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Central to plant survival is the ability to activate immunity upon pathogen perception. Plants deploy immune receptors to recognise specific pathogenderived molecules (effectors) and to trigger defence. These receptors usually recognise a specific effector, but some work in pairs and can detect multiple effectors. The Arabidopsis RRS1-R/RPS4 receptor pair forms an immune complex, conferring recognition of two distinct bacterial effectors, AvrRps4 and PopP2. A paralogous pair linked to RRS1/RPS4, designated as RRS1B/RPS4B, only recognises AvrRps4. My work has revealed that both pairs detect AvrRps4 via an integrated WRKY domain of RRS1 or RRS1B, which mimics the effector’s host targets: the WRKY transcription factors (TF). It has also been shown that the WRKY TF-targeting PopP2 is also perceived by the RRS1-R WRKY domain. Together, we suggest that RRS1 (or RRS1B) with the WRKY domain fusion has evolved to protect defence-regulating WRKY proteins from being attacked by effectors. These integrated domains of immune receptors are becoming popular targets for synthetic resistance engineering. However, one of the biggest challenges is to avoid auto-activity while enabling new recognition capacity when manipulating the integrated domains. To better understand how these receptors operate to convert effector perception into defence activation, I investigated the dynamic molecular interactions in the pre-activation complex, and those that change upon effector perception. I found that RRS1-R/RPS4 complex is negatively regulated by the WRKY domain during pre-activation, and effector-triggered activation is likely mediated by de-repression of the WRKY domain. After effector-triggered RRS1 de-repression, the activation signal is transduced to RPS4. Domain swaps between RRS1-R/RPS4 and RRS1B/RPS4B have revealed the key interaction required for this transduction is between RRS1 domain 4 and the RPS4 C-terminal domain. Furthermore, I discovered possible distinct domain-domain interactions that enable AvrRps4- and PopP2- triggered activation. The mechanistic insights into complex auto-inhibition and activation described in this thesis will prove valuable for many other cooperative immune receptor systems.

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Next-generation sequencing technologies have transformed our understanding of genetic variation segregating in populations and its relationship with phenotypic traits. Sequencing large populations at low coverage, thus sampling only a fraction of the genome of each individual, may increase statistical power in genetic mapping [Pasaniuc,2012] compared to genotyping arrays. This thesis explores several novel applications of low-coverage population-based sequencing, using data from 488 recombinant inbred lines from the MAGIC population of Arabidopsis thaliana, descended from 19 inbred founder accessions. Based on the full catalogue of genetic variation that is available in the 19 founders [Gan, 2011], I describe every MAGIC genome as a mosaic of founder haplotypes and analyse the accuracy of the mosaics by simulation. I then use the mosaics in three ways. First, I investigate structural variation using a novel method that treats anomalies in the alignment of sequencing reads, potentially representing signatures of structural variants (SVs), as quantitative traits. These can be mapped genetically to identify loci in which genetic variation correlates with signatures of SVs. The method can distinguish short- (e.g. indels) and long-range (e.g. translocations) SVs and has led to the discovery of a large number of SVs segregating in the MAGIC population, including thousands of long-range SVs. I show that SVs have a significant impact on silencing gene expression and that they explain a large fraction of the phenotypic variation in several physiological traits. Second, I use the mosaic structure of the MAGIC lines to map recombination events and analyse lineage-specific recombination in MAGIC. I infer recombination hotspots and compared recombination in the MAGIC lines to the Arabidopsis genetic map. Finally, I detect bacterial endosymbionts hosted in MAGIC genomes from unmapped reads that have high sequence similarity with bacterial DNA and examine whether variation in the presence of endosymbionts can be explained by host genetic variation.

Plants are the most abundant biomass on Earth. Understanding plant metabolism represents a significant, fundamental challenge, requiring the incorporation of many fields of study. However it also provides potentially significant leverage with which to change the world in which we live. The model organism Arabidopsis thaliana is probably the single best under- stood plant system. The aim of this thesis is to use mathematical modelling to investigate to what extent existing knowledge can describe broad, emergent aspects of the behaviour of metabolism in this system, with particular respect to the metabolism of sulfur, and other nutrients, and to gain insight into the consequences of the structure of its metabolic network. Constraint-based modelling approaches provide a framework for modelling large reaction networks. Although they require various simplifications, and assump- tions, they provide a route for the understanding of large metabolic networks, which is not possible through other approaches. Here, a genome scale model of Arabidopsis metabolism is developed to reflect experimental data, and deployed in the study of nutrient stress, and nutrient requirements. This model predicts changes in gene expression in response to stress, and provides insight into the consequences of the metabolic structure on nutrient use efficiency, metabolic flexibility, and the consequences of genetic perturbation.

The development of diverse organ shapes involves genetically specified growth patterns which may differ across a tissue in rate and/ or orientation. Understanding specified growth is not intuitive since observed (resultant) growth rates and orientations are the result of specified growth combined with the effects of mechanical constraints in a connected tissue. Growth dynamics in leaves of Arabidopsis have previously been studied experimentally and modelled using a polarity field to orient growth, and regional factors which control local specified growth rates parallel and perpendicular to the polarity. It is unclear whether the mechanisms invoked for the development of 2D leaf shape can be applied to more complex 3D leaf shapes. In this work, I developed Utricularia gibba as a new model system and studied the development of U. gibba 3D epiascidiate (cup-shaped) leaves (known as bladders). I investigated bladder shape changes through development and modelled these transitions using isotropic (equal in all directions) or anisotropic (preferentially in one orientation) specified growth, showing that specified anisotropy is required to generate the full mature bladder shape. The shape of the main body of the bladder could be accounted for by both specified isotropic or anisotropic models. I tested predictions on growth dynamics and polarity made by each model using sector analysis and by investigating markers of tissue cell polarity in bladders. Sector analysis supported an anisotropic specified growth model, while quadrifid gland and UgPIN1 analysis provided evidence of a polarity field in U. gibba. Together, these observations suggest a common underlying mechanism for the generation of 3D and 2D leaves. This work shows how computational modelling can be combined with experimentation in a biological system to allow for a better understanding of the specified growth patterns underlying the generation of an organ shape.