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.     

HAPLESS13, the Arabidopsis μ1 Adaptin,Is Essential for Protein Sorting at the trans-Golgi Network/Early Endosome

In plant cells, secretory and endocytic routes intersect at the trans-Golgi network (TGN)/early endosome (EE), where cargos are further sorted correctly and in a timely manner. Cargo sorting is essential for plant survival and therefore necessitates complex molecular machinery. Adaptor proteins (APs) play key roles in this process by recruiting coat proteins and selecting cargos for different vesicle carriers. The µ1 subunit of AP-1 in Arabidopsis (Arabidopsis thaliana) was recently identified at the TGN/EE and shown to be essential for cytokinesis. However, little was known about other cellular activities affected by mutations in AP-1 or the developmental consequences of such mutations. We report here that HAPLESS13 (HAP13), the Arabidopsis µ1 adaptin, is essential for protein sorting at the TGN/EE. Functional loss of HAP13 displayed pleiotropic developmental defects, some of which were suggestive of disrupted auxin signaling. Consistent with this, the asymmetric localization of PIN-FORMED2 (PIN2), an auxin transporter, was compromised in the mutant. In addition, cell morphogenesis was disrupted. We further demonstrate that HAP13 is critical for brefeldin A-sensitive but wortmannin-insensitive post-Golgi trafficking. Our results show that HAP13 is a key link in the sophisticated trafficking network in plant cells.Plant cells contain sophisticated endomembrane compartments, including the endoplasmic reticulum, the Golgi, the trans-Golgi network (TGN)/early endosome (EE), the prevacuolar compartments/multivesicular bodies (PVC/MVB), various types of vesicles, and the plasma membrane (PM; Ebine and Ueda, 2009; Richter et al., 2009). Intracellular protein sorting between the various locations in the endomembrane system occurs in both secretory and endocytic routes (Richter et al., 2009; De Marcos Lousa et al., 2012). Vesicles in the secretory route start at the endoplasmic reticulum, passing through the Golgi before reaching the TGN/EE, while vesicles in the endocytic route start from the PM before reaching the TGN/EE (Dhonukshe et al., 2007; Viotti et al., 2010). The TGN/EE in Arabidopsis (Arabidopsis thaliana) is an independent and highly dynamic organelle transiently associated with the Golgi (Dettmer et al., 2006; Lam et al., 2007; Viotti et al., 2010), distinct from the animal TGN. Once reaching the TGN/EE, proteins delivered by their vesicle carriers are subject to further sorting, being incorporated either into vesicles that pass through the PVC/MVB before reaching the vacuole for degradation or into vesicles that enter the secretory pathway for delivery to the PM (Ebine and Ueda, 2009; Richter et al., 2009). Therefore, the TGN/EE is a critical sorting compartment that lies at the intersection of the secretory and endocytic routes.Fine-tuned control of intracellular protein sorting at the TGN/EE is essential for plant development (Geldner et al., 2003; Dhonukshe et al., 2007, 2008; Richter et al., 2007; Kitakura et al., 2011; Wang et al., 2013). An auxin gradient is crucial for pattern formation in plants, whose dynamic maintenance requires the polar localization of auxin efflux carrier PINs through endocytic recycling (Geldner et al., 2003; Blilou et al., 2005; Paciorek et al., 2005; Abas et al., 2006; Jaillais et al., 2006; Dhonukshe et al., 2007; Kleine-Vehn et al., 2008). Receptor-like kinases (RLKs) have also been recognized as major cargos undergoing endocytic trafficking, which are either recycled back to the PM or sent for vacuolar degradation (Geldner and Robatzek, 2008; Irani and Russinova, 2009). RLKs are involved in most if not all developmental processes of plants (De Smet et al., 2009).Intracellular protein sorting relies on sorting signals within cargo proteins and on the molecular machinery that recognizes sorting signals (Boehm and Bonifacino, 2001; Robinson, 2004; Dhonukshe et al., 2007). Adaptor proteins (AP) play a key role (Boehm and Bonifacino, 2001; Robinson, 2004) in the recognition of sorting signals. APs are heterotetrameric protein complexes composed of two large subunits (β and γ/α/δ/ε), a small subunit (σ), and a medium subunit (µ) that is crucial for cargo selection (Boehm and Bonifacino, 2001). APs associate with the cytoplasmic side of secretory and endocytic vesicles, recruiting coat proteins and recognizing sorting signals within cargo proteins for their incorporation into vesicle carriers (Boehm and Bonifacino, 2001). Five APs have been identified so far, classified by their components, subcellular localization, and function (Boehm and Bonifacino, 2001; Robinson, 2004; Hirst et al., 2011). Of the five APs, AP-1 associates with the TGN or recycling endosomes (RE) in yeast and mammals (Huang et al., 2001; Robinson, 2004), mediating the sorting of cargo proteins to compartments of the endosomal-lysosomal system or to the basolateral PM of polarized epithelial cells (Gonzalez and Rodriguez-Boulan, 2009). Knockouts of AP-1 components in multicellular organisms resulted in embryonic lethality (Boehm and Bonifacino, 2001; Robinson, 2004).We show here that the recently identified Arabidopsis µ1 adaptin AP1M2 (Park et al., 2013; Teh et al., 2013) is a key component in the cellular machinery mediating intracellular protein sorting at the TGN/EE. AP1M2 was previously named HAPLESS13 (HAP13), whose mutant allele hap13 showed male gametophytic lethality (Johnson et al., 2004). In recent quests for AP-1 in plants, HAP13/AP1M2 was confirmed as the Arabidopsis µ1 adaptin based on its interaction with other components of the AP-1 complex as well as its localization at the TGN (Park et al., 2013; Teh et al., 2013). A novel mutant allele of HAP13/AP1M2, ap1m2-1, was found to be defective in the intracellular distribution of KNOLLE, leading to defective cytokinesis (Park et al., 2013; Teh et al., 2013). However, it was not clear whether HAP13/AP1M2 mediated other cellular activities and their developmental consequences. Using the same mutant allele, we found that functional loss of HAP13 (hap13-1/ap1m2-1) resulted in a full spectrum of growth defects, suggestive of compromised auxin signaling and of defective RLK signaling. Cell morphogenesis was also disturbed in hap13-1. Importantly, hap13-1 was insensitive to brefeldin A (BFA) washout, indicative of defects in guanine nucleotide exchange factors for ADP-ribosylation factor (ArfGEF)-mediated post-Golgi trafficking. Furthermore, HAP13/AP1M2 showed evolutionarily conserved function during vacuolar fusion, providing additional support to its identity as a µ1 adaptin. These results demonstrate the importance of the Arabidopsis µ1 adaptin for intracellular protein sorting centered on the TGN/EE.     

Production and Characterization of Synthetic Carboxysome Shells with Incorporated Luminal Proteins

Spatial segregation of metabolism, such as cellular-localized CO₂ fixation in C4 plants or in the cyanobacterial carboxysome, enhances the activity of inefficient enzymes by selectively concentrating them with their substrates. The carboxysome and other bacterial microcompartments (BMCs) have drawn particular attention for bioengineering of nanoreactors because they are self-assembling proteinaceous organelles. All BMCs share an architecturally similar, selectively permeable shell that encapsulates enzymes. Fundamental to engineering carboxysomes and other BMCs for applications in plant synthetic biology and metabolic engineering is understanding the structural determinants of cargo packaging and shell permeability. Here we describe the expression of a synthetic operon in Escherichia coli that produces carboxysome shells. Protein domains native to the carboxysome core were used to encapsulate foreign cargo into the synthetic shells. These synthetic shells can be purified to homogeneity with or without luminal proteins. Our results not only further the understanding of protein-protein interactions governing carboxysome assembly, but also establish a platform to study shell permeability and the structural basis of the function of intact BMC shells both in vivo and in vitro. This system will be especially useful for developing synthetic carboxysomes for plant engineering.A key enzyme in photosynthesis is the CO₂ fixation enzyme ribulose 1,5-bisphosphate carboxylase/oxygenase (Rubisco). Rubisco not only fixes CO₂, resulting in carbon assimilation, but it can also fix O₂, leading to photorespiration. Suppressing the unwanted oxygenase activity of Rubisco by sequestering Rubisco with a source of CO₂ is Nature’s solution to this substrate discrimination problem. While C4 plants compartmentalize CO₂ fixation in specific cells (Hibberd et al., 2008; Parry et al., 2011), cyanobacteria have evolved a specialized organelle composed entirely of protein to encapsulate Rubisco—the carboxysome.The carboxysome is just one type of bacterial microcompartment (BMC), widespread, functionally diverse bacterial organelles (Axen et al., 2014). All BMCs consist of an enzymatic core surrounded by a selectively permeable protein shell (Kerfeld et al., 2005; Tanaka et al., 2008; Chowdhury et al., 2014; Kerfeld and Erbilgin, 2015). While the encapsulated enzymes differ among functionally distinct BMCs, they share an architecturally similar shell composed of three types of proteins: BMC-H, BMC-T, and BMC-P forming hexamers, pseudohexamers, and pentamers, respectively (Kerfeld and Erbilgin, 2015). These constitute the building blocks of a self-assembling, apparently icosahedral shell with a diameter ranging from 40 to 400 nm (Shively et al., 1973a,b, 1998; Price and Badger, 1991; Bobik et al., 1999; Iancu et al., 2007, 2010; Petit et al., 2013; Erbilgin et al., 2014). Recent studies have also shown that in the biogenesis of BMCs an encapsulation peptide (EP) (Fan and Bobik, 2011; Kinney et al., 2012; Aussignargues et al., 2015; Jakobson et al., 2015), a short (approximately 18 residues) amphipathic α-helix mediates interactions between a subset of core protein and the shell (Fan and Bobik, 2011; Choudhary et al., 2012; Kinney et al., 2012; Lawrence et al., 2014; Lin et al., 2014; Aussignargues et al., 2015). Indeed, because they are self-assembling organelles composed entirely of protein, BMCs hold great promise for diverse applications in bioengineering and development of bionanomaterials (Frank et al., 2013; Chowdhury et al., 2014; Chessher et al., 2015; Kerfeld and Erbilgin, 2015); the key features of BMCs include selective permeability, spatial colocalization of enzymes, the establishment of private cofactor pools, and the potentially beneficial effects of confinement on protein stability. For example, introducing carboxysomes into plants could provide a saltational enhancement of crop photosynthesis (Price et al., 2013; Zarzycki et al., 2013; Lin et al., 2014; McGrath and Long, 2014).The β-carboxysome, which sequesters form 1B Rubisco, has been an important model system for the study of the structural basis of carboxysome function, assembly, and engineering (Kerfeld et al., 2005; Tanaka et al., 2008; Cameron et al., 2013; Aussignargues et al., 2015; Cai et al., 2015). Beta-carboxysomes assemble from the inside out (Cameron et al., 2013; Gonzalez-Esquer et al., 2015). Two proteins that are absolutely conserved and unique to β-carboxysomes, CcmM and CcmN, play essential roles in this process: CcmM crosslinks Rubisco through its C-terminal Rubisco small subunit-like domains (SSLDs; pfam00101); CcmM and CcmN interact through their N-terminal domains; and C-terminal EP of CcmN interacts with the carboxysome shell.Here we describe a system for producing synthetic β-carboxysome shells and encapsulating nonnative cargo. We constructed a synthetic operon composed of ccmK1, ccmK2, ccmL, and ccmO, genes encoding, respectively, two BMC-H proteins, a BMC-P protein, and a BMC-T protein of the carboxysome shell of the halotolerant cyanobacterium, Halothece sp. PCC 7418 (Halo hereafter). Recombinant shells composed of all four proteins were produced and purified. We also demonstrated that the terminal α-helices of CcmK1 and CcmK2 are not, as had been proposed (Samborska and Kimber, 2012), required for the shell formation, and that the synthetic shell is a single-layered protein membrane. Cargo could be targeted to the interior of the synthetic shells using either the EP of CcmN or the N-terminal domain of CcmM; the latter observation provides new insight into the organization of the β-carboxysome. Our results not only further the understanding of protein-protein interactions governing carboxysome assembly but also provide a platform to study carboxysome shell permeability. These results will be useful in guiding the design and optimization of carboxysomes and other BMCs for introduction into plants.     

NdhV Is a Subunit of NADPH Dehydrogenase Essential for Cyclic Electron Transport in Synechocystis sp. Strain PCC 6803

Two mutants sensitive to heat stress for growth and impaired in NADPH dehydrogenase (NDH-1)-dependent cyclic electron transport around photosystem I (NDH-CET) were isolated from the cyanobacterium Synechocystis sp. strain PCC 6803 transformed with a transposon-bearing library. Both mutants had a tag in the same sll0272 gene, encoding a protein highly homologous to NdhV identified in Arabidopsis (Arabidopsis thaliana). Deletion of the sll0272 gene (ndhV) did not influence the assembly of NDH-1 complexes and the activities of CO₂ uptake and respiration but reduced the activity of NDH-CET. NdhV interacted with NdhS, a ferredoxin-binding subunit of cyanobacterial NDH-1 complex. Deletion of NdhS completely abolished NdhV, but deletion of NdhV had no effect on the amount of NdhS. Reduction of NDH-CET activity was more significant in ΔndhS than in ΔndhV. We therefore propose that NdhV cooperates with NdhS to accept electrons from reduced ferredoxin.Cyanobacterial NADPH dehydrogenase (NDH-1) complexes are localized in the thylakoid membrane (Ohkawa et al., 2001, 2002; Zhang et al., 2004; Xu et al., 2008; Battchikova et al., 2011b) and participate in a variety of bioenergetic reactions, such as respiration, cyclic electron transport around photosystem I (NDH-CET), and CO₂ uptake (Ogawa, 1991; Mi et al., 1992; Ohkawa et al., 2000). Structurally, the cyanobacterial NDH-1 complexes closely resemble energy-converting complex I in eubacteria and the mitochondrial respiratory chain regardless of the absence of homologs of three subunits in cyanobacterial genomes that constitute the catalytically active core of complex I (Friedrich et al., 1995; Friedrich and Scheide, 2000; Arteni et al., 2006). Over the past decade, new subunits of NDH-1 complexes specific to oxygenic photosynthesis have been identified in several cyanobacterial strains. They are NdhM to NdhQ and NdhS (Prommeenate et al., 2004; Battchikova et al., 2005, 2011b; Nowaczyk et al., 2011; Wulfhorst et al., 2014; Zhang et al., 2014; Zhao et al., 2014b, 2015), in addition to NdhL first identified in the cyanobacterium Synechocystis sp. strain PCC 6803 (hereafter Synechocystis 6803) about 20 years ago (Ogawa, 1992). Among them, NdhS possesses a ferredoxin (Fd)-binding motif and was shown to bind Fd, which suggested that Fd is one of the electron donors to NDH-1 complexes (Mi et al., 1995; Battchikova et al., 2011b; Ma and Ogawa, 2015). Deletion of NdhS strongly reduced the activity of NDH-CET but had no effect on respiration and CO₂ uptake (Battchikova et al., 2011b; Ma and Ogawa, 2015). The NDH-CET plays an important role in coping with various environmental stresses regardless of its elusive mechanism. For example, this function can greatly alleviate heat-sensitive growth phenotypes (Wang et al., 2006a; Zhao et al., 2014a). Thus, heat treatment strategy can help in identifying the proteins essential to NDH-CET.Here, a new oxygenic photosynthesis-specific (OPS) subunit NdhV was identified in Synechocystis 6803 with the help of heat treatment strategy, and its deletion did not influence the assembly of NDH-1L and NDH-1MS complexes and the activities of CO₂ uptake and respiration but impaired the NDH-CET activity. We give evidence that NdhV interacts with NdhS and is another component of Fd-binding domain of cyanobacterial NDH-1 complex. A possible role of NdhV on the NDH-CET activity is discussed.     

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.