Evolution of Conifer Diterpene Synthases: Diterpene Resin Acid Biosynthesis in Lodgepole Pine and Jack Pine Involves Monofunctional and Bifunctional Diterpene Synthases

Abscisic acid (ABA) induces stomatal closure and inhibits light-induced stomatal opening. The mechanisms in these two processes are not necessarily the same. It has been postulated that the ABA receptors involved in opening inhibition are different from those involved in closure induction. Here, we provide evidence that four recently identified ABA receptors (PYRABACTIN RESISTANCE1 [PYR1], PYRABACTIN RESISTANCE-LIKE1 [PYL1], PYL2, and PYL4) are not sufficient for opening inhibition in Arabidopsis (Arabidopsis thaliana). ABA-induced stomatal closure was impaired in the pyr1/pyl1/pyl2/pyl4 quadruple ABA receptor mutant. ABA inhibition of the opening of the mutant’s stomata remained intact. ABA did not induce either the production of reactive oxygen species and nitric oxide or the alkalization of the cytosol in the quadruple mutant, in accordance with the closure phenotype. Whole cell patch-clamp analysis of inward-rectifying K⁺ current in guard cells showed a partial inhibition by ABA, indicating that the ABA sensitivity of the mutant was not fully impaired. ABA substantially inhibited blue light-induced phosphorylation of H⁺-ATPase in guard cells in both the mutant and the wild type. On the other hand, in a knockout mutant of the SNF1-related protein kinase, srk2e, stomatal opening and closure, reactive oxygen species and nitric oxide production, cytosolic alkalization, inward-rectifying K⁺ current inactivation, and H⁺-ATPase phosphorylation were not sensitive to ABA.The phytohormone abscisic acid (ABA), which is synthesized in response to abiotic stresses, plays a key role in the drought hardiness of plants. Reducing transpirational water loss through stomatal pores is a major ABA response (Schroeder et al., 2001). ABA promotes the closure of open stomata and inhibits the opening of closed stomata. These effects are not simply the reverse of one another (Allen et al., 1999; Wang et al., 2001; Mishra et al., 2006).A class of receptors of ABA was identified (Ma et al., 2009; Park et al., 2009; Santiago et al., 2009; Nishimura et al., 2010). The sensitivity of stomata to ABA was strongly decreased in quadruple and sextuple mutants of the ABA receptor genes PYRABACTIN RESISTANCE/PYRABACTIN RESISTANCE-LIKE/REGULATORY COMPONENT OF ABSCISIC ACID RECEPTOR (PYR/PYL/RCAR; Nishimura et al., 2010; Gonzalez-Guzman et al., 2012). The PYR/PYL/RCAR receptors are involved in the early ABA signaling events, in which a sequence of interactions of the receptors with PROTEIN PHOSPHATASE 2Cs (PP2Cs) and subfamily 2 SNF1-RELATED PROTEIN KINASES (SnRK2s) leads to the activation of downstream ABA signaling targets in guard cells (Cutler et al., 2010; Kim et al., 2010; Weiner et al., 2010). Studies of Commelina communis and Vicia faba suggested that the ABA receptors involved in stomatal opening are not the same as the ABA receptors involved in stomatal closure (Allan et al., 1994; Anderson et al., 1994; Assmann, 1994; Schwartz et al., 1994). The roles of PYR/PYL/RCAR in either stomatal opening or closure remained to be elucidated.Blue light induces stomatal opening through the activation of plasma membrane H⁺-ATPase in guard cells that generates an inside-negative electrochemical gradient across the plasma membrane and drives K⁺ uptake through voltage-dependent inward-rectifying K⁺ channels (Assmann et al., 1985; Shimazaki et al., 1986; Blatt, 1987; Schroeder et al., 1987; Thiel et al., 1992). Phosphorylation of the penultimate Thr of the plasma membrane H⁺-ATPase is a prerequisite for blue light-induced activation of the H⁺-ATPase (Kinoshita and Shimazaki, 1999, 2002). ABA inhibits H⁺-ATPase activity through dephosphorylation of the penultimate Thr in the C terminus of the H⁺-ATPase in guard cells, resulting in prevention of the opening (Goh et al., 1996; Zhang et al., 2004; Hayashi et al., 2011). Inward-rectifying K⁺ currents (I_Kin) of guard cells are negatively regulated by ABA in addition to through the decline of the H⁺ pump-driven membrane potential difference (Schroeder and Hagiwara, 1989; Blatt, 1990; McAinsh et al., 1990; Schwartz et al., 1994; Grabov and Blatt, 1999; Saito et al., 2008). This down-regulation of ion transporters by ABA is essential for the inhibition of stomatal opening.A series of second messengers has been shown to mediate ABA-induced stomatal closure. Reactive oxygen species (ROS) produced by NADPH oxidases play a crucial role in ABA signaling in guard cells (Pei et al., 2000; Zhang et al., 2001; Kwak et al., 2003; Sirichandra et al., 2009; Jannat et al., 2011). Nitric oxide (NO) is an essential signaling component in ABA-induced stomatal closure (Desikan et al., 2002; Guo et al., 2003; Garcia-Mata and Lamattina, 2007; Neill et al., 2008). Alkalization of cytosolic pH in guard cells is postulated to mediate ABA-induced stomatal closure in Arabidopsis (Arabidopsis thaliana) and Pisum sativum and Paphiopedilum species (Irving et al., 1992; Gehring et al., 1997; Grabov and Blatt, 1997; Suhita et al., 2004; Gonugunta et al., 2008). These second messengers transduce environmental signals to ion channels and ion transporters that create the driving force for stomatal movements (Ward et al., 1995; MacRobbie, 1998; Garcia-Mata et al., 2003).In this study, we examined the mobilization of second messengers, the inactivation of I_Kin, and the suppression of H⁺-ATPase phosphorylation evoked by ABA in Arabidopsis mutants to clarify the downstream signaling events of ABA signaling in guard cells. The mutants included a quadruple mutant of PYR/PYL/RCARs, pyr1/pyl1/pyl2/pyl4, and a mutant of a SnRK2 kinase, srk2e.     

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.