Pst), elicitors (flagellin or EF-Tu), effector (HopIA1 or AvrRps2), or hormonal stimulus (auxin or cytokinin). The route of the injected signal can be followed through the network gates and then reaches the marker node of PR-1. We defined PR-1 as marker node for the output as it reflects (Mukhtar et al., 2011) the resulting immunological response of the host plant to the invading pathogen (Pst). Wherever appropriate, targeted experiments were performed to support the outcome of modeling simulations. After careful validation (see Supplemental Table 2 online), the established network topology and dynamic model allowed us to predict the impact of specific hormonal stimuli in plant pathogen interactions as detailed in the following.

Figure 2.
Infection Modeling of Virulent Pathogen Pst and Systems Analysis for Components of Plant Immunity.
Figure 3.
Modeling the Impact of Plant Hormones on the Pathogenicity of Pst.

Modeling of the Infection of Pst in Arabidopsis and Systems Analysis for the Components of Plant Immunity

Plant pathogenic bacteria (Pst) have evolved strategies to keep the host susceptible (Nishimura and Dangl, 2010). Whether guarding the cell surface or being used for surveillance inside the plant cell, receptors are instrumental in the recognition of pathogenic PAMPs and effectors (Figure 1B, red). These recognition events evoke changes in system states of the cell. Therefore, we modeled virulent infection of Pst (fully active input node; see network topology in Figure 2A) and determined resulting activation states for individual nodes across the whole network (Figure 2A and see below; see Supplemental Figure 2A online). However, a less virulent pathogen shows substantially lower activation states (see Supplemental Figure 2B online). Furthermore, immunity is the sum total of various components (Jones and Dangl, 2006), such as PAMP-triggered immunity (PTI) and effector-triggered immunity (ETI). To model PTI, we performed SQUAD simulations such that flagellin and EF-Tu (nodes of network topology; flagellin and elongation factor; Figure 1B) being input nodes were given full activation (1.0 as activation value during simulations). This resulted in the activation of NADPH-oxidase, mitogen-activated protein kinases, WRKY transcription factors, miR393 (suppressor of auxin responses; Navarro et al., 2006), PR-1, and closure of stomata (Figure 2B). Moreover, activation (concentration/response) of nodes representing phytohormones, such as SA (Mishina and Zeier, 2007) and ABA (ABA responses; see Melotto et al., 2006; Nishimura and Dangl, 2010; Zeng and He, 2010), as well as activation of DELLA proteins (flagellin treatment leads to the stabilization of DELLA protein complexes; Navarro et al., 2008) is evident in PTI modeling (Figure 2B; see Supplemental Figure 4B online). To model ETI, we assigned full activation to nodes of bacterial effectors (Figure 1B, red), which subsequently resulted in the activation of relevant nodes across the whole network (Figure 2B; see Supplemental Figure 4B online). Network analysis shows extensive overlap (70%; see Supplemental Figure 5 online) between PTI and ETI, whereas system stability analysis (difference in states for ETI and ETI* over time) revealed robustness as well as redundancy in ETI (Figure 2B, middle and bottom panels). Besides inhibiting PTI (see Supplemental Figure 4B online), the modeling suggests that bacterial effectors alter the hormonal profile of the plant cell (Figure 2B; see Supplemental Figure 4A online). Pst modeling results agree with current literature (see Supplemental Table 2 online). These results demonstrate the behavior of the components of plant immunity and their innate properties and quantify their mutual interactions across the hormone disease networks. Moreover, our model envisions the behavior of Pst infection as a source of hormonal modulation in plants.

Small-Molecule Hormones Modulate Plant Immunity: Cytokinin Crosstalk

Our dynamic model for Pst showed activation for nodes of SA, JA, ET, ABA, and auxin, while demonstrating lack of activation for GA and cytokinin (Figure 2A; see Supplemental Figure 4A online). Lack of activation is not equivalent to repression. Repression requires a signal to go from on to off. To model the impact of these hormones on plant immunity, we performed SQUAD simulations by taking individual hormones as input activating node with and without Pst. The activation of PR-1 over time was used as an index of plant immunity. We found in the simulations that the three hormones ET, SA, and GA activate PR-1 in contrast with JA, ABA, and auxin (Figure 3A, left panel). Interestingly, SA and GA further enhance the signal of the activity of PR-1 in the presence of Pst, whereas JA, auxin and ABA diminish even the residual activity of PR-1 manifested by Pst alone (Figure 3A, right panel). This supports the fact that unlike the promoting effect of SA and GA on immunity, auxin, JA, and ABA mediate susceptibility of Arabidopsis against infection by Pst. The simulation data in fact allow us to distinguish whether a signal goes from on to off (repression in an active sense) or whether there is simply lack of activation (see Supplemental Figure 4A online). In most cases, we observed only lack of activation (e.g., mitigated pathogen for most system nodes modeled). We validated these and further outcomes of SQUAD simulations on hormonal stimuli by detailed comparison with published data (see details in Supplemental Table 2 online).

Although implicated both in resistance and susceptibility (Pertry et al., 2009; Argueso et al., 2012), the phytohormone cytokinin seems to have multifaceted signaling roles in plant diseases. Regarding their connection to the SA-JA/ET backbone of plant immunity, Choi et al. (2010) reported positive crosstalk between cytokinin and SA pathway, whereas Argueso et al. (2012) found negative crosstalk among them. However, their interaction with the JA pathway is still not known. Our own observations from both experiments and modeling intrigued us to investigate the specific crosstalk by cytokinin as this seemed to enhance immunity in plants. We further investigated all these observations by setting up and applying our integrated model and can now provide insights into cytokinin-mediated resistance in Arabidopsis to infection with Pst. Modeling infection by a virulent pathogen gave no indication of a direct activation of the node representing cytokinin (Figure 2A; see Supplemental Figure 2A and Supplemental Figure 4A online). However, when set to be fully activated together with Pst as input activating nodes (modeling the increased cytokinin status of the plant during pathogen infection), the SQUAD analysis (besides Pst-based node activations; Figure 2A) resulted in the activation of nodes such as Arabidopsis Histidine Kinases, Arabidopsis homologs of HPt Proteins, Arabidopsis Response Regulators (ARRs), Cytokinin Oxidase, and PR-1 (Figure 3B; see Supplemental Figure 3A online). Subsequent simulations also highlighted that cytokinin (without Pst) alone can cause the activation of PR-1 and hence promote plant immunity (see Supplemental Figure 3B online). To verify modeling predictions, we applied exogenously kinetin solution (10 µM) and observed clearly reduced Pst disease symptoms compared with mock-fed pathogen-infected Arabidopsis leaves (Figure 3C, left panel). Moreover, reduced in planta bacterial multiplication (Figure 3C, right panel) in cytokinin treated versus control leaves nicely confirms the simulation. Regarding the cytotoxic effect of cytokinin on Pst, we demonstrated experimentally that unlike the antibacterial activity of tetracycline, cultures of Pst grew equally well with and without the addition of cytokinin (see Supplemental Figure 6 online). Additionally, feeding of kinetin (without subsequent pathogen infection) solution to detached leaves of Arabidopsis PR1:GUS (for β-glucuronidase) plants showed higher GUS activity compared with mock feeding (see below). These results substantiate the fact that enhanced plant cytokinin levels boost immunity against infection of Pst and that cytokinin has no direct cytotoxic effect on bacterial multiplication.

Cytokinin Overlay with SA in Regulating Genes of Arabidopsis Implicated in Plant Immunity

Cytokinin-mediated protection of Arabidopsis against infection by Pst (Figure 3C) and its activation of PR-1 (see Supplemental Figure 3B online; see below) pinpoint crosstalk between SA and cytokinin pathways. To explore the mechanism of cytokinin-mediated protection, we investigated genome-wide transcriptional overlay between cytokinin and SA and the impact they have on the expression of genes related to immune defense in plants. We analyzed microarray data from the Gene Expression Omnibus (GEO) on hormonal stimuli of SA (GSE3984; Thibaud-Nissen et al., 2006) and cytokinin (GSE 6832; Hitoshi Sakakibara RIKEN Plant Science Center). After normalization and statistical analysis, the up- and downregulated genes of both these hormonal stimuli are shown in a Venn diagram (Figure 4, left side). We found overlap (an indication of cytokinin-SA crosstalk) between SA- and cytokinin-regulated (regarding both up- and downregulated) genes. The overlapping genes are shown in the heat map representation, indicating their expression together with their annotation (Figure 4, right side). Immunity-relevant genes include those encoding glutaredoxins, chitinase, and the well-known marker PR-1 as an integral part of SA-mediated defense (Bari and Jones, 2009). These were found in the gene expression analysis to be mutually upregulated both by SA and cytokinins. Furthermore, our analysis revealed the RPS4 gene involved in resistance to Pst to be downregulated by methyl jasmonate upregulated by both cytokinin and SA. The mRNA for a chitinase-type broad-spectrum mildew resistance protein MLO11, as well as mRNAs for defensin-related genes, were found to be upregulated. Similarly, a couple of Toll/interleukin-1 receptor domain–containing genes were shown to be mutually upregulated by SA and cytokinin. The gene for JAZ1, a positive regulator of SA responses and CONSTITUTIVE DISEASE RESISTANCE1, is also upregulated by both these hormones. Interestingly, more than one and half dozen disease resistance protein genes belonging either to the coiled-coil–nucleotide binding site–leucine-rich repeat or Toll/interleukin-1 receptor–nucleotide binding site–leucine-rich repeat class (Mukhtar et al., 2011) were found to be mutually upregulated by SA and cytokinins.

Figure 4.
Positive Interaction between SA and Cytokinin Signaling and Its Impact on Plant Immunity.

The genes encoding the MYC2 transcription factor, a negative regulator of SA responses (Figure 4, bottom right panel; Laurie-Berry et al., 2006), and MYB28, which gives protection against necrotrophic pathogens (Stotz et al., 2011), are downregulated by cytokinin and SA. Auxin responses promote susceptibility (Navarro et al., 2006) against biotrophic pathogens; one such auxin-responsive protein gene (AT2G37030) and a gene encoding an enzyme of the auxin biosynthetic pathway (Moubayidin et al., 2009) were found to be repressed both by cytokinin and SA. ABA mutants impaired in ABA biosynthesis or sensitivity are thought to be resistant to infection with Pst DC3000 (de Torres Zabala et al., 2009), and ABA1 is mutually downregulated by cytokinin and SA. DELLA proteins are negative regulators of SA-mediated resistance against Pst DC3000 (Navarro et al., 2008), and two DELLA-like genes, RGL3 and RGL2, are found to be repressed both by cytokinin as well as SA. The axr1 Arabidopsis mutant showed increased resistance to Pst (Kazan and Manners, 2009). AXR1 was analyzed and found to be mutually downregulated by SA and cytokinin. Taken together, these data demonstrate that cytokinin-SA positive crosstalk is not limited to a disease marker gene (Choi et al., 2010) but has wide transcriptional overlap.

Modeling the Behavior of Cytokinin Regarding the SA Pathway and Camalexin Accumulation

Conventionally, resistance to (hemi)biotrophic pathogens has often been associated with elevated SA levels coupled to the upregulation of PR-1 in plants (such as tomato [Solanum lycopersicum], Arabidopsis, tobacco; Nawrath and Métraux, 1999). Therefore, we investigated the impact of enhanced cytokinin levels on the accumulation, as well as downstream signaling, of SA. SQUAD simulations for cytokinin (without Pst) as an input node resulted in the activation of PR-1 without activating the node of SA (see Supplemental Figure 3B online). However, when cytokinin was combined with Pst as input activating node, the activation of PR-1 increased beyond their individual effects, while node of SA received a minor (Pst alone 0.28 to Pst + cytokinin 0.32) activation (Figures 2A and and3B;3B; see Supplemental Figure 3A online). Our analysis of free SA levels did not reveal any significant difference for cytokinin- versus mock-treated leaves (Figure 5A). However, early time points (12 h) prior to cytokinin treatment followed by infiltration with Pst resulted in a higher SA accumulation in comparison to mock, as well as cytokinin, treatments. Moreover, the difference in free SA accumulation between treatments of Pst with and without cytokinin was not significant (Figure 5A). We also analyzed the synthesis of camalexin in Arabidopsis Columbia-0 (Col-0) plants and found that cytokinin-treated plants accumulated significantly more camalexin than did control plants (Figure 5B). Enhanced camalexin accumulation upon cytokinin treatment occurred in late time points (72 h after cytokinin application). These results signify that cytokinin does not cause higher SA accumulation to protect Arabidopsis against the infection with Pst and that cytokinin treatment has a positive effect on camalexin accumulation.

Figure 5.
Effects of Cytokinin on the SA Pathway of Resistance in Arabidopsis.

We performed SQUAD simulations to investigate to what degree cytokinin-mediated protection against Pst is independent of the accumulation of SA and to investigate whether the node of interaction is downstream from the synthesis of SA. We used cytokinin as the activating input node in combination with Pst in the presence (+SA; to mimic overaccumulation of SA) and absence (−SA; to mimic deficiency in SA accumulation) of SA, while activation of PR-1 over time was used as the output marker node for the immune response (Figure 5C). Individually, activation of SA in comparison to cytokinin strongly activated PR-1 during infection by Pst, while combined activation of SA, cytokinin, and Pst further augmented the PR-1 signal (Figure 5C, top three rows). Interestingly, deletion of SA from the network (this mimics the state of the sid2 mutant) brought the PR-1 signal back to the level of Pst with cytokinin. However, deletion of TGA3-TF efficiently reduced this signal (Figure 5C). To verify the computational analysis, experiments were performed on sid2 mutants (SA-deficient plants; Wildermuth et al., 2001). Prior to the inoculation with Pst, exogenous application of kinetin to sid2 plants resulted in a less severe disease phenotype in comparison to mock treatment (Figure 5D). Quantification of the protective effect (Figure 5D) of cytokinin on sid2 plants showed that this happens only at a low initial pathogen load (see below). Thus, both modeling and experimental data substantiate that the interaction between cytokinin and SA is down the node of SA biosynthesis, while higher SA levels only fortify the cytokinin-mediated system state with better protection against infection with Pst.

Modeling the Impact of Cytokinin on PTI

Cytokinin augments PTI in Arabidopsis by enhancing callose deposition upon flagellin treatment (Choi et al., 2010). Moreover, cytokinin upregulates PR-1, which is regarded as a downstream event (Mishina and Zeier, 2007) in PAMP–Pattern Recognition Receptor interaction. However, it is still unknown if cytokinin influences early events of pathogen recognition (Zipfel et al., 2006) in plants. To model the impact cytokinin has on early events of PAMP recognition, we performed SQUAD simulation while taking flagellin as input activation node in the presence and absence of cytokinin as well as cytokinin alone as input stimuli. Our dynamic model contains all relevant nodes and models the time evolution of stimulatory input regarding the whole network. We can thus predict and distinguish early versus late events in the activation as well as differences in activation by flagellin and/or cytokinin and resulting responses for any other network node of interest. Figure 6A shows for the aforementioned input stimuli the state of key nodes representing PR-1, NADPH-Oxi, and reactive oxygen species (ROS) with regard to their direct relevance to recognition events (Panstruga et al., 2009). Cytokinin alone does not cause the activation of NADPH-Oxi and ROS but activates marker node PR-1 (Figure 6A). Furthermore, together with flagellin, cytokinin changes the activation state of the PR-1 marker node without influencing the states of NADPH-Oxi and ROS (Figure 6A). Moreover, the corresponding calculated detailed activation states over time for the impact of cytokinin on flagellin-mediated ROS production are shown in Supplemental Data Set 1 online.

Figure 6.
The Impact Cytokinin Has on PTI and Early Events of Recognition.

To validate these simulations regarding the impact of cytokinin on early events of flagellin/PAMP recognition (Figure 6A), we performed experiments on flagellin elicitation of ROS production in Arabidopsis leaf fragments. Upon treatment with 10 nM flagellin, leaf fragments produced ROS within 3 min, reached a maximum level, and then returned back to the initial state (Figure 6B). Addition of t-zeatin (1 µM) did not change the flagellin-mediated amplitude of ROS production (Figure 6B) in Arabidopsis leaf fragments. To get further experimental insight, we performed pH experiments (Kunze et al., 2004) on Arabidopsis cell suspension cultures and showed that [increment]pH (change in medium alkalinization) for flagellin did not change with the addition of t-zeatin. Furthermore, we did not notice any elicitation effect of t-zeatin alone on the cultures used in such experiments (Figure 6C). These experiments were repeated with various types and concentrations of cytokinin and similar results were obtained (data not shown). Taken together, both modeling and experiments underpin the importance of cytokinin in promoting PTI but show that they seem not to influence the early (PAMP–Pattern Recognition Receptor) events of pathogen recognition via ROS production and medium alkalinization.

Contrasting Behavior of Auxin and Cytokinin in Plant Immunity: Transcriptional and Physiological Evidence

Pst DC3000 injects its effector weaponry and hormonal mimicry to render the host more susceptible (Pieterse et al., 2009). Compromised virulence occurs when Pst is kept deprived of both these tools (Thilmony et al., 2006). Our virulent infection model revealed that during the course of infection, Pst causes the activation of auxin concentration/response but not that of cytokinin (Figure 2A; see Supplemental Figure 4A and Supplemental Data Set 2 online). A Pst mutant (deficient in Type three secretion system) grew well in cytokinin-deficient (35S:Cytokinin Oxidase4,) plants compared with higher cytokinin–producing (35S:IPT3) and wild-type Col-0 Arabidopsis plants (Choi et al., 2010). We therefore hypothesized that virulent and avirulent strains of Pst differentially manipulate auxin and cytokinin pathways in plants. The mutated pathogen could be considered to represent a mitigated infection, and we compared our modeling predictions with the gene expression data. To validate this notion at the transcriptional level, we retrieved raw but complete (genome-wide) microarray data on the infection of the Pst wild type (Pst DC3000) and Pst coronatine and hp mutant (Pst Cor hp; Thilmony et al., 2006) in Arabidopsis from the GEO database (ID GSE5520; Thilmony et al., 2006) (Figure 7A). Among the significantly regulated genes, we focused only on genes related to the biosynthesis, signaling, transport, or degradation of auxin or cytokinin.

Figure 7.
Signaling of Plant Hormones Auxin and Cytokinin during the Course of Infection by Pst in Arabidopsis.

The auxin pathway genes downregulated upon the infection with Pst included auxin transporters (PIN3, PIN4, PIN7, AUX1, LAX1, and LAX3) auxin repressors (AUX/IAAs), and small auxin upregulated (SAUR) genes. However, upon the infection with the mutant pathogen Pst Cor hp, the above genes were either slightly upregulated or remained unchanged (Figure 7A). The upregulated genes included those that convert amino acid conjugates of indole-3-acetic acid (IAA) to free IAA (e.g., genes encoding IAA–amino acid hydrolases [ILR1, ILL5, and ILL6] and genes for IAA-amido synthases [DFL2, GH3.3, and GH3.12]). In contrast with wild-type infection, infection with the mutant Pst strain caused either repression of these genes or left them unchanged. Genes implicated in the biosynthesis of IAA, such as IAA18 and NIT3, are derepressed upon the infection with Pst (Figure 7A). Genes that are negative regulators (Muller and Sheen, 2007) of cytokinin signaling, such as type A response regulators (ARR4, 5, 6, 7, 9, 15, and 16), are repressed during the infection with Pst. However, the contrary is the case for the Pst Cor hp pathogen in terms of expression. Moreover, the IPT3 gene responsible for cytokinin biosynthesis is downregulated by Pst and not regulated by the mutant bacterial counterpart (Figure 7A). In contrast with the infection by Pst Cor hp, we find that genes encoding enzymes of cytokinin degradation are upregulated by wild-type Pst in Arabidopsis (Figure 7A). Taken together, during infection, Pst promotes auxin accumulation by upregulating biosynthesis and deconjugation enzymes genes, as well as by repressing transporters. Auxin signaling is enhanced by the repression of auxin repressors. On the other hand, repressing a gene encoding an enzyme of cytokinin biosynthesis and derepression of the genes for cytokinin degradation enzymes reduces the levels of cytokinin upon pathogen infection.

We next studied the impact of the infection of Pst on Arabidopsis promoter-reporter lines (AtDR5:GUS and AtARR5:GUS), designed to show responses/concentrations of auxins and cytokinin, respectively (Siemens et al., 2006; Liu et al., 2010). Upon inoculation of AtDR5:GUS plants with Pst, we found substantially stronger GUS activity at the host-pathogen interface, followed by faint staining in the surrounding tissue of the same leaf and compared this to mock-inoculated leaves (Figure 7B). Pst-inoculated leaves of the AtARR5:GUS line manifested low GUS activity at the site of infection, while mock-inoculated leaves showed residual background staining (Figure 7B). We determined the accumulation of auxin and cytokinin with gas chromatography–mass spectrometry and found significantly higher free IAA levels at the site of infection of Pst compared with the control (Figure 7C). On the other hand, the level of t-zeatin at the site of infection was significantly lower than in mock-inoculated leaves (Figure 7C). These experiments complement the virulent pathogen infection modeling (Figure 2A) and gene expression with Pst infection in Arabidopsis (Figure 7A) and substantiate that both elevated auxin and repressed cytokinin are part of the infection process that Pst mediates in Arabidopsis.

SQUAD simulation provided further insight into the contrasting behavior of auxin and cytokinin in plant immunity. Auxin together with Pst as input activating nodes resulted in the reduction of PR-1 signal in comparison to Pst alone (Figure 3A). The simulation predicted reduced immunity upon the application of auxin (Figure 7D). Prior to Pst inoculation, 1-naphthaleneacetic acid (NAA)–fed (10 μM) Col-0 Arabidopsis leaves manifested severely enhanced symptoms compared with corresponding controls (Figure 7E and see below). This substantiates that auxin promotes susceptibility of Arabidopsis to Pst, and cytokinin does not have this effect (Figure 3C). By virtue of their positive crosstalk to SA (down the node of synthesis) cytokinin promotes resistance against the infection with Pst (Figures 5C and 5D). To study how auxin influences the SA pathway of resistance, we modeled this interaction with SQUAD simulation. We monitored the activation of the marker node PR-1 in the presence of pathogen as well as modulated (partial or full activation) conditions of SA versus auxin. Together with Pst, the signal for PR-1 activation was higher when SA was given full activation and auxin was kept either partially active or inactive (Figure 7D, middle panel). Decay in the PR-1 signal resulted when SA was either partially or completely inactivated while auxin was given full activation (Figure 7D, right panel). This implies that a decrease in the level of plant SA and increase in auxins further enhance vulnerability. To support this, we demonstrated that sid2 mutants exhibited severe Pst disease phenotypes when given exogenous NAA (Figure 5D). These results demonstrate that unlike cytokinin, which promotes the SA sector of immunity, auxin counterregulates SA responses in favor of susceptibility to infection by Pst in Arabidopsis.

Auxin–Cytokinin Interaction: A New Perspective of Antagonism in Plant Immunity

Auxin and cytokinin have long been known to interact in growth and development of the plant (Liu et al., 2010). Furthermore, auxin promotes Arabidopsis susceptibility to infection with Pst by its positive and negative crosstalk with JA and SA, respectively (Navarro et al., 2006; Chen et al., 2007; Wang et al., 2007; Llorente et al., 2008). On the other hand, cytokinin promotes plant immunity against Pst (Figure 3). With the spotlight on their individual roles, the combined effect of auxin and cytokinin in plant immunity is still not known. To address this more closely, we modeled the combined effects of auxin and cytokinin on plant immunity. To mimic modulation in levels of auxin and cytokinin, we chose various input activation values (partial and full activation) for nodes of auxin and cytokinin without Pst (Pst is also capable of increasing in planta auxin concentrations; Figures 7B and 7C) while performing SQUAD simulations. Partially and fully activated nodes of auxin and cytokinin as a combined input signal did change system states across the whole network (see Supplemental Data Set 3 online). Furthermore, we monitored states of marker node PR-1 as an indicator of immunity status and presented it as trajectory over time for each hormonal combination (Figure 8A). Input activations of higher (1.0 as input activation) auxin and lower (0.2 as input activation) cytokinin resulted in low PR-1 activity. When this combination was reversed, PR-1 gained more activation (Figure 8A). Full to partial input activation of auxin resulted in low PR-1 activity, whereas partial to full input activation of cytokinin resulted in an increase in the state of PR-1 (Figure 8A). Furthermore, using SQUAD simulation, we determined values of PR-1 activation for combination of auxin and cytokinin at various input activating values (ranging from 0 to 1). Three-dimensional visualization of the data provided an overview showing that a relative shift of activation from auxin to cytokinin resulted in the activation of PR-1, whereas the reverse is observed when the trend of activation was directed toward auxin (Figure 8A, bottom panel). More precisely, immunity is compromised when auxin supersedes the activation of cytokinin, and immunity is achieved when cytokinin attains higher activation than auxin.

Figure 8.
The Interaction between Auxin and Cytokinin Modulates Plant Immunity.

Pst inoculation enhances de novo auxin synthesis in Arabidopsis and reduces cytokinin levels at the site of infection (Figures 7B and 7C). However, to demonstrate the impact of interaction between auxin and cytokinin on plant immunity, we performed experiments (exogenous application of hormones) on PR1:GUS Arabidopsis transgenic plants. PR1:GUS plants were induced (via petiole feeding of hormones) with kinetin and NAA alone, as well as in combination without inoculation of Pst. Following hormonal induction, leaves were subjected to histochemical GUS staining. Kinetin-treated PR1:GUS leaves showed higher GUS activity, while NAA treatment alone did not induce any GUS activity (Figure 8B). However, the combination of kinetin and NAA manifested low GUS activity compared with kinetin alone. Taken together, our model and experimental data substantiate that a balance between auxin and cytokinin modulates plant immunity against infection with Pst in Arabidopsis.

To confirm that a balance between auxin and cytokinin affects plant immunity during infection of Arabidopsis by Pst, we next performed simulations using auxin and cytokinin (in various input activations) as input nodes in the presence of Pst. Activation of PR-1 as function of time (arbitrary units) is shown by a heat map (Figure 9A). In this simulation, the combination of auxin and Pst (auxin together with Pst) resulted in decay of the PR-1 signal generated by Pst alone (Figure 9A, top three rows). However, together with Pst, cytokinin increased the signal of PR-1. Full activation of both auxin and cytokinin in the presence of Pst as input nodes diminished the signal of PR-1 activation entirely. However, in the presence of Pst, the signal was regained when auxin was kept partially activated and cytokinin was used as a fully active input node (Figure 9A, bottom and middle rows). Experimentally, exogenous application of kinetin led to significantly reduced in planta bacterial growth, whereas NAA enhanced the sensitivity of the plant to infection with Pst. When administered exogenously, the combination of both auxin and cytokinin led to intermediate bacterial growth in comparison to auxin and cytokinin alone (Figure 9B). These results suggest that the balance between auxin and cytokinin modulates in planta Pst multiplication in Arabidopsis.

Figure 9.
Antagonistic Interaction between Auxin and Cytokinin and Its Impact on Infection by Pst in Arabidopsis.

We tested how the interaction between auxin and cytokinin influences the SA pathway of resistance against infection with Pst. Simulations were conducted in the presence (+SA; enhanced levels) and absence (−SA; mimic SA biosynthesis mutants) of SA together with different auxin and cytokinin combinations, as well as Pst as activating input nodes. We analyzed further a modified network in which the node of SA was completely deleted (to mimic a sid2 mutant). In the presence of Pst, the activation of auxin as input node abolished the signal of PR-1 (Figure 9C). However, the activity of PR-1 was regained when auxin was replaced with cytokinin. Experimentally, we found higher bacterial growth in sid2 mutant plants when treated with NAA compared with nontreated and kinetin-treated plants (Figure 9D). Intriguingly, application of auxin and cytokinin in combination on the sid2 mutant resulted in intermediate bacterial growth in comparison to the individual application of auxin or cytokinin (Figure 9D). These results substantiate that fine-tuning between auxin and cytokinin has an impact on the SA pathway of resistance against infection with Pst.

DISCUSSION

Networking of small-molecule hormones is a newly emerging aspect of plant immunity where crosstalk between growth regulators and stress-specific hormones modulate plant pathogen interactions. Pathogen infection profoundly alters the hormonal profile and network dynamics of the plant cell (Grant and Jones, 2009). Therefore, dynamic modeling is required for better understanding of hormonal implications in plant immunity. However, kinetic data on complex hormonal interactions are scarce. A solution is standardized qualitative dynamic modeling in which connectivity (Sankar et al., 2011) is emphasized more than kinetics. Our modeling illustrates the impact of integration between bacterial effectors and host hormonal cues and sampling over the whole network and documents the eventual outcome for the host-pathogen system. Cytokinin is among the most important, but immunologically least deciphered, plant hormones. With modeling and targeted experiments, we have now identified previously undiscovered crosstalk in cytokinin signaling.

Pst infection modeling and subsequent systems stability analysis revealed profound overlap between PTI and ETI. However, redundancy and robustness was exclusively found in ETI (Figure 2B; see Supplemental Figures 5 and 7 online). Robustness in ETI restricts incompatibilities but allows compatible pathogens (Tsuda et al., 2009; Nishimura and Dangl, 2010) to establish susceptibility of the host. Moreover, Type three secretion system–deficient mutants failed to mount an immune response equivalent to that of virulence and effector-deficient mutants (see Supplemental Figure 5 online) and were therefore subject to suboptimal multiplication (Hauck et al., 2003). Such mutants fail to breach the first line of defense (PTI; Figure 2B; see Supplemental Figure 4B online), which is otherwise circumvented by effectors of the virulence system (see Supplemental Figure 4B online) in favor of optimal multiplication. Our modeling envisions zigzag immune responses (Jones and Dangl, 2006) in dynamic terms and provides relative quantification to various components of plant immunity.

Regarding the first line of defense, SQUAD modeling highlighted the promoting effect of cytokinin on PTI (Figure 6A). However, the early events of recognition (ROS production and media alkalinization; Zipfel et al., 2006) seem to be cytokinin independent. It remains to be elucidated if cytokinin influences PTI by modulating nitric oxide signaling. However, during the advanced phase of the infection, repression of the cytokinin response/concentration (Figure 7) at the host-pathogen interface seems to be a virulence strategy Pst has adopted for optimal in planta bacterial multiplication. Pst, unlike other biotrophic pathogens (Walters and McRoberts, 2006; Pertry et al., 2009), does not produce cytokinin during infection. Cytokinin response reporter lines (AtARR5:GUS) demonstrate the repression of host cytokinin during pathogen infection in detail (Figure 7B). Additionally, the sequenced genome of Pst (http://www.pseudomonas-syringae.org/pst_DC3000_gen.htm) contains no genes related to biosynthesis or degradation of cytokinin otherwise present in Arabidopsis, Agrobacterium tumefaciens, Pseudomonas savastanoi, and Ralstonia fascians, as well as other plant growth-promoting bacteria. The lack of increase in cytokinin concentration/response (measurements in Figures 7B and 7C) during the course of infection suggests the absence of potential effectors in Pst that would otherwise enhance the level/response of cytokinin in the host.

Modeling the impact of phytohormones on Pst infection revealed that JA, auxin, and ABA reduced Pst-mediated PR-1 activation and promoted susceptibility. SA and GA further increased PR-1 activation and enhanced resistance (Figure 3A). Predicted node switching and model outputs are supported in detail according to current literature (see Supplemental Table 2 online). Modeling of increased plant cytokinin with/without the addition of Pst revealed a boosting effect on plant immunity (Figures 3B and and9A;9A; see Supplemental Figure 3B online). Experiments support this by revealing reduced disease symptoms with abrogated in planta bacterial multiplication (Figure 3C) and higher expression of PR-1 (Figure 8B) upon cytokinin treatment. Moreover, increased cytokinin levels enhanced resistance in transgenic plants to Pst (Choi et al., 2010) and Pst tabaci (Grosskinsky et al., 2011), whereas decreased cytokinin level reduced the expression of PR-1 (Uchida and Tasaka, 2010). We took up the challenge to create a dynamic model of these various influences. Thus, modulation in plant cytokinin changes infection dynamics such that an increase in cytokinin favors protection, while its decline adds to vulnerability. Argueso et al. (2012) found that comparatively low exogenous cytokinin application lead to higher susceptibility against infection with Hpa Noco2 in Arabidopsis. However, (1) unlike Hpa, Pst reduces the level of in planta cytokinin at the site of infection (Figure 7C); (2) Pst weakens cytokinin responses (Figures 7A and 7B) to avoid cytokinin-mediated defense (Choi et al., 2010) for self-multiplication; and (3) in contrast with infection with Hpa Noco2, where cytokinin operates upstream (EDS16) of SA production, cytokinin-mediated protection against Pst operates downstream (TGA3) of the SA synthesis (Figure 5D); (4) finally, Hpa Noco2 uses type A response regulators to suppress SA-mediated defense responses, whereas Pst infection downregulates A type ARRs (Figure 7A; Thilmony et al., 2006), and in the presence of cytokinin, B-type ARR (ARR2) is implicated in immunity (Choi et al., 2010). Trophic nature, in planta cytokinin secreting capabilities, host cell inoculation, and multiplication are features that further differentiate Hpa Noco2 from Pst. We thus argue that infection dynamics of Hpa Noco2 and Pst regarding cytokinin responses are out of phase and, hence, not directly comparable.

Cytokinin-mediated increases in the expression of marker gene PR-1 (Figure 8B) made SA-cytokinin positive crosstalk quite plausible. However, our simulations on the mode of interaction between SA and cytokinin (Figures 4 and and9C)9C) revealed important biological inferences. SQUAD modeling and subsequent experimental analysis on cytokinin-treated Arabidopsis leaves demonstrated no significant increase for the accumulation of SA (Figures 3B and and5A).5A). By contrast, without pathogen inoculation, cytokinin treatment led to significantly higher camalexin accumulation in treated versus control leaves (Figure 5B). These results prompted experiments on SA-deficient mutants for the underlying SA-cytokinin crosstalk. However, the literature is mired with contrasting conclusions in this regard. Cytokinin-treated, Pst-inoculated NahG Arabidopsis plants (Choi et al., 2010) failed to show rescued susceptibility. By contrast, Pst tabaci–inoculated prior cytokinin-treated NahG tobacco (Grosskinsky et al., 2011) plants showed a completely rescued susceptible disease phenotype. We analyzed this dichotomy with SQUAD simulations and found that in Arabidopsis, interaction between SA and cytokinin positively influenced plant immunity against Pst (Figure 5C). However, deletion of SA in the presence of cytokinin slightly affected the activity of PR-1, whereas deletion of TGA substantially reduced this activity (Figure 5C). In corresponding experiments, we partially restored resistance against Pst by the application of exogenous cytokinin to sid2 mutants (Figures 5D and and9D).9D). This seems to be in contrast with a previous finding where prior cytokinin treatment could not prevent the severe disease phenotype in NahG plants (Choi et al., 2010). This may be due to the nature of the mutant we investigated. In addition, according to our results, the system response differs according to pathogen load. At low Pst density, cytokinin treatment does prevent the severe phenotype. Enhanced pathogen resistance by elevated cytokinin is thus partly a consequence of direct crosstalk between SA and cytokinin. However, SA biosynthesis adds up to better protection by enhancing system states already in place (Figure 5C), and cytokinin-driven phytolaxin (Figure 5B) accumulation and weakening of auxin responses (Figures 8 and and9C)9C) are additional factors contributing to immunity in Arabidopsis against Pst.

The Pst infection model determined activation states for nodes representing hormonal stimuli. In contrast with the lack of activation found for cytokinin, auxin was activated over time (Figure 2A; see Supplemental Figure 4A online). Analysis of large-scale gene expression data on Pst infection in Arabidopsis also mirrored the notion of repression and derepression for cytokinin and auxin responses, respectively (Figure 7A). Moreover, Pst infection experiments on response regulators of auxin and cytokinin reporter lines (Figure 7B) nicely complemented the infection modeling. Significantly higher auxin and reduced cytokinin accumulation at the host-pathogen interface due to the infection with Pst (Figure 7C) further supported the outcome of experiments on reporter lines. These different and complementary approaches adequately highlighted the activation of auxin and inhibition of cytokinin as a consequence of infection with Pst in Arabidopsis. However, SQUAD simulations and subsequent experimental analysis support that both auxin and cytokinin change the infection dynamics of Pst (Figures 5C, 5D, ,7D,7D, and and7E).7E). Cytokinin increased protection, whereas auxin enhanced susceptibility of Arabidopsis to infection by Pst. Our findings are in agreement with previous findings. Increased endogenous cytokinin (35S:IPT; Choi et al., 2010) restricted Pst multiplication, whereas enhanced auxin levels (Wang et al., 2007; Crabill et al., 2010) in Arabidopsis led to enhanced sensitivity to infection with Pst. However, rather than investigating the role of auxin (Wang et al., 2007) and cytokinin (Choi et al., 2010) as independent events in plant immunity, we captured them in concert and substantiated that activation of the former with concomitant inhibition of the latter combine to create optimal infection conditions for Pst in Arabidopsis.

Crosstalk between auxin and cytokinin is quite prevalent in biological processes related to growth and development of plants (Moubayidin et al., 2009; Liu et al., 2010), but the literature is scarce regarding its impact on plant immunity. SQUAD modeling and subsequent experimental demonstration elucidated the interplay between auxin and cytokinin and revealed that immunity and susceptibility are a carefully controlled balance (Figure 8A). States of activation of cytokinin over auxin divert the trend toward resistance, and the opposite holds true for susceptibility (Figure 8A). Experimentally, in comparison to auxin, addition of cytokinin alone resulted in higher PR-1 expression, while their combination minimized this effect (Figure 8B). Moreover, higher auxin levels led to severe disease symptoms coupled with fast bacterial growth (Figures 7E and and9B).9B). Auxin and cytokinin fed to Arabidopsis leaves in combination and in concentrations as above resulted in an intermediate state of response (Figure 9B). Intermediate bacterial growth resulted from the combined effect of cytokinin and auxin in sid2 mutants (Figure 9D). Mutual regulation between auxin and cytokinin from the perspective of host-pathogen interaction has not been explored so far. The antagonistic effect of auxin on cytokinin levels has already been investigated for morphogenesis (Nordström et al., 2004). However, via type B response regulators, cytokinin decreases auxin responses by inducing auxin repressor protein (AUX/IAA; Moubayidin et al., 2009). Cytokinin also affects auxin flux and gradients by decreasing the level of auxin efflux carriers (PIN1) through a transcription-independent mechanism (Stepanova and Alonso, 2011). Taken together for the host-pathogen system, our modeling and experimental analysis substantiates an antagonistic interaction between auxin and cytokinin. Stronger resistance is impaired by auxin and increased sensitivity is diminished by cytokinin. Likely mechanisms to promote resistance involve (1) cytokinin stabilizing the auxin repressors AUX/IAA through positive interaction with SA and (2) cytokinin influencing transport of auxin away from the site of infection via auxin transporters. Likewise, auxins might promote (1) degradation of cytokinin through oxidation by their activation enzymes or (2) conversion of cytokinin to inactive storage forms. Molecular mechanisms that influence these interactions need detailed further experimentation and more resolution.

Tiny and simply structured yet complex in cell signaling, cytokinin is actively involved in vital processes, such as cell division, tissue differentiation, lateral bud growth, photosynthetic activity, and floral development (Kudo et al., 2010). They thus have immense potential in enhancement of crop yield. By virtue of their implications in delaying aging and retaining plant chlorophyll (Gan and Amasino, 1995) cytokinin is instrumental in increasing crop shelf life, reduction of post-harvest losses, and thus ensuring food security. Elevated cytokinin promotes antioxidative forces, keeps the cellular redox potential high, and protects plants from abiotic environmental extremes (Rivero et al., 2007). Adding further to this list, our systems biology–led experiments and modeling suggests crosstalk possibilities for cytokinin in complex hormonal disease networks with implications for new yield protection strategies in agriculture.

METHODS

General Network Setup

Data mining and an extensive literature survey established the plant hormone disease network with all undisputed key nodes (see Supplemental Table 1 online) and inhibitory and/or activatory edges (type of interactions, references, and evaluation; see Supplemental Tables 1 and 2 online). The network was implemented in CellDesigner version 3.5.1 and stored in SBML format.

Modeling Strategy

The SQUAD suite (Di Cara et al., 2007) performed steady state analyses and dynamic simulations (see Supplemental Figure 1 online), recognizing nodes and interacting edges automatically. For the discrete dynamic analysis, SQUAD uses generic assumptions: The presence of any one of the activators can effectively activate a target node, and the presence of any one of the inhibitors can successfully inactivate the target node. For more complex scenarios, different inputs are each considered and integrated according to the network topology (see details in Supplemental Methods 1 online; in particular, equation 2). Thereby, SQUAD identifies the number of stable steady states located in the network. Furthermore, it calculates activity values of each node at a given steady state. SQUAD simulations and steady state analyses highlight system equilibria and their changes upon signaling stimuli (pathogen and/or hormonal signals). The activity value of each node was set to zero (trivial steady state) as a starting point for continuous dynamic simulations to bring the network at equilibrium. To monitor the global system response semiquantitatively toward pathogen or hormonal stimuli, an input signal of the system was initially set to 1. To study the mutual impact of two or more stimuli, we simultaneously set their initial activation to 1. For partial activation, input stimuli were initialized with values between 0 and 1. SQUAD converted the effect of these perturbations into time-resolved activity curves (sigmoid) for each and every node of the network which then was visualized by means of heat maps and trajectory plots. Supplemental Methods 1 online shows the exact equations and resulting curve shape used for this and integration of activating as well as inhibitory input (or both) according to logical connectivity including partial activation of nodes. Supplemental Data Set 4 online shows for illustration the detailed modeling of auxin and cytokinin during the infection of Pst DC3000 in Arabidopsis thaliana (see also results above). For validation, this can be compared with Supplemental Data Set 5 online, for measured gene expression values of auxin and cytokinin pathways during infection of Arabidopsis by Pst DC3000 versus mock (see also results above).

To asses the robustness of the network, we randomly added and removed nodes from the network and performed simulations as detailed above (see Supplemental Figure 7 online).

Analysis of Microarray Data

We analyzed original submitter-supplied microarray data in the GEO database using the interactive Web tool GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r/). This allows users to compare two or more samples within the GEO submitted microarray series. GEO2R uses Bioconductor R packages GEOquery (Sean and Meltzer, 2007) and limma (Linear Models for Microarray Analysis; Smyth, 2004). GEOquery parses GEO submitted array data into R data structures, while limma applies multiple testing corrections on P values to correct the occurrence of false positives and identifies differentially (P value ≤ 0.05) expressed genes. We entered GEO accession numbers of the above microarray experiments and then defined groups as treated and control with given number of replicates found in GEO. We calculated the distribution of the values for selected samples via value distribution option in GEO2R and graphically presented them as box plots to view their suitability for comparison. For multiple-testing corrections, the Benjamini and Hochberg false discovery rate method (Benjamini and Hochberg, 1995) was applied. GEO2R provided limma-generated statistical analysis of the data, such as adjusted and raw P values, t and B values, as well as fold changes.

Plant Pathogen Experiments

Four- to five-week-old Arabidopsis thaliana Col-0 (see http://www.Arabidopsis.org/) and sid2 mutant plants were used in experiments (Raacke et al., 2006). Plants were grown in soil with 9 to 16 h of light and periods of 5 weeks in a growth chamber. The bacterial strain Pseudomonas syringae pv tomato DC3000 was cultured in King's broth medium containing 50 mg/L rifampicin. Harvested cells were resuspended in 10 mM MgCl2. Bacterial inoculations were performed by syringe infiltration. Furthermore, bacterial growth was quantified through a plate counting method. Arabidopsis Col-0 and sid2 mutants were sprayed with kinetin and/or NAA solutions. For pathogen inoculation, the spray method was also used. PR-1:GUS plants (Gust et al., 2007) were also investigated. GUS staining and subsequent destaining were performed according to standard procedures. Free SA and phytoalexin were analyzed according to Mishina and Zeier (2006), nad t-zeatin and IAA were analyzed as described by Hussain et al. (2010). ROS and pH experiments were performed as described by Ahmed (2010).

Supplemental Data

The following materials are available in the online version of this article.

Supplementary Material

Supplemental Data:

Acknowledgments

We thank the German Research Council (Deutsche Forschungsgemeinshaft: Da 208/10-2; 12-1), Bundesministerium für Bildung und Forschung (0315395B), and Land Bavaria for funding.

AUTHOR CONTRIBUTIONS

M.N. and T.D. conceived and planned the project. M.N. and N.P. set up simulations and M.N., N.P., G.W., and T.D. analyzed them. M.N. (main), A.H., and N.A. performed experiments for verification. T.D. supervised and guided the study. All authors wrote the article and approved the final version.

Notes

Glossary
PAMP
pathogen-associated molecular pattern
SA
salicylic acid
JA
jasmonic acid
ET
ethylene
ABA
abscisic acid
Pst
Pseudomonas syringae pv tomato
ODE
ordinary differential equation
PTI
PAMP-triggered immunity
ETI
effector-triggered immunity
ARR
to be defined
GUS
β-glucuronidase
GEO
Gene Expression Omnibus
Col-0
Columbia-0
ROS
reactive oxygen species
IAA
indole-3-acetic acid
NAA
to be defined
DPPI
days postpathogen inoculation

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