Spore Density Determines Infection Strategy by the Plant Pathogenic Fungus Plectosphaerella cucumerina

Necrotrophic and biotrophic pathogens are resisted by different plant defenses. While necrotrophic pathogens are sensitive to jasmonic acid (JA)-dependent resistance, biotrophic pathogens are resisted by salicylic acid (SA)- and reactive oxygen species (ROS)-dependent resistance. Although many pathogens switch from biotrophy to necrotrophy during infection, little is known about the signals triggering this transition. This study is based on the observation that the early colonization pattern and symptom development by the ascomycete pathogen Plectosphaerella cucumerina (P. cucumerina) vary between inoculation methods. Using the Arabidopsis (Arabidopsis thaliana) defense response as a proxy for infection strategy, we examined whether P. cucumerina alternates between hemibiotrophic and necrotrophic lifestyles, depending on initial spore density and distribution on the leaf surface. Untargeted metabolome analysis revealed profound differences in metabolic defense signatures upon different inoculation methods. Quantification of JA and SA, marker gene expression, and cell death confirmed that infection from high spore densities activates JA-dependent defenses with excessive cell death, while infection from low spore densities induces SA-dependent defenses with lower levels of cell death. Phenotyping of Arabidopsis mutants in JA, SA, and ROS signaling confirmed that P. cucumerina is differentially resisted by JA- and SA/ROS-dependent defenses, depending on initial spore density and distribution on the leaf. Furthermore, in situ staining for early callose deposition at the infection sites revealed that necrotrophy by P. cucumerina is associated with elevated host defense. We conclude that P. cucumerina adapts to early-acting plant defenses by switching from a hemibiotrophic to a necrotrophic infection program, thereby gaining an advantage of immunity-related cell death in the host.Plant pathogens are often classified as necrotrophic or biotrophic, depending on their infection strategy (Glazebrook, 2005; Nishimura and Dangl, 2010). Necrotrophic pathogens kill living host cells and use the decayed plant tissue as a substrate to colonize the plant, whereas biotrophic pathogens parasitize living plant cells by employing effector molecules that suppress the host immune system (Pel and Pieterse, 2013). Despite this binary classification, the majority of pathogenic microbes employ a hemibiotrophic infection strategy, which is characterized by an initial biotrophic phase followed by a necrotrophic infection strategy at later stages of infection (Perfect and Green, 2001). The pathogenic fungi Magnaporthe grisea, Sclerotinia sclerotiorum, and Mycosphaerella graminicola, the oomycete Phytophthora infestans, and the bacterial pathogen Pseudomonas syringae are examples of hemibiotrophic plant pathogens (Perfect and Green, 2001; Koeck et al., 2011; van Kan et al., 2014; Kabbage et al., 2015).Despite considerable progress in our understanding of plant resistance to necrotrophic and biotrophic pathogens (Glazebrook, 2005; Mengiste, 2012; Lai and Mengiste, 2013), recent debate highlights the dynamic and complex interplay between plant-pathogenic microbes and their hosts, which is raising concerns about the use of infection strategies as a static tool to classify plant pathogens. For instance, the fungal genus Botrytis is often labeled as an archetypal necrotroph, even though there is evidence that it can behave as an endophytic fungus with a biotrophic lifestyle (van Kan et al., 2014). The rice blast fungus Magnaporthe oryzae, which is often classified as a hemibiotrophic leaf pathogen (Perfect and Green, 2001; Koeck et al., 2011), can adopt a purely biotrophic lifestyle when infecting root tissues (Marcel et al., 2010). It remains unclear which signals are responsible for the switch from biotrophy to necrotrophy and whether these signals rely solely on the physiological state of the pathogen, or whether host-derived signals play a role as well (Kabbage et al., 2015).The plant hormones salicylic acid (SA) and jasmonic acid (JA) play a central role in the activation of plant defenses (Glazebrook, 2005; Pieterse et al., 2009, 2012). The first evidence that biotrophic and necrotrophic pathogens are resisted by different immune responses came from Thomma et al. (1998), who demonstrated that Arabidopsis (Arabidopsis thaliana) genotypes impaired in SA signaling show enhanced susceptibility to the biotrophic pathogen Hyaloperonospora arabidopsidis (formerly known as Peronospora parastitica), while JA-insensitive genotypes were more susceptible to the necrotrophic fungus Alternaria brassicicola. In subsequent years, the differential effectiveness of SA- and JA-dependent defense mechanisms has been confirmed in different plant-pathogen interactions, while additional plant hormones, such as ethylene, abscisic acid (ABA), auxins, and cytokinins, have emerged as regulators of SA- and JA-dependent defenses (Bari and Jones, 2009; Cao et al., 2011; Pieterse et al., 2012). Moreover, SA- and JA-dependent defense pathways have been shown to act antagonistically on each other, which allows plants to prioritize an appropriate defense response to attack by biotrophic pathogens, necrotrophic pathogens, or herbivores (Koornneef and Pieterse, 2008; Pieterse et al., 2009; Verhage et al., 2010).In addition to plant hormones, reactive oxygen species (ROS) play an important regulatory role in plant defenses (Torres et al., 2006; Lehmann et al., 2015). Within minutes after the perception of pathogen-associated molecular patterns, NADPH oxidases and apoplastic peroxidases generate early ROS bursts (Torres et al., 2002; Daudi et al., 2012; O’Brien et al., 2012), which activate downstream defense signaling cascades (Apel and Hirt, 2004; Torres et al., 2006; Miller et al., 2009; Mittler et al., 2011; Lehmann et al., 2015). ROS play an important regulatory role in the deposition of callose (Luna et al., 2011; Pastor et al., 2013) and can also stimulate SA-dependent defenses (Chaouch et al., 2010; Yun and Chen, 2011; Wang et al., 2014; Mammarella et al., 2015). However, the spread of SA-induced apoptosis during hyperstimulation of the plant immune system is contained by the ROS-generating NADPH oxidase RBOHD (Torres et al., 2005), presumably to allow for the sufficient generation of SA-dependent defense signals from living cells that are adjacent to apoptotic cells. Nitric oxide (NO) plays an additional role in the regulation of SA/ROS-dependent defense (Trapet et al., 2015). This gaseous molecule can stimulate ROS production and cell death in the absence of SA while preventing excessive ROS production at high cellular SA levels via S-nitrosylation of RBOHD (Yun et al., 2011). Recently, it was shown that pathogen-induced accumulation of NO and ROS promotes the production of azelaic acid, a lipid derivative that primes distal plants for SA-dependent defenses (Wang et al., 2014). Hence, NO, ROS, and SA are intertwined in a complex regulatory network to mount local and systemic resistance against biotrophic pathogens. Interestingly, pathogens with a necrotrophic lifestyle can benefit from ROS/SA-dependent defenses and associated cell death (Govrin and Levine, 2000). For instance, Kabbage et al. (2013) demonstrated that S. sclerotiorum utilizes oxalic acid to repress oxidative defense signaling during initial biotrophic colonization, but it stimulates apoptosis at later stages to advance necrotrophic colonization. Moreover, SA-induced repression of JA-dependent resistance not only benefits necrotrophic pathogens but also hemibiotrophic pathogens after having switched from biotrophy to necrotrophy (Glazebrook, 2005; Pieterse et al., 2009, 2012).Plectosphaerella cucumerina ((P. cucumerina, anamorph Plectosporum tabacinum) anamorph Plectosporum tabacinum) is a filamentous ascomycete fungus that can survive saprophytically in soil by decomposing plant material (Palm et al., 1995). The fungus can cause sudden death and blight disease in a variety of crops (Chen et al., 1999; Harrington et al., 2000). Because P. cucumerina can infect Arabidopsis leaves, the P. cucumerina-Arabidopsis interaction has emerged as a popular model system in which to study plant defense reactions to necrotrophic fungi (Berrocal-Lobo et al., 2002; Ton and Mauch-Mani, 2004; Carlucci et al., 2012; Ramos et al., 2013). Various studies have shown that Arabidopsis deploys a wide range of inducible defense strategies against P. cucumerina, including JA-, SA-, ABA-, and auxin-dependent defenses, glucosinolates (Tierens et al., 2001; Sánchez-Vallet et al., 2010; Gamir et al., 2014; Pastor et al., 2014), callose deposition (García-Andrade et al., 2011; Gamir et al., 2012, 2014; Sánchez-Vallet et al., 2012), and ROS (Tierens et al., 2002; Sánchez-Vallet et al., 2010; Barna et al., 2012; Gamir et al., 2012, 2014; Pastor et al., 2014). Recent metabolomics studies have revealed large-scale metabolic changes in P. cucumerina-infected Arabidopsis, presumably to mobilize chemical defenses (Sánchez-Vallet et al., 2010; Gamir et al., 2014; Pastor et al., 2014). Furthermore, various chemical agents have been reported to induce resistance against P. cucumerina. These chemicals include β-amino-butyric acid, which primes callose deposition and SA-dependent defenses, benzothiadiazole (BTH or Bion; Görlach et al., 1996; Ton and Mauch-Mani, 2004), which activates SA-related defenses (Lawton et al., 1996; Ton and Mauch-Mani, 2004; Gamir et al., 2014; Luna et al., 2014), JA (Ton and Mauch-Mani, 2004), and ABA, which primes ROS and callose deposition (Ton and Mauch-Mani, 2004; Pastor et al., 2013). However, among all these studies, there is increasing controversy about the exact signaling pathways and defense responses contributing to plant resistance against P. cucumerina. While it is clear that JA and ethylene contribute to basal resistance against the fungus, the exact roles of SA, ABA, and ROS in P. cucumerina resistance vary between studies (Thomma et al., 1998; Ton and Mauch-Mani, 2004; Sánchez-Vallet et al., 2012; Gamir et al., 2014).This study is based on the observation that the disease phenotype during P. cucumerina infection differs according to the inoculation method used. We provide evidence that the fungus follows a hemibiotrophic infection strategy when infecting from relatively low spore densities on the leaf surface. By contrast, when challenged by localized host defense to relatively high spore densities, the fungus switches to a necrotrophic infection program. Our study has uncovered a novel strategy by which plant-pathogenic fungi can take advantage of the early immune response in the host plant.     

Multiscale Metabolic Modeling: Dynamic Flux Balance Analysis on a Whole-Plant Scale

Plant metabolism is characterized by a unique complexity on the cellular, tissue, and organ levels. On a whole-plant scale, changing source and sink relations accompanying plant development add another level of complexity to metabolism. With the aim of achieving a spatiotemporal resolution of source-sink interactions in crop plant metabolism, a multiscale metabolic modeling (MMM) approach was applied that integrates static organ-specific models with a whole-plant dynamic model. Allowing for a dynamic flux balance analysis on a whole-plant scale, the MMM approach was used to decipher the metabolic behavior of source and sink organs during the generative phase of the barley (Hordeum vulgare) plant. It reveals a sink-to-source shift of the barley stem caused by the senescence-related decrease in leaf source capacity, which is not sufficient to meet the nutrient requirements of sink organs such as the growing seed. The MMM platform represents a novel approach for the in silico analysis of metabolism on a whole-plant level, allowing for a systemic, spatiotemporally resolved understanding of metabolic processes involved in carbon partitioning, thus providing a novel tool for studying yield stability and crop improvement.Plants are of vital significance as a source of food (Grusak and DellaPenna, 1999; Rogalski and Carrer, 2011), feed (Lu et al., 2011), energy (Tilman et al., 2006; Parmar et al., 2011), and feedstocks for the chemical industry (Metzger and Bornscheuer, 2006; Kinghorn et al., 2011). Given the close connection between plant metabolism and the usability of plant products, there is a growing interest in understanding and predicting the behavior and regulation of plant metabolic processes. In order to increase crop quality and yield, there is a need for methods guiding the rational redesign of the plant metabolic network (Schwender, 2009).Mathematical modeling of plant metabolism offers new approaches to understand, predict, and modify complex plant metabolic processes. In plant research, the issue of metabolic modeling is constantly gaining attention, and different modeling approaches applied to plant metabolism exist, ranging from highly detailed quantitative to less complex qualitative approaches (for review, see Giersch, 2000; Morgan and Rhodes, 2002; Poolman et al., 2004; Rios-Estepa and Lange, 2007).A widely used modeling approach is flux balance analysis (FBA), which allows the prediction of metabolic capabilities and steady-state fluxes under different environmental and genetic backgrounds using (non)linear optimization (Orth et al., 2010). Assuming steady-state conditions, FBA has the advantage of not requiring the knowledge of kinetic parameters and, therefore, can be applied to model detailed, large-scale systems. In recent years, the FBA approach has been applied to several different plant species, such as maize (Zea mays; Dal’Molin et al., 2010; Saha et al., 2011), barley (Hordeum vulgare; Grafahrend-Belau et al., 2009b; Melkus et al., 2011; Rolletschek et al., 2011), rice (Oryza sativa; Lakshmanan et al., 2013), Arabidopsis (Arabidopsis thaliana; Poolman et al., 2009; de Oliveira Dal’Molin et al., 2010; Radrich et al., 2010; Williams et al., 2010; Mintz-Oron et al., 2012; Cheung et al., 2013), and rapeseed (Brassica napus; Hay and Schwender, 2011a, 2011b; Pilalis et al., 2011), as well as algae (Boyle and Morgan, 2009; Cogne et al., 2011; Dal’Molin et al., 2011) and photoautotrophic bacteria (Knoop et al., 2010; Montagud et al., 2010; Boyle and Morgan, 2011). These models have been used to study different aspects of metabolism, including the prediction of optimal metabolic yields and energy efficiencies (Dal’Molin et al., 2010; Boyle and Morgan, 2011), changes in flux under different environmental and genetic backgrounds (Grafahrend-Belau et al., 2009b; Dal’Molin et al., 2010; Melkus et al., 2011), and nonintuitive metabolic pathways that merit subsequent experimental investigations (Poolman et al., 2009; Knoop et al., 2010; Rolletschek et al., 2011). Although FBA of plant metabolic models was shown to be capable of reproducing experimentally determined flux distributions (Williams et al., 2010; Hay and Schwender, 2011b) and generating new insights into metabolic behavior, capacities, and efficiencies (Sweetlove and Ratcliffe, 2011), challenges remain to advance the utility and predictive power of the models.Given that many plant metabolic functions are based on interactions between different subcellular compartments, cell types, tissues, and organs, the reconstruction of organ-specific models and the integration of these models into interacting multiorgan and/or whole-plant models is a prerequisite to get insight into complex plant metabolic processes organized on a whole-plant scale (e.g. source-sink interactions). Almost all FBA models of plant metabolism are restricted to one cell type (Boyle and Morgan, 2009; Knoop et al., 2010; Montagud et al., 2010; Cogne et al., 2011; Dal’Molin et al., 2011), one tissue or one organ (Grafahrend-Belau et al., 2009b; Hay and Schwender, 2011a, 2011b; Pilalis et al., 2011; Mintz-Oron et al., 2012), and only one model exists taking into account the interaction between two cell types by specifying the interaction between mesophyll and bundle sheath cells in C4 photosynthesis (Dal’Molin et al., 2010). So far, no model representing metabolism at the whole-plant scale exists.Considering whole-plant metabolism raises the problem of taking into account temporal and environmental changes in metabolism during plant development and growth. Although classical static FBA is unable to predict the dynamics of metabolic processes, as the network analysis is based on steady-state solutions, time-dependent processes can be taken into account by extending the classical static FBA to a dynamic flux balance analysis (dFBA), as proposed by Mahadevan et al. (2002). The static (SOA) and dynamic optimization approaches introduced in this work provide a framework for analyzing the transience of metabolism by integrating kinetic expressions to dynamically constrain exchange fluxes. Due to the requirement of knowing or estimating a large number of kinetic parameters, so far dFBA has only been applied to a plant metabolic model once, to study the photosynthetic metabolism in the chloroplasts of C3 plants by a simplified model of five biochemical reactions (Luo et al., 2009). Integrating a dynamic model into a static FBA model is an alternative approach to perform dFBA.In this study, a multiscale metabolic modeling (MMM) approach was applied with the aim of achieving a spatiotemporal resolution of cereal crop plant metabolism. To provide a framework for the in silico analysis of the metabolic dynamics of barley on a whole-plant scale, the MMM approach integrates a static multiorgan FBA model and a dynamic whole-plant multiscale functional plant model (FPM) to perform dFBA. The performance of the novel whole-plant MMM approach was tested by studying source-sink interactions during the seed developmental phase of barley plants.     

Focus Issue on Calcium Signaling: Identification of Cyclic GMP-Activated Nonselective Ca2+-Permeable Cation Channels and Associated CNGC5 and CNGC6 Genes in Arabidopsis Guard Cells

Cytosolic Ca²⁺ in guard cells plays an important role in stomatal movement responses to environmental stimuli. These cytosolic Ca²⁺ increases result from Ca²⁺ influx through Ca²⁺-permeable channels in the plasma membrane and Ca²⁺ release from intracellular organelles in guard cells. However, the genes encoding defined plasma membrane Ca²⁺-permeable channel activity remain unknown in guard cells and, with some exceptions, largely unknown in higher plant cells. Here, we report the identification of two Arabidopsis (Arabidopsis thaliana) cation channel genes, CNGC5 and CNGC6, that are highly expressed in guard cells. Cytosolic application of cyclic GMP (cGMP) and extracellularly applied membrane-permeable 8-Bromoguanosine 3′,5′-cyclic monophosphate-cGMP both activated hyperpolarization-induced inward-conducting currents in wild-type guard cells using Mg²⁺ as the main charge carrier. The cGMP-activated currents were strongly blocked by lanthanum and gadolinium and also conducted Ba²⁺, Ca²⁺, and Na⁺ ions. cngc5 cngc6 double mutant guard cells exhibited dramatically impaired cGMP-activated currents. In contrast, mutations in CNGC1, CNGC2, and CNGC20 did not disrupt these cGMP-activated currents. The yellow fluorescent protein-CNGC5 and yellow fluorescent protein-CNGC6 proteins localize in the cell periphery. Cyclic AMP activated modest inward currents in both wild-type and cngc5cngc6 mutant guard cells. Moreover, cngc5 cngc6 double mutant guard cells exhibited functional abscisic acid (ABA)-activated hyperpolarization-dependent Ca²⁺-permeable cation channel currents, intact ABA-induced stomatal closing responses, and whole-plant stomatal conductance responses to darkness and changes in CO₂ concentration. Furthermore, cGMP-activated currents remained intact in the growth controlled by abscisic acid2 and abscisic acid insensitive1 mutants. This research demonstrates that the CNGC5 and CNGC6 genes encode unique cGMP-activated nonselective Ca²⁺-permeable cation channels in the plasma membrane of Arabidopsis guard cells.Plants lose water via transpiration and take in CO₂ for photosynthesis through stomatal pores. Each stomatal pore is surrounded by two guard cells, and stomatal movements are driven by the change of turgor pressure in guard cells. The intracellular second messenger Ca²⁺ functions in guard cell signal transduction (Schroeder and Hagiwara, 1989; McAinsh et al., 1990; Webb et al., 1996; Grabov and Blatt, 1998; Allen et al., 1999; MacRobbie, 2000; Mori et al., 2006; Young et al., 2006; Siegel et al., 2009; Chen et al., 2010; Hubbard et al., 2012). Plasma membrane ion channel activity and gene expression in guard cells are finely regulated by the intracellular free calcium concentration ([Ca2+]_cyt; Schroeder and Hagiwara, 1989; Webb et al., 2001; Allen et al., 2002; Siegel et al., 2009; Kim et al., 2010; Stange et al., 2010). Ca²⁺-dependent protein kinases (CPKs) function as targets of the cytosolic Ca²⁺ signal, and several members of the CPK family have been shown to function in stimulus-induced stomatal closing, including the Arabidopsis (Arabidopsis thaliana) CPK3, CPK4, CPK6, CPK10, and CPK11 proteins (Mori et al., 2006; Zhu et al., 2007; Zou et al., 2010; Brandt et al., 2012; Hubbard et al., 2012). Further research found that several CPKs could activate the S-type anion channel SLAC1 in Xenopus laevis oocytes, including CPK21, CPK23, and CPK6 (Geiger et al., 2010; Brandt et al., 2012). At the same time, the Ca²⁺-independent protein kinase Open Stomata1 mediates stomatal closing and activates the S-type anion channel SLAC1 (Mustilli et al., 2002; Yoshida et al., 2002; Geiger et al., 2009; Lee et al., 2009; Xue et al., 2011), indicating that both Ca²⁺-dependent and Ca²⁺-independent pathways function in guard cells.Multiple essential factors of guard cell abscisic acid (ABA) signal transduction function in the regulation of Ca²⁺-permeable channels and [Ca2+]_cyt elevations, including Abscisic Acid Insensitive1 (ABI1), ABI2, Enhanced Response to Abscisic Acid1 (ERA1), the NADPH oxidases AtrbohD and AtrbohF, the Guard Cell Hydrogen Peroxide-Resistant1 (GHR1) receptor kinase, as well as the Ca²⁺-activated CPK6 protein kinase (Pei et al., 1998; Allen et al., 1999, 2002; Kwak et al., 2003; Miao et al., 2006; Mori et al., 2006; Hua et al., 2012). [Ca2+]_cyt increases result from both Ca²⁺ release from intracellular Ca²⁺ stores (McAinsh et al., 1992) and Ca²⁺ influx across the plasma membrane (Hamilton et al., 2000; Pei et al., 2000; Murata et al., 2001; Kwak et al., 2003; Hua et al., 2012). Electrophysiological analyses have characterized nonselective Ca²⁺-permeable channel activity in the plasma membrane of guard cells (Schroeder and Hagiwara, 1990; Hamilton et al., 2000; Pei et al., 2000; Murata et al., 2001; Köhler and Blatt, 2002; Miao et al., 2006; Mori et al., 2006; Suh et al., 2007; Vahisalu et al., 2008; Hua et al., 2012). However, the genetic identities of Ca²⁺-permeable channels in the plasma membrane of guard cells have remained unknown despite over two decades of research on these channel activities.The Arabidopsis genome includes 20 genes encoding cyclic nucleotide-gated channel (CNGC) homologs and 20 genes encoding homologs to animal Glu receptor channels (Lacombe et al., 2001; Kaplan et al., 2007; Ward et al., 2009), which have been proposed to function in plant cells as cation channels (Schuurink et al., 1998; Arazi et al., 1999; Köhler et al., 1999). Recent research has demonstrated functions of specific Glu receptor channels in mediating Ca²⁺ channel activity (Michard et al., 2011; Vincill et al., 2012). Previous studies have shown cAMP activation of nonselective cation currents in guard cells (Lemtiri-Chlieh and Berkowitz, 2004; Ali et al., 2007). However, only a few studies have shown the disappearance of a defined plasma membrane Ca²⁺ channel activity in plants upon mutation of candidate Ca²⁺ channel genes (Ali et al., 2007; Michard et al., 2011; Laohavisit et al., 2012; Vincill et al., 2012). Some CNGCs have been found to be involved in cation nutrient intake, including monovalent cation intake (Guo et al., 2010; Caballero et al., 2012), salt tolerance (Guo et al., 2008; Kugler et al., 2009), programmed cell death and pathogen responses (Clough et al., 2000; Balagué et al., 2003; Urquhart et al., 2007; Abdel-Hamid et al., 2013), thermal sensing (Finka et al., 2012; Gao et al., 2012), and pollen tube growth (Chang et al., 2007; Frietsch et al., 2007; Tunc-Ozdemir et al., 2013a, 2013b). Direct in vivo disappearance of Ca²⁺ channel activity in cngc disruption mutants has been demonstrated in only a few cases thus far (Ali et al., 2007; Gao et al., 2012). In this research, we show that CNGC5 and CNGC6 are required for a cyclic GMP (cGMP)-activated nonselective Ca²⁺-permeable cation channel activity in the plasma membrane of Arabidopsis guard cells.     

Disruption of Fumarylacetoacetate Hydrolase Causes Spontaneous Cell Death under Short-Day Conditions in Arabidopsis

Fumarylacetoacetate hydrolase (FAH) hydrolyzes fumarylacetoacetate to fumarate and acetoacetate, the final step in the tyrosine (Tyr) degradation pathway that is essential to animals. Deficiency of FAH in animals results in an inborn lethal disorder. However, the role for the Tyr degradation pathway in plants remains to be elucidated. In this study, we isolated an Arabidopsis (Arabidopsis thaliana) short-day sensitive cell death1 (sscd1) mutant that displays a spontaneous cell death phenotype under short-day conditions. The SSCD1 gene was cloned via a map-based cloning approach and found to encode an Arabidopsis putative FAH. The spontaneous cell death phenotype of the sscd1 mutant was completely eliminated by further knockout of the gene encoding the putative homogentisate dioxygenase, which catalyzes homogentisate into maleylacetoacetate (the antepenultimate step) in the Tyr degradation pathway. Furthermore, treatment of Arabidopsis wild-type seedlings with succinylacetone, an abnormal metabolite caused by loss of FAH in the Tyr degradation pathway, mimicked the sscd1 cell death phenotype. These results demonstrate that disruption of FAH leads to cell death in Arabidopsis and suggest that the Tyr degradation pathway is essential for plant survival under short-day conditions.Programmed cell death (PCD) has been defined as a sequence of genetically regulated events that lead to the elimination of specific cells, tissues, or whole organs (Lockshin and Zakeri, 2004). In plants, PCD is essential for developmental processes and defense responses (Dangl et al., 1996; Greenberg, 1996; Durrant et al., 2007). One well-characterized example of plant PCD is the hypersensitive response occurring during incompatible plant-pathogen interactions (Lam, 2004), which results in cell death to form visible lesions at the site of infection by an avirulent pathogen and consequently limits the pathogen spread (Morel and Dangl, 1997).To date, a large number of mutants that display spontaneous cell death lesions have been identified in barley (Hordeum vulgare), maize (Zea mays), rice (Oryza sativa), and Arabidopsis (Arabidopsis thaliana; Marchetti et al., 1983; Wolter et al., 1993; Dietrich et al., 1994; Gray et al., 1997). Because lesions form in the absence of pathogen infection, these mutants have been collectively termed as lesion-mimic mutants. Many genes with regulatory roles in PCD and defense responses, including LESION SIMULATING DISEASE1, ACCELERATED CELL DEATH11, and VASCULAR ASSOCIATED DEATH1, have been cloned and characterized (Dietrich et al., 1997; Brodersen et al., 2002; Lorrain et al., 2004).The appearance of spontaneous cell death lesions in some lesion-mimic mutants is dependent on photoperiod. For example, the Arabidopsis mutant lesion simulating disease1 and myoinositol-1-phosphate synthase1 show lesions under long days (LD; Dietrich et al., 1994; Meng et al., 2009), whereas the lesion simulating disease2, lesion initiation1, enhancing RPW8-mediated HR-like cell death1, and lag one homolog1 display lesions under short days (SD; Dietrich et al., 1994; Ishikawa et al., 2003; Wang et al., 2008; Ternes et al., 2011).Blockage of some metabolic pathways in plants may cause cell death and result in lesion formation. For example, the lesion-mimic phenotypes in the Arabidopsis mutants lesion initiation2 and accelerated cell death2 and the maize mutant lesion mimic22 result from an impairment of porphyrin metabolism (Hu et al., 1998; Ishikawa et al., 2001; Mach et al., 2001). Deficiency in fatty acid, sphingolipid, and myoinositol metabolism also causes cell death in Arabidopsis (Mou et al., 2000; Liang et al., 2003; Wang et al., 2008; Meng et al., 2009; Donahue et al., 2010; Berkey et al., 2012).Tyr degradation is an essential five-step pathway in animals (Lindblad et al., 1977). First, Tyr aminotransferase catalyzes the conversion of Tyr into 4-hydroxyphenylpyruvate, which is further transformed into homogentisate by 4-hydroxyphenylpyruvate dioxygenase. Through the sequential action of homogentisate dioxygenase (HGO), maleylacetoacetate isomerase (MAAI), and fumarylacetoacetate hydrolase (FAH), homogentisate is catalyzed to generate fumarate and acetoacetate (Lindblad et al., 1977). Blockage of this pathway in animals results in metabolic disorder diseases (Lindblad et al., 1977; Ruppert et al., 1992; Grompe et al., 1993). For example, human FAH deficiency causes hereditary tyrosinemia type I (HT1), an inborn lethal disease (St-Louis and Tanguay, 1997). Although the homologous genes putatively encoding these enzymes exist in plants (Dixon et al., 2000; Lopukhina et al., 2001; Dixon and Edwards, 2006), it is unclear whether this pathway is essential for plant growth and development.In this study, we report the isolation and characterization of a recessive short-day sensitive cell death1 (sscd1) mutant in Arabidopsis. Map-based cloning of the corresponding gene revealed that SSCD1 encodes the Arabidopsis putative FAH. Further knockout of the gene encoding the Arabidopsis putative HGO completely eliminated the spontaneous cell death phenotype in the sscd1 mutant. Furthermore, we found that treatment of Arabidopsis wild-type seedlings with succinylacetone, an abnormal metabolite caused by loss of FAH in the Tyr degradation pathway (Lindblad et al., 1977), is able to mimic the sscd1 cell death phenotype. These results demonstrate that disruption of FAH leads to cell death in Arabidopsis and suggest that the Tyr degradation pathway is essential for plant survival under SD.