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 on Metabolism: A Method of Accounting for Enzyme Costs in Flux Balance Analysis Reveals Alternative Pathways and Metabolite Stores in an Illuminated Arabidopsis Leaf

Flux balance analysis of plant metabolism is an established method for predicting metabolic flux phenotypes and for exploring the way in which the plant metabolic network delivers specific outcomes in different cell types, tissues, and temporal phases. A recurring theme is the need to explore the flexibility of the network in meeting its objectives and, in particular, to establish the extent to which alternative pathways can contribute to achieving specific outcomes. Unfortunately, predictions from conventional flux balance analysis minimize the simultaneous operation of alternative pathways, but by introducing flux-weighting factors to allow for the variable intrinsic cost of supporting each flux, it is possible to activate different pathways in individual simulations and, thus, to explore alternative pathways by averaging thousands of simulations. This new method has been applied to a diel genome-scale model of Arabidopsis (Arabidopsis thaliana) leaf metabolism to explore the flexibility of the network in meeting the metabolic requirements of the leaf in the light. This identified alternative flux modes in the Calvin-Benson cycle revealed the potential for alternative transitory carbon stores in leaves and led to predictions about the light-dependent contribution of alternative electron flow pathways and futile cycles in energy rebalancing. Notable features of the analysis include the light-dependent tradeoff between the use of carbohydrates and four-carbon organic acids as transitory storage forms and the way in which multiple pathways for the consumption of ATP and NADPH can contribute to the balancing of the requirements of photosynthetic metabolism with the energy available from photon capture.Computational modeling of metabolism is increasingly used to analyze the complexity of plant metabolic networks and to understand system-level properties such as carbon use efficiency (Sweetlove and Ratcliffe, 2011; Nägele and Weckwerth, 2012; de Oliveira Dal’Molin and Nielsen, 2013; Kruger and Ratcliffe, 2015). Flux balance analysis (FBA), which is a method for predicting steady-state flux distributions using a stoichiometric model of the network, is particularly well suited to this task, because it can be applied to large-scale metabolic networks (Lewis et al., 2012). It is also computationally efficient, meaning that models of different cell types (de Oliveira Dal’Molin et al., 2010), different temporal phases (Cheung et al., 2014), and different tissues (Borisjuk et al., 2013; Grafahrend-Belau et al., 2013) can be combined.FBA can generate accurate predictions of plant metabolic fluxes (Williams et al., 2010; Hay and Schwender, 2011; Cheung et al., 2013), but the analysis is complicated by the presence of alternative pathways that share the same function within the network. For example, mitochondria and chloroplasts have several potential mechanisms for maintaining energetic homeostasis, including alternative electron flow pathways, metabolite shuttles for the transfer of reducing power or ATP, and uncoupling mechanisms (Millar et al., 2011; Taniguchi and Miyake, 2012). More generally, the distributed robustness of metabolic networks means that they have the inherent property of being able to achieve cellular objectives in different ways (Wagner, 2005). However, FBA does not automatically identify these alternative flux distributions, because the immediate output of the analysis is a single flux distribution that satisfies the constraints and objectives applied to the model. This has the effect of masking the potential contribution of alternative pathways, and to avoid this, it is necessary to extend the analysis in a way that will reveal them.The most commonly used approach to this problem is flux variability analysis (FVA), which defines the permissible flux ranges for each reaction in the optimal flux space (Mahadevan and Schilling, 2003). Another possibility is random sampling of the optimal flux space, using a uniform sampling algorithm that was originally introduced to characterize the entire feasible flux solution space (Price et al., 2004). While both approaches are useful for exploring the capability of the metabolic system in achieving the cellular objectives, they do not give any indication of which alternative optimal flux solutions are more likely to be found in vivo, and they do not generate flux distributions that represent the biological reality in which alternative pathways may be operating simultaneously.Here, we develop a methodology that permits alternative pathways to be explored efficiently and that allows the consequences of the simultaneous operation of alternative pathways on the rest of the metabolic network to be examined. Our approach emerged from a reconsideration of the use of flux minimization as the objective function. Minimization of the sum of the absolute flux values supported by all the reactions in the network is often used as an objective function in FBA on the principle that cells have evolved to minimize the costs for the synthesis of the enzymes and membrane transporters that support growth and cell maintenance (Holzhütter, 2004). However, no weighting is applied when calculating the sum of fluxes, so there is an implicit assumption that the machinery cost per unit of flux is the same for all reactions. This assumption is invalid in vivo, as enzymes vary in terms of their size, number of subunits, and catalytic capacity. Ideally, each reaction should be weighted by its enzyme machinery costs per unit of flux, but such information is not available for the majority of the reactions in large-/genome-scale metabolic models. Here, we develop a modeling method, cost-weighted FBA, that avoids the invalid assumption of equal costs and that allows the evaluation of alternative metabolic routes in a complex network. The method was used to demonstrate the flexibility of leaf metabolism in meeting the metabolic requirements of an Arabidopsis (Arabidopsis thaliana) leaf in the light. A genome-scale diel FBA model, in which the light and dark phases of the diel cycle were solved as a single optimization problem, was used for the analysis because this approach currently provides the most realistic constraints-based framework for modeling leaf metabolism (Cheung et al., 2014; de Oliveira Dal’Molin et al., 2015).     

Genome-Wide Association of Carbon and Nitrogen Metabolism in the Maize Nested Association Mapping Population

Carbon (C) and nitrogen (N) metabolism are critical to plant growth and development and are at the basis of crop yield and adaptation. We performed high-throughput metabolite analyses on over 12,000 samples from the nested association mapping population to identify genetic variation in C and N metabolism in maize (Zea mays ssp. mays). All samples were grown in the same field and used to identify natural variation controlling the levels of 12 key C and N metabolites, namely chlorophyll a, chlorophyll b, fructose, fumarate, glucose, glutamate, malate, nitrate, starch, sucrose, total amino acids, and total protein, along with the first two principal components derived from them. Our genome-wide association results frequently identified hits with single-gene resolution. In addition to expected genes such as invertases, natural variation was identified in key C₄ metabolism genes, including carbonic anhydrases and a malate transporter. Unlike several prior maize studies, extensive pleiotropy was found for C and N metabolites. This integration of field-derived metabolite data with powerful mapping and genomics resources allows for the dissection of key metabolic pathways, providing avenues for future genetic improvement.Carbon (C) and nitrogen (N) metabolism are the basis for life on Earth. The production, balance, and tradeoffs of C and N metabolism are critical to all plant growth, yield, and local adaptation (Coruzzi and Bush, 2001; Coruzzi et al., 2007). In plants, there is a critical balance between the tissues that are producing energy (sources) and those using it (sinks), as the identities and locations of these vary through time and developmental stage (Smith et al., 2004). While a great deal of research has focused on the key genes and proteins involved in these processes (Wang et al., 1993; Kim et al., 2000; Takahashi et al., 2009), relatively little is known about the natural variation within a species that fine-tunes these processes in individual plants.In addition, a key aspect of core C metabolism involves the nature of plant photosynthesis. While the majority of plants use standard C₃ photosynthetic pathways, some, including maize (Zea mays) and many other grasses, use C₄ photosynthesis to concentrate CO₂ in bundle sheath cells to avoid wasteful photorespiration (Sage, 2004). Under some conditions (such as drought or high temperatures), C₄ photosynthesis is much more efficient than C₃ photosynthesis. Since these conditions are expected to become more prevalent in the near future due to climate change, various research groups are working to convert C₃ crop species to C₄ metabolism in order to boost crop production and food security (Sage and Zhu, 2011). Beyond this, better understanding of both C₃ and C₄ metabolic pathways will aid efforts to breed crops for superior yield, N-use efficiency, and other traits important for global food production.In the last two decades, quantitative trait locus (QTL) mapping, first with linkage analysis and later with association mapping, has been used to dissect C and N metabolism in several species, including Arabidopsis (Arabidopsis thaliana; Mitchell-Olds and Pedersen, 1998; Keurentjes et al., 2008; Lisec et al., 2008; Sulpice et al., 2009), tomato (Solanum lycopersicum; Schauer et al., 2006), and maize (Hirel et al., 2001; Limami et al., 2002; Zhang et al., 2006, 2010a, 2010b). These studies identified key genetic regions underlying variation in core C and N metabolism, many of which include candidate genes known to be involved in these processes.Previous studies of genetic variation for C and N metabolism are limited by the fact that they identified trait loci only through linkage mapping in artificial families or through association mapping across populations of unrelated individuals. Linkage mapping benefits from high statistical power due to many individuals sharing the same genotype at any given location, but it suffers from low resolution due to the limited number of generations (and hence recombination events) since the initial founders. Association mapping, in turn, enjoys high resolution due to the long recombination histories of natural populations but suffers from low power, since most genotypes occur in only a few individuals. In addition, many of these studies focused on C and N in artificial settings (e.g. greenhouses or growth chambers) instead of field conditions, running the risk that important genetic loci could be missed if the conditions do not include important (and potentially unknown) natural environmental variables.To address these issues and improve our understanding of C and N metabolism in maize, we used a massive and diverse germplasm resource, the maize nested association mapping (NAM) population (Buckler et al., 2009; McMullen et al., 2009), to evaluate genetic variation underlying the accumulation of 12 targeted metabolites in maize leaf tissue under field conditions. This population was formed by mating 25 diverse maize lines to the reference line, B73, and creating a 200-member biparental family from each of these crosses. The entire 5,000-member NAM population thus combines the strengths of both linkage and association mapping (McMullen et al., 2009), and it has been used to identify QTLs for important traits such as flowering time (Buckler et al., 2009), disease resistance (Kump et al., 2011; Poland et al., 2011), and plant architecture (Tian et al., 2011; Peiffer et al., 2013). Most importantly, this combination of power and resolution frequently resolves associations down to the single-gene level, even when using field-based data.The metabolites we profiled are key indicators of photosynthesis, respiration, glycolysis, and protein and sugar metabolism in the plant (Sulpice et al., 2009). By taking advantage of a robotized metabolic phenotyping platform (Gibon et al., 2004), we performed more than 100,000 assays across 12,000 samples, with two independent samples per experimental plot. Raw data and the best linear unbiased predictors (BLUPs) of these data were included as part of a study of general functional variation in maize (Wallace et al., 2014), but, to our knowledge, this is the first in-depth analysis of these metabolic data. We find strong correlations among several of the metabolites, and we also find extensive pleiotropy among the different traits. Many of the top QTLs are also near or within candidate genes relating to C and N metabolism, thus identifying targets for future breeding and selection. These results provide a powerful resource for those working with core C and N metabolism in plants and for improving maize performance in particular.     

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.     

The Arabidopsis Endosomal Sorting Complex Required for Transport III Regulates Internal Vesicle Formation of the Prevacuolar Compartment and Is Required for Plant Development

We have established an efficient transient expression system with several vacuolar reporters to study the roles of endosomal sorting complex required for transport (ESCRT)-III subunits in regulating the formation of intraluminal vesicles of prevacuolar compartments (PVCs)/multivesicular bodies (MVBs) in plant cells. By measuring the distributions of reporters on/within the membrane of PVC/MVB or tonoplast, we have identified dominant negative mutants of ESCRT-III subunits that affect membrane protein degradation from both secretory and endocytic pathways. In addition, induced expression of these mutants resulted in reduction in luminal vesicles of PVC/MVB, along with increased detection of membrane-attaching vesicles inside the PVC/MVB. Transgenic Arabidopsis (Arabidopsis thaliana) plants with induced expression of ESCRT-III dominant negative mutants also displayed severe cotyledon developmental defects with reduced cell size, loss of the central vacuole, and abnormal chloroplast development in mesophyll cells, pointing out an essential role of the ESCRT-III complex in postembryonic development in plants. Finally, membrane dissociation of ESCRT-III components is important for their biological functions and is regulated by direct interaction among Vacuolar Protein Sorting-Associated Protein20-1 (VPS20.1), Sucrose Nonfermenting7-1, VPS2.1, and the adenosine triphosphatase VPS4/SUPPRESSOR OF K⁺ TRANSPORT GROWTH DEFECT1.Endomembrane trafficking in plant cells is complicated such that secretory, endocytic, and recycling pathways are usually integrated with each other at the post-Golgi compartments, among which, the trans-Golgi network (TGN) and prevacuolar compartment (PVC)/multivesicular body (MVB) are best studied (Tse et al., 2004; Lam et al., 2007a, 2007b; Müller et al., 2007; Foresti and Denecke, 2008; Hwang, 2008; Otegui and Spitzer, 2008; Robinson et al., 2008; Richter et al., 2009; Ding et al., 2012; Gao et al., 2014). Following the endocytic trafficking of a lipophilic dye, FM4-64, the TGN and PVC/MVB are sequentially labeled and thus are defined as the early and late endosome, respectively, in plant cells (Lam et al., 2007a; Chow et al., 2008). While the TGN is a tubular vesicular-like structure that may include several different microdomains and fit its biological function as a sorting station (Chow et al., 2008; Kang et al., 2011), the PVC/MVB is 200 to 500 nm in size with multiple luminal vesicles of approximately 40 nm (Tse et al., 2004). Membrane cargoes destined for degradation are sequestered into these tiny luminal vesicles and delivered to the lumen of the lytic vacuole (LV) via direct fusion between the PVC/MVB and the LV (Spitzer et al., 2009; Viotti et al., 2010; Cai et al., 2012). Therefore, the PVC/MVB functions between the TGN and LV as an intermediate organelle and decides the fate of membrane cargoes in the LV.In yeast (Saccharomyces cerevisiae), carboxypeptidase S (CPS) is synthesized as a type II integral membrane protein and sorted from the Golgi to the lumen of the vacuole (Spormann et al., 1992). Genetic analyses on the trafficking of CPS have led to the identification of approximately 17 class E genes (Piper et al., 1995; Babst et al., 1997, 2002a, 2002b; Odorizzi et al., 1998; Katzmann et al., 2001) that constitute the core endosomal sorting complex required for transport (ESCRT) machinery. The evolutionarily conserved ESCRT complex consists of several functionally different subcomplexes, ESCRT-0, ESCRT-I, ESCRT-II, and ESCRT-III and the ESCRT-III-associated/Vacuolar Protein Sorting4 (VPS4) complex. Together, they form a complex protein-protein interaction network that coordinates sorting of cargoes and inward budding of the membrane on the MVB (Hurley and Hanson, 2010; Henne et al., 2011). Cargo proteins carrying ubiquitin signals are thought to be passed from one ESCRT subcomplex to the next, starting with their recognition by ESCRT-0 (Bilodeau et al., 2002, 2003; Hislop and von Zastrow, 2011; Le Bras et al., 2011; Shields and Piper, 2011; Urbé, 2011). ESCRT-0 recruits the ESCRT-I complex, a heterotetramer of VPS23, VPS28, VPS37, and MVB12, from the cytosol to the endosomal membrane (Katzmann et al., 2001, 2003). The C terminus of VPS28 interacts with the N terminus of VPS36, a member of the ESCRT-II complex (Kostelansky et al., 2006; Teo et al., 2006). Then, cargoes passed from ESCRT-I and ESCRT-II are concentrated in certain membrane domains of the endosome by ESCRT-III, which includes four coiled-coil proteins and is sufficient to induce the membrane invagination (Babst et al., 2002b; Saksena et al., 2009; Wollert et al., 2009). Finally, the ESCRT components are disassociated from the membrane by the adenosine triphosphatase (ATPase) associated with diverse cellular activities (AAA) VPS4/SUPPRESSOR OF K+ TRANSPORT GROWTH DEFECT1 (SKD1) before releasing the internal vesicles (Babst et al., 1997, 1998).Putative homologs of ESCRT-I–ESCRT-III and ESCRT-III-associated components have been identified in plants, except for ESCRT-0, which is only present in Opisthokonta (Winter and Hauser, 2006; Leung et al., 2008; Schellmann and Pimpl, 2009). To date, only a few plant ESCRT components have been studied in detail. The Arabidopsis (Arabidopsis thaliana) AAA ATPase SKD1 localized to the PVC/MVB and showed ATPase activity that was regulated by Lysosomal Trafficking Regulator-Interacting Protein5, a plant homolog of Vps Twenty Associated1 Protein (Haas et al., 2007). Expression of the dominant negative form of SKD1 caused an increase in the size of the MVB and a reduction in the number of internal vesicles (Haas et al., 2007). This protein also contributes to the maintenance of the central vacuole and might be associated with cell cycle regulation, as leaf trichomes expressing its dominant negative mutant form lost the central vacuole and frequently contained multiple nuclei (Shahriari et al., 2010). Double null mutants of CHARGED MULTIVESICULAR BODY PROTEIN, chmp1achmp1b, displayed severe growth defects and were seedling lethal. This may be due to the mislocalization of plasma membrane (PM) proteins, including those involved in auxin transport such as PINFORMED1, PINFORMED2, and AUXIN-RESISTANT1, from the vacuolar degradation pathway to the tonoplast of the LV (Spitzer et al., 2009).Plant ESCRT components usually contain several homologs, with the possibility of functional redundancy. Single mutants of individual ESCRT components may not result in an obvious phenotype, whereas knockout of all homologs of an ESCRT component by generating double or triple mutants may be lethal to the plant. As a first step to carry out systematic analysis on each ESCRT complex in plant cells, here, we established an efficient analysis system to monitor the localization changes of four vacuolar reporters that accumulate either in the lumen (LRR84A-GFP, EMP12-GFP, and aleurain-GFP) or on the tonoplast (GFP-VIT1) of the LV and identified several ESCRT-III dominant negative mutants. We reported that ESCRT-III subunits were involved in the release of PVC/MVB’s internal vesicles from the limiting membrane and were required for membrane protein degradation from secretory and endocytic pathways. In addition, transgenic Arabidopsis plants with induced expression of ESCRT-III dominant negative mutants showed severe cotyledon developmental defects. We also showed that membrane dissociation of ESCRT-III subunits was regulated by direct interaction with SKD1.