Integrated Systems View on Networking by Hormones in <i>Arabidopsis</i> Immunity Reveals Multiple Crosstalk for Cytokinin

Naseem, Muhammad; Philippi, Nicole; Hussain, Anwar; Wangorsch, Gaby; Ahmed, Nazeer; Dandekar, Thomas

doi:10.1105/tpc.112.098335

Cited by 106 publications

(113 citation statements)

References 82 publications

(124 reference statements)

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“…The recent growth of computational and systems biology approaches is a welcome development that can help to alleviate these difficulties. Modelling hormonal interaction networks and simulating them in silico will be invaluable in helping us to understand their dynamics, allowing us to formulate better hypotheses and even to predict possible novel interactions and partners (Ibañes et al, 2009;Chickarmane et al, 2012;Naseem et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

Crossing paths: cytokinin signalling and crosstalk

2013

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SummaryCytokinins are a major class of plant hormones that are involved in various aspects of plant development, ranging from organ formation and apical dominance to leaf senescence. Cytokinin and auxin have long been known to interact antagonistically, and more recent studies have shown that cytokinins also interact with other plant hormones to regulate plant development. A growing body of research has begun to elucidate the molecular and genetic underpinnings of this extensive crosstalk. The rich interconnections between the synthesis, perception and transport networks of these plant hormones provide a wide range of opportunities for them to modulate, amplify or buffer one another. Here, we review this exciting and rapidly growing area of cytokinin research.

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Section: Discussionmentioning

confidence: 99%

Crossing paths: cytokinin signalling and crosstalk

2013

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“…A heat shock factor-like TF, TRANSLOCON1-BINDING TRANSCRIPTION FACTOR (TBF1), was recently identified as a major TF that controls transcriptional reprogramming in PTI and ETI to favor defense over normal growth (Pajerowska-Mukhtar et al, 2012). Several pioneering studies explored how robust immunity is achieved in PTI and ETI based on these well-known key immune-related genes (Tsuda et al, 2009;Sato et al, 2010;Naseem et al, 2012;Kim et al, 2014). For example, leveraging a DELAYED DEHISCENCE2 (dde2)/ETHYLENE INSENSITIVE2 (ein2)/PHYTOALEXIN DEFICIENT4 (pad4)/SALICYLIC ACID INDUCTION DEFICIENT2 (sid2)-quadruple Arabidopsis mutant, Tsuda et al (2009) found that immune signaling pathways tend to be synergistic in PTI, but compensatory in ETI.…”

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confidence: 99%

“…high activation) of the other pathways (Sato et al, 2010). To examine hormone crosstalk in ETI and PTI, network modeling was used to analyze a phytohormonecentric regulatory network containing 105 nodes (Naseem et al, 2012). The aforementioned studies have provided invaluable insight into differences in immune regulation.…”

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confidence: 99%

Revealing Shared and Distinct Gene Network Organization in Arabidopsis Immune Responses by Integrative Analysis

et al. 2015

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Pattern-triggered immunity (PTI) and effector-triggered immunity (ETI) are two main plant immune responses to counter pathogen invasion. Genome-wide gene network organizing principles leading to quantitative differences between PTI and ETI have remained elusive. We combined an advanced machine learning method and modular network analysis to systematically characterize the organizing principles of Arabidopsis (Arabidopsis thaliana) PTI and ETI at three network resolutions. At the single network node/ edge level, we ranked genes and gene interactions based on their ability to distinguish immune response from normal growth and successfully identified many immune-related genes associated with PTI and ETI. Topological analysis revealed that the top-ranked gene interactions tend to link network modules. At the subnetwork level, we identified a subnetwork shared by PTI and ETI encompassing 1,159 genes and 1,289 interactions. This subnetwork is enriched in interactions linking network modules and is also a hotspot of attack by pathogen effectors. The subnetwork likely represents a core component in the coordination of multiple biological processes to favor defense over development. Finally, we constructed modular network models for PTI and ETI to explain the quantitative differences in the global network architecture. Our results indicate that the defense modules in ETI are organized into relatively independent structures, explaining the robustness of ETI to genetic mutations and effector attacks. Taken together, the multiscale comparisons of PTI and ETI provide a systems biology perspective on plant immunity and emphasize coordination among network modules to establish a robust immune response.

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“…In principle, a hormonal crosstalk network can be extended to include any hormonal signalling components (Hill et al, 2013;Naseem et al, 2012); kinetic equations can include any degree of complexity in the regulatory relationships Sauro, 2011); and construction of an in silico root map can include any level of detail within a 3-dimensional root structure (Barbier de Reuille et al, 2015, Yoshida et al, 2014. In addition, modelling auxin gradients can also incorporate different mechanisms of polar auxin transport (van Berkel et al, 2013) and the interaction of hormone gradients and root growth (De Rybel et al, 2014;Mahonen et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Modelling Plant Hormone Gradients

Moore

Zhang

Lindsey

2015

Encyclopedia of Life Sciences

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Cellular patterning in the Arabidopsis root is coordinated via a localised auxin concentration maximum in the root tip, requiring the regulated expression of specific genes. The activities of plant hormones such as auxin, ethylene and cytokinin depend on cellular context and exhibit either synergistic or antagonistic interactions. Due to the complexity and nonlinearity of spatiotemporal interactions between both hormones and gene expression in root development, modelling plant hormone gradients requires a systems approach in which experimental data and modelling analysis are closely combined. Modelling therefore allows a predictive interrogation of highly complex and nonintuitive interactions between components in the system. Important factors to be considered when modelling hormone gradients include the construction of a hormonal crosstalk network, the formulation of kinetic equations and the construction of an in silico root map. A modelling approach enables the analysis of relationships between multiple hormone gradients, predictions on how hormone gradients emerge under the action of hormonal crosstalk, and the prediction and elucidation of experimental results from mutant roots. Key Concepts Patterning in Arabidopsis root development is coordinated via a localised auxin concentration maximum in the root tip, requiring the regulated expression of specific genes. The activities of plant hormones such as auxin, ethylene, cytokinin, abscisic acid, gibberellin and brassinosteroids depend on cellular context and exhibit either synergistic or antagonistic interactions. Auxin concentration and the associated regulatory and target genes are regulated by diverse interacting hormones and gene expression and therefore cannot change independently of the various crosstalk components in space and time. Other hormone concentrations, such as ethylene and cytokinin concentrations, and expression of the associated regulatory and target genes are also interlinked. Modelling plant hormone gradients requires a systems approach where experimental data and modelling analysis are closely combined. A hormonal crosstalk network describes the regulatory relationships between hormones and their associated genes. A kinetic equation can be formulated for any regulatory relationship following thermodynamic and kinetic principles. Construction of an in silico root map enables the study of multicellular cell–cell communications in Arabidopsis root development. Modelling hormone gradients enables the analysis of relationships between multiple hormone gradients, predictions on how hormone gradients emerge under the action of hormonal crosstalk, and the prediction and elucidation of experimental results from mutant roots. Modelling auxin gradients can also incorporate different mechanisms of polar auxin transport and the interaction of hormone gradients and root growth.

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

Cited by 106 publications

References 82 publications

Crossing paths: cytokinin signalling and crosstalk

Crossing paths: cytokinin signalling and crosstalk

Revealing Shared and Distinct Gene Network Organization in Arabidopsis Immune Responses by Integrative Analysis

Modelling Plant Hormone Gradients

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