Functional Analysis of the Hsp93/ClpC Chaperone at the Chloroplast Envelope

The Hsp100-type chaperone Hsp93/ClpC has crucial roles in chloroplast biogenesis. In addition to its role in proteolysis in the stroma, biochemical and genetic evidence led to the hypothesis that this chaperone collaborates with the inner envelope TIC complex to power preprotein import. Recently, it was suggested that Hsp93, working together with the Clp proteolytic core, can confer a protein quality control mechanism at the envelope. Thus, the role of envelope-localized Hsp93, and the mechanism by which it participates in protein import, remain unclear. To analyze the function of Hsp93 in protein import independently of its ClpP association, we created a mutant of Hsp93 affecting its ClpP-binding motif (PBM) (Hsp93[P-]), which is essential for the chaperone’s interaction with the Clp proteolytic core. The Hsp93[P-] construct was ineffective at complementing the pale-yellow phenotype of hsp93 Arabidopsis (Arabidopsis thaliana) mutants, indicating that the PBM is essential for Hsp93 function. As expected, the PBM mutation negatively affected the degradation activity of the stromal Clp protease. The mutation also disrupted association of Hsp93 with the Clp proteolytic core at the envelope, without affecting the envelope localization of Hsp93 itself or its association with the TIC machinery, which we demonstrate to be mediated by a direct interaction with Tic110. Nonetheless, Hsp93[P-] expression did not detectably improve the protein import efficiency of hsp93 mutant chloroplasts. Thus, our results do not support the proposed function of Hsp93 in protein import propulsion, but are more consistent with the notion of Hsp93 performing a quality control role at the point of import.Chloroplasts are essential organelles in plant cells as they are responsible for performing a variety of functions (Jarvis and López-Juez, 2013). Although chloroplasts have their own genome (encoding approximately 100 proteins), the majority of the proteins found in these organelles are nucleus-encoded (approximately 3,000) (Leister, 2003), synthesized in the cytosol, and imported into the chloroplast as precursor proteins (preproteins), each one with a cleavable N-terminal extension or transit peptide (Shi and Theg, 2013a; Paila et al., 2015). The preprotein import mechanism is initiated by the interaction of the transit peptide with the translocon at the outer envelope membrane of chloroplasts (TOC) complex and subsequently involves transport through the translocon at the inner envelope membrane of chloroplasts (TIC) machinery in an energy-dependent process (Theg et al., 1989; Shi and Theg, 2013b). The Tic110 and Tic40 components have long been described as central TIC components, but these proteins were absent from a recently described 1-MD TIC complex (consisting of Tic20, Tic56, Tic100, and Tic214; Kovács-Bogdan et al., 2010; Nakai, 2015). One possible explanation is that two TIC complexes act sequentially during protein import (e.g. a Tic110-containing complex may act downstream of the 1-MD complex). A TIC complex associated import motor is proposed to exist at the stromal side of the inner envelope, and several stromal chaperones, including Hsp93/ClpC and Hsp70, have been proposed to act as motors to drive protein translocation into the stroma (for review, see Flores-Pérez and Jarvis, 2013).Hsp93 is closely related to bacterial ClpC and is a member of the Class I subfamily of Hsp100 chaperones, which themselves belong to the wider AAA+ (ATPases associated with various cellular activities) superfamily (Hanson and Whiteheart, 2005; Flores-Pérez and Jarvis, 2013). AAA+ enzymes are involved in a variety of cellular processes, such as protein folding, unfolding for proteolysis, and disassembly of protein aggregates or protein complexes. Although AAA+ chaperones are well characterized in bacteria, they are found in all kingdoms (Hanson and Whiteheart, 2005). Such proteins possess one or two nucleotide binding domains, both of which contain conserved Walker A and B motifs. These chaperones may also contain a conserved ClpP-binding motif (PBM), or P-loop, which is essential for interaction with the unrelated, proteolytic ClpP subunit (Weibezahn et al., 2004; Hanson and Whiteheart, 2005).In the chloroplast, Hsp93/ClpC partitions between the inner envelope membrane and the chloroplast stroma. Most Hsp93/ClpC protein is located in the stroma. Nonetheless, a large proportion of the total chloroplast Hsp93/ClpC pool (30%) associates with the envelope (Sjögren et al., 2014). Hsp93 has frequently been copurified with TIC and TOC complex components, which led to the hypothesis that it provides the driving force for preprotein import (Akita et al., 1997; Nielsen et al., 1997). Also, Hsp93 was found to specifically coimmunoprecipitate with preproteins under limiting ATP conditions and to stably bind to transit peptides in vitro (Nielsen et al., 1997; Rosano et al., 2011). Genetic and molecular studies have suggested that it functions in close association with Tic110 and Tic40 (Chou et al., 2003; Kovacheva et al., 2005; Chou et al., 2006). More recently, it was shown that the N-terminal domain of Hsp93 is important for its membrane association (Chu and Li, 2012). Despite all this evidence, the nature of the interaction between Hsp93 and the TIC apparatus has not been fully characterized.Analysis of mutants also supported the involvement of the Hsp93 chaperone in protein import. In Arabidopsis (Arabidopsis thaliana), two homologous genes, atHSP93-V (CLPC1) and atHSP93-III (CLPC2), code for Hsp93/ClpC, and the resulting protein isoforms share 91% amino acid sequence identity (Kovacheva et al., 2007). The Hsp93-V protein is the most abundant isoform, and mutations in the atHSP93-V gene lead to a pale-green plant phenotype with protein import defective chloroplasts. In contrast, atHSP93-III knockout plants are indistinguishable from the wild type, most likely due to the compensatory presence of functionally redundant and abundant atHsp93-V (Kovacheva et al., 2005, 2007). Complete loss of both proteins in Arabidopsis is lethal during embryo development, whereas double mutants lacking Hsp93-V but retaining partial Hsp93-III activity are viable but exhibit severe chlorosis and protein import defects (Kovacheva et al., 2007).More typically, as expected by its close relationship to bacterial orthologs, Hsp93/ClpC is a functional component of the caseinolytic protease (Clp) in the chloroplast stroma, where it recognizes and unfolds substrates for degradation (Shanklin et al., 1995). Significantly, the Clp proteolytic core is also bound to the envelope membranes, in quantities which are sufficient to bind to all of the similarly localized Hsp93/ClpC (Sjögren et al., 2014). This recent finding suggested a role for the Clp protease in protein quality control at the envelope. The structure of the Clp protease complex comprises a cylinder-like protease core and an AAA+ chaperone ring complex, and it is generally conserved throughout evolution (Nishimura and van Wijk, 2015). In Arabidopsis, the plastid Clp proteolytic core contains two distinct heptameric rings (the P-ring consisting of ClpP3-P6 and the R-ring consisting of ClpP1 and ClpR1-R4; Sjögren et al., 2006), and attached to this are accessory ClpT proteins involved in core assembly (Sjögren and Clarke, 2011). Several studies have shown that deficiency of the proteolytic subunits of the core complex leads to sick plant phenotypes (Sjögren et al., 2004; Rudella et al., 2006; Sjögren et al., 2006), highlighting the essential nature of Clp proteolytic activity to chloroplast function and plant viability.As described above, the putative interacting partners of Hsp93 at the envelope are Tic110 and Tic40. Tic110 is a highly abundant protein and is essential for plastid biogenesis (Inaba et al., 2005; Kovacheva et al., 2007). It has two N-terminal transmembrane α-helices, and it projects a large C-terminal hydrophilic domain into the stroma (Jackson et al., 1998; Inaba et al., 2003). A stromal region proximal to the second transmembrane helix selectively associates with transit peptides, serving as a docking site for preproteins as they emerge from the TIC channel (Inaba et al., 2003). The hydrophilic domain of algal Tic110 possesses a rod-shaped helix-repeat structure similar to HEAT-repeat domains (and plant Tic110 proteins are predicted to be similar), and these typically function as scaffolds for protein-protein interactions (Tsai et al., 2013). Tic40 is topologically similar to Tic110 and is proposed to act as a cochaperone in the preprotein import motor (Chou et al., 2003). In the corresponding model, a transit peptide emerging from the TIC channel binds to the stromal domain of Tic110; this binding causes a conformational change of Tic110 to recruit Tic40, which in turn triggers transit peptide release to enable association of the preprotein with Hsp93 (Inaba et al., 2003; Chou et al., 2006). Finally, Tic40 is proposed to stimulate ATP hydrolysis by Hsp93 so that the chaperone pulls the preprotein into the stroma (Chou et al., 2006).Although there is good evidence that Hsp93 is involved in protein import, the ability of Hsp93 to associate with the Clp protease core means that, in principle, any aspect of the hsp93 mutant phenotype could be due to disruption of the ClpP-linked functions of the protein. Bearing this in mind, we aimed to further characterize the role of Hsp93 at the inner envelope membrane. First, we analyzed the putative interactions of Hsp93 with the TIC components, Tic110 and Tic40, in a complementary set of in vitro and in vivo studies. Second, we evaluated the proposed role of Hsp93 in protein import independently of its role in proteolysis by creating a PBM mutant of the major Hsp93 isoform, atHsp93-V, and studying its activity in planta.     

Neofunctionalization of Duplicated Tic40 Genes Caused a Gain-of-Function Variation Related to Male Fertility in Brassica oleracea Lineages

Gene duplication followed by functional divergence in the event of polyploidization is a major contributor to evolutionary novelties. The Brassica genus evolved from a common ancestor after whole-genome triplication. Here, we studied the evolutionary and functional features of Brassica spp. homologs to Tic40 (for translocon at the inner membrane of chloroplasts with 40 kDa). Four Tic40 loci were identified in allotetraploid Brassica napus and two loci in each of three basic diploid Brassica spp. Although these Tic40 homologs share high sequence identities and similar expression patterns, they exhibit altered functional features. Complementation assays conducted on Arabidopsis thaliana tic40 and the B. napus male-sterile line 7365A suggested that all Brassica spp. Tic40 homologs retain an ancestral function similar to that of AtTic40, whereas BolC9.Tic40 in Brassica oleracea and its ortholog in B. napus, BnaC9.Tic40, in addition, evolved a novel function that can rescue the fertility of 7365A. A homologous chromosomal rearrangement placed bnac9.tic40 originating from the A genome (BraA10.Tic40) as an allele of BnaC9.Tic40 in the C genome, resulting in phenotypic variation for male sterility in the B. napus near-isogenic two-type line 7365AB. Assessment of the complementation activity of chimeric B. napus Tic40 domain-swapping constructs in 7365A suggested that amino acid replacements in the carboxyl terminus of BnaC9.Tic40 cause this functional divergence. The distribution of these amino acid replacements in 59 diverse Brassica spp. accessions demonstrated that the neofunctionalization of Tic40 is restricted to B. oleracea and its derivatives and thus occurred after the divergence of the Brassica spp. A, B, and C genomes.Polyploidy or whole-genome duplication is thought to be a prominent evolutionary force in eukaryotes (Wolfe, 2001; Udall and Wendel, 2006), especially for flowering plants (Blanc and Wolfe, 2004; Van de Peer et al., 2009). Almost 95% of angiosperms show evidence of having undergone at least one round of whole-genome duplication in their evolutionary history, suggesting that most extant diploid flowering plants have evolved from ancient polyploids (Cui et al., 2006; Soltis et al., 2009). Gene duplications in the event of polyploidization provide sources for evolutionary novelties that could benefit plants (Lukens et al., 2004; Chen, 2007). Divergence after gene duplication could result in three primary evolutionary fates of duplicated genes: pseudogenization, neofunctionalization, and subfunctionalization (Force et al., 1999; Conant and Wolfe, 2008; Liu and Adams, 2010). Pseudogenization implies that duplicated genes with redundant functions lose their function by accumulating negative mutations; neofunctionalization denotes that the redundant gene evolves a new adaptive function; while subfunctionalization causes the duplicated genes to adopt a different part of the function of an ancestral gene (Rodríguez-Trelles et al., 2003; Flagel and Wendel, 2009; Liu and Adams, 2010).The Brassica genus consists of three basic diploid species: Brassica rapa (AA; n = 10), Brassica nigra (BB; n = 8), and Brassica oleracea (CC; n = 9), and their derivative allotetraploid species: Brassica juncea (AABB; n = 18), Brassica napus (AACC; n = 19), and Brassica carinata (BBCC; n = 17; Beilstein et al., 2006). Comparative genetic mapping demonstrated that these Brassica spp., which diverged from Arabidopsis thaliana approximately 20 to 40 million years ago (Lagercrantz and Lydiate, 1996; Blanc et al., 2003; Town et al., 2006), descended from a common ancestor after whole-genome triplication (Parkin et al., 2002). Collinear comparison showed that for each of 24 ancestral genomic blocks defined in the ancestral karyotype in A. thaliana, three syntenic copies were identified in each of the diploid Brassica spp. genomes, with only one exception (Schranz et al., 2006; Wang et al., 2011; Cheng et al., 2013). Fractionation (gene loss from homologous genomic regions) and chromosomal rearrangements were prevalent in the diploidization process of the hexaploid Brassica spp. common ancestor (Lagercrantz, 1998; Town et al., 2006; Ziolkowski et al., 2006; Mun et al., 2009). Based on gene density differences caused by varying gene loss rates in the three collinear genomic block copies, these genomic blocks were classified into three subgenomes in the diploid Brassica spp. genomes: the least fractionated (LF), the medium fractionated (MF1), and the most fractionated (MF2) subgenomes (Wang et al., 2011; Tang and Lyons, 2012; Cheng et al., 2013). The different rates of gene loss of the three subgenomes support a two-step origin of the Brassiceae ancestral genome involving a tetraploidization process followed by substantial fractionation of the subgenomes MF1 and MF2 and more recently hybridization with the third subgenome LF to form a hexaploid (Wang et al., 2011; Tang et al., 2012). Although collinearity and changes in genomic structure, including duplications, deletions, and rearrangements of the Brassica spp. and A. thaliana are well studied, there is limited knowledge of the molecular and functional divergence of duplicated or homologous genes in the Brassica spp.To utilize heterosis in B. napus breeding, hybrid production is based mainly on male sterility. Currently, the recessive epistatic genic male-sterile three-type line system 7365ABC is widely used for oilseed heterosis due to its advantages, producing 100% sterile offspring for realizing the triple-cross hybrid (Huang et al., 2007; Xia et al., 2012). The male sterility in this system is controlled by two genes, a recessive male sterile gene Bnms3 and a epistatic gene BnRf (Zhou et al., 2012). Bnms3 was recently reported to be a homolog to A. thaliana Tic40 (Dun et al., 2011). Tic40 was identified as a member of the TIC (for translocon at the inner envelope membrane of chloroplasts) complex that functions as a cochaperone to coordinate Tic110 and the stromal chaperone heat shock protein93 (Hsp93) (Chou et al., 2003, 2006). The A. thaliana tic40 mutant displayed a chlorotic phenotype throughout development (Chou et al., 2003) but a male-fertile phenotype with mature pollen grains (Dun et al., 2011). Interestingly, one allele of Bnms3, BnaC.Tic40, can rescue the fertility of the B. napus male-sterile line 7365A (Dun et al., 2011).In this study, we identified and characterized Tic40 homologs in B. napus and three basic diploid Brassica spp. We suggested that neofunctionalization of BnaC9.Tic40 after the divergence of the Brassica spp. A, B, and C genomes was caused by amino acid replacements in the C terminus of BnaC9.Tic40. In addition, we validated that the allelic genes BnaC9.Tic40 (equivalent to BnaC.Tic40 as described by Dun et al. [2011]) and bnac9.tic40 originate from the C and A genomes, respectively, and became allelic due to a homologous chromosomal rearrangement in B. napus. These results provide further knowledge for the effective utilization of the restoring gene of 7365A and a better insight into the functional divergence of homologous duplicated genes in paleoploid Brassica spp.     

Surveying Rubisco Diversity and Temperature Response to Improve Crop Photosynthetic Efficiency

The threat to global food security of stagnating yields and population growth makes increasing crop productivity a critical goal over the coming decades. One key target for improving crop productivity and yields is increasing the efficiency of photosynthesis. Central to photosynthesis is Rubisco, which is a critical but often rate-limiting component. Here, we present full Rubisco catalytic properties measured at three temperatures for 75 plants species representing both crops and undomesticated plants from diverse climates. Some newly characterized Rubiscos were naturally “better” compared to crop enzymes and have the potential to improve crop photosynthetic efficiency. The temperature response of the various catalytic parameters was largely consistent across the diverse range of species, though absolute values showed significant variation in Rubisco catalysis, even between closely related species. An analysis of residue differences among the species characterized identified a number of candidate amino acid substitutions that will aid in advancing engineering of improved Rubisco in crop systems. This study provides new insights on the range of Rubisco catalysis and temperature response present in nature, and provides new information to include in models from leaf to canopy and ecosystem scale.In a changing climate and under pressure from a population set to hit nine billion by 2050, global food security will require massive changes to the way food is produced, distributed, and consumed (Ort et al., 2015). To match rising demand, agricultural production must increase by 50 to 70% in the next 35 years, and yet the gains in crop yields initiated by the green revolution are slowing, and in some cases, stagnating (Long and Ort, 2010; Ray et al., 2012). Among a number of areas being pursued to increase crop productivity and food production, improving photosynthetic efficiency is a clear target, offering great promise (Parry et al., 2007; von Caemmerer et al., 2012; Price et al., 2013; Ort et al., 2015). As the gatekeeper of carbon entry into the biosphere and often acting as the rate-limiting step of photosynthesis, Rubisco, the most abundant enzyme on the planet (Ellis, 1979), is an obvious and important target for improving crop photosynthetic efficiency.Rubisco is considered to exhibit comparatively poor catalysis, in terms of catalytic rate, specificity, and CO₂ affinity (Tcherkez et al., 2006; Andersson, 2008), leading to the suggestion that even small increases in catalytic efficiency may result in substantial improvements to carbon assimilation across a growing season (Zhu et al., 2004; Parry et al., 2013; Galmés et al., 2014a; Carmo-Silva et al., 2015). If combined with complimentary changes such as optimizing other components of the Calvin Benson or photorespiratory cycles (Raines, 2011; Peterhansel et al., 2013; Simkin et al., 2015), optimized canopy architecture (Drewry et al., 2014), or introducing elements of a carbon concentrating mechanism (Furbank et al., 2009; Lin et al., 2014a; Hanson et al., 2016; Long et al., 2016), Rubisco improvement presents an opportunity to dramatically increase the photosynthetic efficiency of crop plants (McGrath and Long, 2014; Long et al., 2015; Betti et al., 2016). A combination of the available strategies is essential for devising tailored solutions to meet the varied requirements of different crops and the diverse conditions under which they are typically grown around the world.Efforts to engineer an improved Rubisco have not yet produced a “super Rubisco” (Parry et al., 2007; Ort et al., 2015). However, advances in engineering precise changes in model systems continue to provide important developments that are increasing our understanding of Rubisco catalysis (Spreitzer et al., 2005; Whitney et al., 2011a, 2011b; Morita et al., 2014; Wilson et al., 2016), regulation (Andralojc et al., 2012; Carmo-Silva and Salvucci, 2013; Bracher et al., 2015), and biogenesis (Saschenbrecker et al., 2007; Whitney and Sharwood, 2008; Lin et al., 2014b; Hauser et al., 2015; Whitney et al., 2015).A complementary approach is to understand and exploit Rubisco natural diversity. Previous characterization of Rubisco from a limited number of species has not only demonstrated significant differences in the underlying catalytic parameters, but also suggests that further undiscovered diversity exists in nature and that the properties of some of these enzymes could be beneficial if present in crop plants (Carmo-Silva et al., 2015). Recent studies clearly illustrate the variation possible among even closely related species (Galmés et al., 2005, 2014b, 2014c; Kubien et al., 2008; Andralojc et al., 2014; Prins et al., 2016).Until recently, there have been relatively few attempts to characterize the consistency, or lack thereof, of temperature effects on in vitro Rubisco catalysis (Sharwood and Whitney, 2014), and often studies only consider a subset of Rubisco catalytic properties. This type of characterization is particularly important for future engineering efforts, enabling specific temperature effects to be factored into any attempts to modify crops for a future climate. In addition, the ability to coanalyze catalytic properties and DNA or amino acid sequence provides the opportunity to correlate sequence and biochemistry to inform engineering studies (Christin et al., 2008; Kapralov et al., 2011; Rosnow et al., 2015). While the amount of gene sequence information available grows rapidly with improving technology, knowledge of the corresponding biochemical variation resulting has yet to be determined (Cousins et al., 2010; Carmo-Silva et al., 2015; Sharwood and Whitney, 2014; Nunes-Nesi et al., 2016).This study aimed to characterize the catalytic properties of Rubisco from diverse species, comprising a broad range of monocots and dicots from diverse environments. The temperature dependence of Rubisco catalysis was evaluated to tailor Rubisco engineering for crop improvement in specific environments. Catalytic diversity was analyzed alongside the sequence of the Rubisco large subunit gene, rbcL, to identify potential catalytic switches for improving photosynthesis and productivity. In vitro results were compared to the average temperature of the warmest quarter in the regions where each species grows to investigate the role of temperature in modulating Rubisco catalysis.     

Focus on Ethylene: Ethylene and the Regulation of Physiological and Morphological Responses to Nutrient Deficiencies

To cope with nutrient deficiencies, plants develop both morphological and physiological responses. The regulation of these responses is not totally understood, but some hormones and signaling substances have been implicated. It was suggested several years ago that ethylene participates in the regulation of responses to iron and phosphorous deficiency. More recently, its role has been extended to other deficiencies, such as potassium, sulfur, and others. The role of ethylene in so many deficiencies suggests that, to confer specificity to the different responses, it should act through different transduction pathways and/or in conjunction with other signals. In this update, the data supporting a role for ethylene in the regulation of responses to different nutrient deficiencies will be reviewed. In addition, the results suggesting the action of ethylene through different transduction pathways and its interaction with other hormones and signaling substances will be discussed.When plants suffer from a mineral nutrient deficiency, they develop morphological and physiological responses (mainly in their roots) aimed to facilitate the uptake and mobilization of the limiting nutrient. After the nutrient has been acquired in enough quantity, these responses need to be switched off to avoid toxicity and conserve energy. In recent years, different plant hormones (e.g. ethylene, auxin, cytokinins, jasmonic acid, abscisic acid, brassinosteroids, GAs, and strigolactones) have been implicated in the regulation of these responses (Romera et al., 2007, 2011, 2015; Liu et al., 2009; Rubio et al., 2009; Kapulnik et al., 2011; Kiba et al., 2011; Iqbal et al., 2013; Zhang et al., 2014).Before the 1990s, there were several publications relating ethylene and nutrient deficiencies (cited in Lynch and Brown [1997] and Romera et al. [1999]) without establishing a direct implication of ethylene in the regulation of nutrient deficiency responses. In 1994, Romera and Alcántara (1994) published an article in Plant Physiology suggesting a role for ethylene in the regulation of Fe deficiency responses. In 1999, Borch et al. (1999) showed the participation of ethylene in the regulation of P deficiency responses. Since then, evidence has been accumulating in support of a role for ethylene in the regulation of both Fe (Romera et al., 1999, 2015; Waters and Blevins, 2000; Lucena et al., 2006; Waters et al., 2007; García et al., 2010, 2011, 2013, 2014; Yang et al., 2014) and P deficiency responses (Kim et al., 2008; Lei et al., 2011; Li et al., 2011; Nagarajan and Smith, 2012; Wang et al., 2012, 2014c). Both Fe and P may be poorly available in most soils, and plants develop similar responses under their deficiencies (Romera and Alcántara, 2004; Zhang et al., 2014). More recently, a role for ethylene has been extended to other deficiencies, such as K (Shin and Schachtman, 2004; Jung et al., 2009; Kim et al., 2012), S (Maruyama-Nakashita et al., 2006; Wawrzyńska et al., 2010; Moniuszko et al., 2013), and B (Martín-Rejano et al., 2011). Ethylene has also been implicated in both N deficiency and excess (Tian et al., 2009; Mohd-Radzman et al., 2013; Zheng et al., 2013), and its participation in Mg deficiency has been suggested (Hermans et al., 2010).In this update, we will review the information supporting a role for ethylene in the regulation of different nutrient deficiency responses. For information relating ethylene to other aspects of plant mineral nutrition, such as N₂ fixation and responses to excess of nitrate or essential heavy metals, the reader is referred to other reviews (for review, see Maksymiec, 2007; Mohd-Radzman et al., 2013; Steffens, 2014).     

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.     

Plants Actively Avoid State Transitions upon Changes in Light Intensity: Role of Light-Harvesting Complex II Protein Dephosphorylation in High Light

Photosystem II (PSII) core and light-harvesting complex II (LHCII) proteins in plant chloroplasts undergo reversible phosphorylation upon changes in light intensity (being under control of redox-regulated STN7 and STN8 kinases and TAP38/PPH1 and PSII core phosphatases). Shift of plants from growth light to high light results in an increase of PSII core phosphorylation, whereas LHCII phosphorylation concomitantly decreases. Exactly the opposite takes place when plants are shifted to lower light intensity. Despite distinct changes occurring in thylakoid protein phosphorylation upon light intensity changes, the excitation balance between PSII and photosystem I remains unchanged. This differs drastically from the canonical-state transition model induced by artificial states 1 and 2 lights that concomitantly either dephosphorylate or phosphorylate, respectively, both the PSII core and LHCII phosphoproteins. Analysis of the kinase and phosphatase mutants revealed that TAP38/PPH1 phosphatase is crucial in preventing state transition upon increase in light intensity. Indeed, tap38/pph1 mutant revealed strong concomitant phosphorylation of both the PSII core and LHCII proteins upon transfer to high light, thus resembling the wild type under state 2 light. Coordinated function of thylakoid protein kinases and phosphatases is shown to secure balanced excitation energy for both photosystems by preventing state transitions upon changes in light intensity. Moreover, PROTON GRADIENT REGULATION5 (PGR5) is required for proper regulation of thylakoid protein kinases and phosphatases, and the pgr5 mutant mimics phenotypes of tap38/pph1. This shows that there is a close cooperation between the redox- and proton gradient-dependent regulatory mechanisms for proper function of the photosynthetic machinery.Photosynthetic light reactions take place in the chloroplast thylakoid membrane. Primary energy conversion reactions are performed by synchronized function of the two light energy-driven enzymes PSII and PSI. PSII uses excitation energy to split water into electrons and protons. PSII feeds electrons to the intersystem electron transfer chain (ETC) consisting of plastoquinone, cytochrome b₆f, and plastocyanin. PSI oxidizes the ETC in a light-driven reduction of NADP to NADPH. Light energy is collected by the light-harvesting antenna systems in the thylakoid membrane composed of specific pigment-protein complexes (light-harvesting complex I [LHCI] and LHCII). The majority of the light-absorbing pigments are bound to LHCII trimers that can serve the light harvesting of both photosystems (Galka et al., 2012; Kouřil et al., 2013; Wientjes et al., 2013b). Energy distribution from LHCII is regulated by protein phosphorylation (Bennett, 1979; Bennett et al., 1980; Allen et al., 1981) under control of the STN7 and STN8 kinases (Depège et al., 2003; Bellafiore et al., 2005; Bonardi et al., 2005; Vainonen et al., 2005) and the TAP38/PPH1 and Photosystem II Core Phosphatase (PBCP) phosphatases (Pribil et al., 2010; Shapiguzov et al., 2010; Samol et al., 2012). LHCII trimers are composed of LHCB1, LHCB2, and LHCB3 proteins, and in addition to reversible phosphorylation of LHCB1 and LHCB2, the protein composition of the LHCII trimers also affects the energy distribution from the light-harvesting system to photosystems (Damkjaer et al., 2009; Pietrzykowska et al., 2014). Most of the LHCII trimers are located in the PSII-rich grana membranes and PSII- and PSI-rich grana margins of the thylakoid membrane, and only a minor fraction resides in PSI- and ATP synthase-rich stroma lamellae (Tikkanen et al., 2008b; Suorsa et al., 2014). Both photosystems bind a small amount of LHCII trimers in biochemically isolatable PSII-LHCII and PSI-LHCII complexes (Pesaresi et al., 2009; Järvi et al., 2011; Caffarri et al., 2014). The large portion of the LHCII, however, does not form isolatable complexes with PSII or PSI, and therefore, it separates as free LHCII trimers upon biochemical fractionation of the thylakoid membrane by Suc gradient centrifugation or in native gel analyses (Caffarri et al., 2009; Järvi et al., 2011), the amount being dependent on the thylakoid isolation method. Nonetheless, in vivo, this major LHCII antenna fraction serves the light-harvesting function. This is based on the fact that fluorescence from free LHCII, peaking at 680 nm in 77-K fluorescence emission spectra, can only be detected when the energy transfer properties of the thylakoid membrane are disturbed by detergents (Grieco et al., 2015).Regulation of excitation energy distribution from LHCII to PSII and PSI has, for decades, been linked to LHCII phosphorylation and state transitions (Bennett, 1979; Bennett et al., 1980; Allen et al., 1981). It has been explained that a fraction of LHCII gets phosphorylated and migrates from PSII to PSI, which can be evidenced as increase in PSI cross section and was assigned as transition to state 2 (for review, see Allen, 2003; Rochaix et al., 2012). The LHCII proteins are, however, phosphorylated all over the thylakoid membrane (i.e. in the PSII- and LHCII-rich grana core) in grana margins containing PSII, LHCII, and PSI as well as in PSI-rich stroma lamellae also harboring PSII-LHCII, LHCII, and PSI-LHCII complexes in minor amounts (Tikkanen et al., 2008b; Grieco et al., 2012; Leoni et al., 2013; Wientjes et al., 2013a)—making the canonical-state transition theory inadequate to explain the physiological role of reversible LHCII phosphorylation (Tikkanen and Aro, 2014). Moreover, the traditional-state transition model is based on lateral segregation of PSII-LHCII and PSI-LHCI to different thylakoid domains. It, however, seems likely that PSII and PSI are energetically connected through a shared light-harvesting system composed of LHCII trimers (Grieco et al., 2015), and there is efficient excitation energy transfer between the two photosystems (Yokono et al., 2015). Nevertheless, it is clear that LHCII phosphorylation is a prerequisite to form an isolatable PSI-LHCII complex called the state transition complex (Pesaresi et al., 2009; Järvi et al., 2011). Existence of a minor state transition complex, however, does not explain why LHCII is phosphorylated all over the thylakoid membrane and how the energy transfer is regulated from the majority of LHCII antenna that is shared between PSII and PSI but does not form isolatable complexes with them (Grieco et al., 2015).Plants grown under any steady-state white light condition show the following characteristics of the thylakoid membrane: PSII core and LHCII phosphoproteins are moderately phosphorylated, phosphorylation takes place all over the thylakoid membrane, and the PSI-LHCII state transition complex is present (Järvi et al., 2011; Grieco et al., 2012; Wientjes et al., 2013b). Upon changes in the light intensity, the relative phosphorylation level between PSII core and LHCII phosphoproteins drastically changes (Rintamäki et al., 1997, 2000) in the timescale of 5 to 30 min. When light intensity increases, the PSII core protein phosphorylation increases, whereas the level of LHCII phosphorylation decreases. On the contrary, a decrease in light intensity decreases the phosphorylation level of PSII core proteins but strongly increases the phosphorylation of the LHCII proteins (Rintamäki et al., 1997, 2000). The presence and absence of the PSI-LHCII state transition complex correlate with LHCII phosphorylation (similar to the state transitions; Pesaresi et al., 2009; Wientjes et al., 2013b). Despite all of these changes in thylakoid protein phosphorylation, the relative excitation of PSII and PSI (i.e. the absorption cross section of PSII and PSI measured by 77-K fluorescence) remains nearly unchanged upon changes in white-light intensity (i.e. no state transitions can be observed despite massive differences in LHCII protein phosphorylation; Tikkanen et al., 2010).The existence of the opposing behaviors of PSII core and LHCII protein phosphorylation, as described above, has been known for more than 15 years (Rintamäki et al., 1997, 2000), but the physiological significance of this phenomenon has remained elusive. It is known that PSII core protein phosphorylation in high light (HL) facilitates the unpacking of PSII-LHCII complexes required for proper processing of the damaged PSII centers and thus, prevents oxidative damage of the photosynthetic machinery (Tikkanen et al., 2008a; Fristedt et al., 2009; Goral et al., 2010; Kirchhoff et al., 2011). It is also known that the damaged PSII core protein D1 needs to be dephosphorylated before its proteolytic degradation upon PSII turnover (Koivuniemi et al., 1995). There is, however, no coherent understanding available to explain why LHCII proteins are dephosphorylated upon exposure of plants to HL and PSII core proteins are dephosphorylated upon exposure to low light (LL).The above-described light quantity-dependent control of thylakoid protein phosphorylation drastically differs from the light quality-dependent protein phosphorylation (Tikkanen et al., 2010). State transitions are generally investigated by using different light qualities, preferentially exciting either PSI or PSII. State 1 light favors PSI excitation, leading to oxidation of the ETC and dephosphorylation of both the PSII core and LHCII proteins. State 2 light, in turn, preferentially excites PSII, leading to reduction of ETC and strong concomitant phosphorylation of both the PSII core and LHCII proteins (Haldrup et al., 2001). Shifts between states 1 and 2 lights induce state transitions, mechanisms that change the excitation between PSII and PSI (Murata and Sugahara, 1969; Murata, 2009). Similar to shifts between state lights, the shifts between LL and HL intensity also change the phosphorylation of the PSII core and LHCII proteins (Rintamäki et al., 1997, 2000). Importantly, the white-light intensity-induced changes in thylakoid protein phosphorylation do not change the excitation energy distribution between the two photosystems (Tikkanen et al., 2010). Despite this fundamental difference between the light quantity- and light quality-induced thylakoid protein phosphorylations, a common feature for both mechanisms is a strict requirement of LHCII phosphorylation for formation of the PSI-LHCII complex. However, it is worth noting that LHCII phosphorylation under state 2 light is not enough to induce the state 2 transition but that the P-LHCII docking proteins in the PSI complex are required (Lunde et al., 2000; Jensen et al., 2004; Zhang and Scheller, 2004; Leoni et al., 2013).Thylakoid protein phosphorylation is a dynamic redox-regulated process dependent on the interplay between two kinases (STN7 and STN8; Depège et al., 2003; Bellafiore et al., 2005; Bonardi et al., 2005; Vainonen et al., 2005) and two phosphatases (TAP38/PPH1 and PBCP; Pribil et al., 2010; Shapiguzov et al., 2010; Samol et al., 2012). Concerning the redox regulation mechanisms in vivo, only the LHCII kinase (STN7) has so far been thoroughly studied (Vener et al., 1997; Rintamäki et al., 2000; Lemeille et al., 2009). The STN7 kinase is considered as the LHCII kinase, and indeed, it phosphorylates the LHCB1 and LHCB2 proteins (Bellafiore et al., 2005; Bonardi et al., 2005; Tikkanen et al., 2006). In addition to this, STN7 takes part in the phosphorylation of PSII core proteins (Vainonen et al., 2005), especially in LL (Tikkanen et al., 2008b, 2010). The STN8 kinase is required for phosphorylation of PSII core proteins in HL but does not significantly participate in phosphorylation of LHCII (Bellafiore et al., 2005; Bonardi et al., 2005; Vainonen et al., 2005; Tikkanen et al., 2010). It has been shown that, in traditional state 1 condition, which oxidizes the ETC, the dephosphorylation of LHCII is dependent on TAP38/PPH1 phosphatase (Pribil et al., 2010; Shapiguzov et al., 2010), whereas the PSII core protein dephosphorylation is dependent on the PBCP phosphatase (Samol et al., 2012). However, it remains unresolved whether and how the TAP38/PPH1 and PBCP phosphatases are involved in the light intensity-dependent regulation of thylakoid protein phosphorylation typical for natural environments.Here, we have used the two kinase (stn7 and stn8) and the two phosphatase (tap38/pph1and pbcp) mutants of Arabidopsis (Arabidopsis thaliana) to elucidate the individual roles of these enzymes in reversible thylakoid protein phosphorylation and distribution of excitation energy between PSII and PSI upon changes in light intensity. It is shown that the TAP38/PPH1-dependent, redox-regulated LHCII dephosphorylation is the key component to maintain excitation balance between PSII and PSI upon increase in light intensity, which at the same time, induces strong phosphorylation of the PSII core proteins. Collectively, reversible but opposite phosphorylation and dephosphorylation of the PSII core and LHCII proteins upon increase or decrease in light intensity are shown to be crucial for maintenance of even distribution of excitation energy to both photosystems, thus preventing state transitions. Moreover, evidence is provided indicating that the pH gradient across the thylakoid membrane is yet another important component in regulation of the distribution of excitation energy to PSII and PSI, possibly by affecting the regulation of thylakoid kinases and phosphatases.     

Cavitation Resistance in Seedless Vascular Plants: The Structure and Function of Interconduit Pit Membranes

Plant water transport occurs through interconnected xylem conduits that are separated by partially digested regions in the cell wall known as pit membranes. These structures have a dual function. Their porous construction facilitates water movement between conduits while limiting the spread of air that may enter the conduits and render them dysfunctional during a drought. Pit membranes have been well studied in woody plants, but very little is known about their function in more ancient lineages such as seedless vascular plants. Here, we examine the relationships between conduit air seeding, pit hydraulic resistance, and pit anatomy in 10 species of ferns (pteridophytes) and two lycophytes. Air seeding pressures ranged from 0.8 ± 0.15 MPa (mean ± sd) in the hydric fern Athyrium filix-femina to 4.9 ± 0.94 MPa in Psilotum nudum, an epiphytic species. Notably, a positive correlation was found between conduit pit area and vulnerability to air seeding, suggesting that the rare-pit hypothesis explains air seeding in early-diverging lineages much as it does in many angiosperms. Pit area resistance was variable but averaged 54.6 MPa s m⁻¹ across all surveyed pteridophytes. End walls contributed 52% to the overall transport resistance, similar to the 56% in angiosperm vessels and 64% in conifer tracheids. Taken together, our data imply that, irrespective of phylogenetic placement, selection acted on transport efficiency in seedless vascular plants and woody plants in equal measure by compensating for shorter conduits in tracheid-bearing plants with more permeable pit membranes.Water transport in plants occurs under tension, which renders the xylem susceptible to air entry. This air seeding may lead to the rupture of water columns (cavitation) such that the air expands within conduits to create air-vapor embolisms that block further transport. (Zimmermann and Tyree, 2002). Excessive embolism such as that which occurs during a drought may jeopardize leaf hydration and lead to stomatal closure, overheating, wilting, and possibly death of the plant (Hubbard et al., 2001; Choat et al., 2012; Schymanski et al., 2013). Consequently, strong selection pressure resulted in compartmentalized and redundant plant vascular networks that are adapted to a species habitat water availability by way of life history strategy (i.e. phenology) or resistance to air seeding (Tyree et al., 1994; Mencuccini et al., 2010; Brodersen et al., 2012). The spread of drought-induced embolism is limited primarily by pit membranes, which are permeable, mesh-like regions in the primary cell wall that connect two adjacent conduits. The construction of the pit membrane is such that water easily moves across the membrane between conduits, but because of the small membrane pore size and the presence of a surface coating on the membrane (Pesacreta et al., 2005; Lee et al., 2012), the spread of air and gas bubbles is restricted up to a certain pressure threshold known as the air-seeding pressure (ASP). When xylem sap tension exceeds the air-seeding threshold, air can be aspirated from an air-filled conduit into a functional water-filled conduit through perhaps a large, preexisting pore or one that is created by tension-induced membrane stress (Rockwell et al., 2014). Air seeding leads to cavitation and embolism formation, with emboli potentially propagating throughout the xylem network (Tyree and Sperry, 1988; Brodersen et al., 2013). So, on the one hand, pit membranes are critical to controlling the spread of air throughout the vascular network, while on the other hand, they must facilitate the efficient flow of water between conduits (Choat et al., 2008; Domec et al., 2008; Pittermann et al., 2010; Schulte, 2012). Much is known about such hydraulic tradeoffs in the pit membranes of woody plants, but comparatively little data exist on seedless vascular plants such as ferns and lycophytes. Given that seedless vascular plants may bridge the evolutionary transition from bryophytes to woody plants, the lack of functional data on pit membrane structure in early-derived tracheophytes is a major gap in our understanding of the evolution of plant water transport.In woody plants, pit membranes fall into one of two categories: the torus-margo type found in most gymnosperms and the homogenous pit membrane characteristic of angiosperms (Choat et al., 2008; Choat and Pittermann, 2009). In conifers, water moves from one tracheid to another through the margo region of the membrane, with the torus sealing the pit aperture should one conduit become embolized. Air seeding occurs when water potential in the functional conduit drops low enough to dislodge the torus from its sealing position, letting air pass through the pit aperture into the water-filled tracheid (Domec et al., 2006; Delzon et al., 2010; Pittermann et al., 2010; Schulte, 2012; but see Jansen et al., 2012). Across north-temperate conifer species, larger pit apertures correlate with lower pit resistance to water flow (r_pit; MPa s m⁻¹), but it is the ratio of torus-aperture overlap that sets a species cavitation resistance (Pittermann et al., 2006, 2010; Domec et al., 2008; Hacke and Jansen, 2009). A similar though mechanistically different tradeoff exists in angiosperm pit membranes. Here, air seeding reflects a probabilistic relationship between membrane porosity and the total area of pit membranes present in the vessel walls. Specifically, the likelihood of air aspirating into a functional conduit is determined by the combination of xylem water potential and the diameter of the largest pore and/or the weakest zone in the cellulose matrix in the vessel’s array of pit membranes (Wheeler et al., 2005; Hacke et al., 2006; Christman et al., 2009; Rockwell et al., 2014). As it has come to be known, the rare-pit hypothesis suggests that the infrequent, large-diameter leaky pore giving rise to that rare pit reflects some combination of pit membrane traits such as variation in conduit membrane area (large or small), membrane properties (tight or porous), and hydrogel membrane chemistry (Hargrave et al., 1994; Choat et al., 2003; Wheeler et al., 2005; Hacke et al., 2006; Christman et al., 2009; Lee et al., 2012; Plavcová et al., 2013; Rockwell et al., 2014). The maximum pore size is critical because, per the Young-Laplace law, the larger the radius of curvature, the lower the air-water pressure difference under which the contained meniscus will fail (Jarbeau et al., 1995; Choat et al., 2003; Jansen et al., 2009). Consequently, angiosperms adapted to drier habitats may exhibit thicker, denser, smaller, and less abundant pit membranes than plants occupying regions with higher water availability (Wheeler et al., 2005; Hacke et al., 2007; Jansen et al., 2009; Lens et al., 2011; Scholz et al., 2013). However, despite these qualitative observations, there is no evidence that increased cavitation resistance arrives at the cost of higher r_pit. Indeed, the bulk of the data suggest that prevailing pit membrane porosity is decoupled from the presence of the single largest pore that allows air seeding to occur (Choat et al., 2003; Wheeler et al., 2005 Hacke et al., 2006, 2007).As water moves from one conduit to another, pit membranes offer considerable hydraulic resistance throughout the xylem network. On average, r_pit contributes 64% and 56% to transport resistance in conifers and angiosperms, respectively (Wheeler et al., 2005; Pittermann et al., 2006; Sperry et al., 2006). In conifers, the average r_pit is estimated at 6 ± 1 MPa s m⁻¹, almost 60 times lower than the 336 ± 81 MPa s m⁻¹ computed for angiosperms (Wheeler et al., 2005; Hacke et al., 2006; Sperry et al., 2006). Presumably, the high porosity of conifer pits compensates for the higher transport resistance offered by a vascular system composed of narrow, short, single-celled conduits (Pittermann et al., 2005; Sperry et al., 2006).Transport in seedless vascular plants presents an interesting conundrum because, with the exception of a handful of species, their primary xylem is composed of tracheids, the walls of which are occupied by homogenous pit membranes (Gibson et al., 1985; Carlquist and Schneider, 2001, 2007; but see Morrow and Dute, 1998, for torus-margo membranes in Botrychium spp.). At first pass, this combination of traits appears hydraulically maladaptive, but several studies have shown that ferns can exhibit transport capacities that are on par with more recently evolved plants (Wheeler et al., 2005; Watkins et al., 2010; Pittermann et al., 2011, 2013; Brodersen et al., 2012). Certainly, several taxa possess large-diameter, highly overlapping conduits, some even have vessels such as Pteridium aquilinum and many species have high conduit density, all of which could contribute to increased hydraulic efficiency (Wheeler et al., 2005; Pittermann et al., 2011, 2013). But how do the pit membranes of seedless vascular plants compare? Scanning electron micrographs of fern and lycopod xylem conduits suggest that they are thin, diaphanous, and susceptible to damage during specimen preparation (Carlquist and Schneider 2001, 2007). Consistent with such observations, two estimates of r_pit imply that r_pit in ferns may be significantly lower than in angiosperms; Wheeler et al. (2005) calculated r_pit in the fern Pteridium aquilinum at 31 MPa s m⁻¹, while Schulte et al. (1987) estimated r_pit at 1.99 MPa s m⁻¹ in the basal fern Psilotum nudum. The closest structural analogy to seedless vascular plant tracheids can be found in the secondary xylem of the early-derived vesselless angiosperms, in which tracheids possess homogenous pit membranes with r_pit values that at 16 MPa s m⁻¹ are marginally higher than those of conifers (Hacke et al., 2007). Given that xylem in seedless vascular plants is functionally similar to that in vesselless angiosperms, we expected convergent r_pit values in these two groups despite their phylogenetic distance. We tested this hypothesis, as well as the intrinsic cavitation resistance of conduits in seedless vascular plants, by scrutinizing the pit membranes of ferns and fern allies using the anatomical and experimental approaches applied previously to woody taxa. In particular, we focused on the relationship between pit membrane traits and cavitation resistance at the level of the individual conduit.     

Patterns of Metabolite Changes Identified from Large-Scale Gene Perturbations in Arabidopsis Using a Genome-Scale Metabolic Network

Metabolomics enables quantitative evaluation of metabolic changes caused by genetic or environmental perturbations. However, little is known about how perturbing a single gene changes the metabolic system as a whole and which network and functional properties are involved in this response. To answer this question, we investigated the metabolite profiles from 136 mutants with single gene perturbations of functionally diverse Arabidopsis (Arabidopsis thaliana) genes. Fewer than 10 metabolites were changed significantly relative to the wild type in most of the mutants, indicating that the metabolic network was robust to perturbations of single metabolic genes. These changed metabolites were closer to each other in a genome-scale metabolic network than expected by chance, supporting the notion that the genetic perturbations changed the network more locally than globally. Surprisingly, the changed metabolites were close to the perturbed reactions in only 30% of the mutants of the well-characterized genes. To determine the factors that contributed to the distance between the observed metabolic changes and the perturbation site in the network, we examined nine network and functional properties of the perturbed genes. Only the isozyme number affected the distance between the perturbed reactions and changed metabolites. This study revealed patterns of metabolic changes from large-scale gene perturbations and relationships between characteristics of the perturbed genes and metabolic changes.Rational and quantitative assessment of metabolic changes in response to genetic modification (GM) is an open question and in need of innovative solutions. Nontargeted metabolite profiling can detect thousands of compounds, but it is not easy to understand the significance of the changed metabolites in the biochemical and biological context of the organism. To better assess the changes in metabolites from nontargeted metabolomics studies, it is important to examine the changed metabolites in the context of the genome-scale metabolic network of the organism.Metabolomics is a technique that aims to quantify all the metabolites in a biological system (Nikolau and Wurtele, 2007; Nicholson and Lindon, 2008; Roessner and Bowne, 2009). It has been used widely in studies ranging from disease diagnosis (Holmes et al., 2008; DeBerardinis and Thompson, 2012) and drug discovery (Cascante et al., 2002; Kell, 2006) to metabolic reconstruction (Feist et al., 2009; Kim et al., 2012) and metabolic engineering (Keasling, 2010; Lee et al., 2011). Metabolomic studies have demonstrated the possibility of identifying gene functions from changes in the relative concentrations of metabolites (metabotypes or metabolic signatures; Ebbels et al., 2004) in various species including yeast (Saccharomyces cerevisiae; Raamsdonk et al., 2001; Allen et al., 2003), Arabidopsis (Arabidopsis thaliana; Brotman et al., 2011), tomato (Solanum lycopersicum; Schauer et al., 2006), and maize (Zea mays; Riedelsheimer et al., 2012). Metabolomics has also been used to better understand how plants interact with their environments (Field and Lake, 2011), including their responses to biotic and abiotic stresses (Dixon et al., 2006; Arbona et al., 2013), and to predict important agronomic traits (Riedelsheimer et al., 2012). Metabolite profiling has been performed on many plant species, including angiosperms such as Arabidopsis, poplar (Populus trichocarpa), and Catharanthus roseus (Sumner et al., 2003; Rischer et al., 2006), basal land plants such as Selaginella moellendorffii and Physcomitrella patens (Erxleben et al., 2012; Yobi et al., 2012), and Chlamydomonas reinhardtii (Fernie et al., 2012; Davis et al., 2013). With the availability of whole genome sequences of various species, metabolomics has the potential to become a useful tool for elucidating the functions of genes using large-scale systematic analyses (Fiehn et al., 2000; Saito and Matsuda, 2010; Hur et al., 2013).Although metabolomics data have the potential for identifying the roles of genes that are associated with metabolic phenotypes, the biochemical mechanisms that link functions of genes with metabolic phenotypes are still poorly characterized. For example, we do not yet know the principles behind how perturbing the expression of a single gene changes the metabolic system as a whole. Large-scale metabolomics data have provided useful resources for linking phenotypes to genotypes (Fiehn et al., 2000; Roessner et al., 2001; Tikunov et al., 2005; Schauer et al., 2006; Lu et al., 2011; Fukushima et al., 2014). For example, Lu et al. (2011) compared morphological and metabolic phenotypes from more than 5,000 Arabidopsis chloroplast mutants using gas chromatography (GC)- and liquid chromatography (LC)-mass spectrometry (MS). Fukushima et al. (2014) generated metabolite profiles from various characterized and uncharacterized mutant plants and clustered the mutants with similar metabolic phenotypes by conducting multidimensional scaling with quantified metabolic phenotypes. Nonetheless, representation and analysis of such a large amount of data remains a challenge for scientific discovery (Lu et al., 2011). In addition, these studies do not examine the topological and functional characteristics of metabolic changes in the context of a genome-scale metabolic network. To understand the relationship between genotype and metabolic phenotype, we need to investigate the metabolic changes caused by perturbing the expression of a gene in a genome-scale metabolic network perspective, because metabolic pathways are not independent biochemical factories but are components of a complex network (Berg et al., 2002; Merico et al., 2009).Much progress has been made in the last 2 decades to represent metabolism at a genome scale (Terzer et al., 2009). The advances in genome sequencing and emerging fields such as biocuration and bioinformatics enabled the representation of genome-scale metabolic network reconstructions for model organisms (Bassel et al., 2012). Genome-scale metabolic models have been built and applied broadly from microbes to plants. The first step toward modeling a genome-scale metabolism in a plant species started with developing a genome-scale metabolic pathway database for Arabidopsis (AraCyc; Mueller et al., 2003) from reference pathway databases (Kanehisa and Goto, 2000; Karp et al., 2002; Zhang et al., 2010). Genome-scale metabolic pathway databases have been built for several plant species (Mueller et al., 2005; Zhang et al., 2005, 2010; Urbanczyk-Wochniak and Sumner, 2007; May et al., 2009; Dharmawardhana et al., 2013; Monaco et al., 2013, 2014; Van Moerkercke et al., 2013; Chae et al., 2014; Jung et al., 2014). Efforts have been made to develop predictive genome-scale metabolic models using enzyme kinetics and stoichiometric flux-balance approaches (Sweetlove et al., 2008). de Oliveira Dal’Molin et al. (2010) developed a genome-scale metabolic model for Arabidopsis and successfully validated the model by predicting the classical photorespiratory cycle as well as known key differences between redox metabolism in photosynthetic and nonphotosynthetic plant cells. Other genome-scale models have been developed for Arabidopsis (Poolman et al., 2009; Radrich et al., 2010; Mintz-Oron et al., 2012), C. reinhardtii (Chang et al., 2011; Dal’Molin et al., 2011), maize (Dal’Molin et al., 2010; Saha et al., 2011), sorghum (Sorghum bicolor; Dal’Molin et al., 2010), and sugarcane (Saccharum officinarum; Dal’Molin et al., 2010). These predictive models have the potential to be applied broadly in fields such as metabolic engineering, drug target discovery, identification of gene function, study of evolutionary processes, risk assessment of genetically modified crops, and interpretations of mutant phenotypes (Feist and Palsson, 2008; Ricroch et al., 2011).Here, we interrogate the metabotypes caused by 136 single gene perturbations of Arabidopsis by analyzing the relative concentration changes of 1,348 chemically identified metabolites using a reconstructed genome-scale metabolic network. We examine the characteristics of the changed metabolites (the metabolites whose relative concentrations were significantly different in mutants relative to the wild type) in the metabolic network to uncover biological and topological consequences of the perturbed genes.     

Cell biology in neuroscience: Death of developing neurons: New insights and implications for connectivity

The concept that target tissues determine the survival of neurons has inspired much of the thinking on neuronal development in vertebrates, not least because it is supported by decades of research on nerve growth factor (NGF) in the peripheral nervous system (PNS). Recent discoveries now help to understand why only some developing neurons selectively depend on NGF. They also indicate that the survival of most neurons in the central nervous system (CNS) is not simply regulated by single growth factors like in the PNS. Additionally, components of the cell death machinery have begun to be recognized as regulators of selective axonal degeneration and synaptic function, thus playing a critical role in wiring up the nervous system.

Why do so many neurons die during development?

Programmed cell death occurs throughout life, as cell turnover is part of homeostasis and maintenance in most organs and tissues. The situation in the nervous system is principally different, as the vast majority of neurons undergo their last round of cell division early in development. Soon after exiting the cell cycle, neurons start elongating axons to innervate their targets. It is during this period that they are highly susceptible to undergo programmed cell death: a large percentage, as much as 50% in several ganglia in the peripheral nervous system (PNS) as well as in various central nervous system (CNS) areas, is eliminated around the time that connections are being made with other cells. Later in development, the propensity of neurons to initiate apoptosis progressively decreases. The likelihood for a neuron to undergo apoptosis seems to be determined by a tightly regulated apoptotic machinery (summarized in Fig. 1). Therefore, modulation of the expression levels or the activity of components of this apoptotic balance changes the sensitivity to death-promoting cues, allowing temporal restriction of cell death.Open in a separate windowFigure 1.Core components of the apoptotic machinery. The likelihood that a neuron undergoes apoptosis is determined by the interplay of the tightly interlinked apoptotic machinery, many components of which are highly conserved between species. The critical, and often terminal, step in programmed cell death is the proteolytic activation of the executor caspases (such as caspase 3, 6, 7) by the initiator caspases (i.e., caspase 8, 9, and 10; Riedl and Salvesen, 2007). In mammalian cells, initiation of the executor caspases is regulated by two distinct protein cascades: the intrinsic pathway, also known as the mitochondrial pathway, and the extrinsic pathway. The intrinsic pathway integrates a number of intra- and extracellular signal modalities, such as redox state (for example, the reactive oxygen species; Franklin, 2011), DNA damage (Sperka et al., 2012), ER stress (Puthalakath et al., 2007) and growth factor deprivation (Deckwerth et al., 1998; Putcha et al., 2003; Bredesen et al., 2005), or activation of the p75NTR neurotrophin receptor by pro-neurotrophins (Nykjaer et al., 2005). The stressors converge onto pro- and anti-apoptotic members of the Bcl-2 protein family (for example: BCL-2, BCL-X_l, BAX, and tBID; Youle and Strasser, 2008). These proteins regulate the release of cytochrome c from mitochondria, which activates the initiator caspase 9 through Apaf1 (Riedl and Salvesen, 2007). The extrinsic pathway links activation of ligand-bound death receptors (such as Fas/CD95 and TNFR) to the initiator caspase 8 and 10, through formation of the death-inducing signaling complex (DISC; LeBlanc and Ashkenazi, 2003; Peter and Krammer, 2003). Together with additional regulatory elements (including the Inhibitors of apoptosis proteins [IAP]; Vaux and Silke, 2005) and cFLIP (Scaffidi et al., 1999; Wang et al., 2005), the apoptotic machinery forms a balance that determines the propensity of the neuron to undergo apoptosis.Programmed cell death eliminates many neurons during development, even in organisms comprised of only few cells, such as Caenorhabditis elegans. As neurons and their targets are initially separated, it is possible that the initial generation of an overabundance of neurons is simply part of a mechanism to ensure that distal targets are adequately innervated (Oppenheim, 1991; Conradt, 2009; Chen et al., 2013). In various tissues other than the nervous system, programmed cell death is used to eliminate cells that are no longer needed, defective, or harmful to the function of the organism. However, there is strong evidence that the elimination of superfluous neurons in the developing nervous system is not essential. For example, early work in C. elegans revealed that preventing programmed cell death does not result in significant behavioral alterations (Ellis and Horvitz, 1986). In the C57BL/6 mouse strain, deletion of the executor caspases 3 and 7 (Fig. 1) has a remarkably limited neuropathological and morphological impact in the CNS (Leonard et al., 2002; Lakhani et al., 2006) compared with the 129X1/SvJ strain, in which deletion of these caspases causes neurodevelopmental defects (Leonard et al., 2002). Similar conclusions were reached by blocking the Bcl-2–associated X protein (BAX)–dependent pathway in many neuronal populations, including motoneurons (Buss et al., 2006a). A recent study in the developing retina showed that in mice lacking the central apoptotic regulator BAX, the normal mosaic distribution of intrinsically photosensitive retinal ganglion cells (ipRGCs) was perturbed (Chen et al., 2013). Although this abnormal distribution is dispensable for the intrinsic photosensitivity of the ipRGCs, it is required for establishing proper connections to other neurons in the retina, which is necessary for rod/cone photo-entrainment (Chen et al., 2013). Even though this finding highlights a physiological role for programmed cell death in the CNS, the functional consequences remain rather underwhelming in the face of a process that eliminates such large numbers of neurons (Purves and Lichtman, 1984; Oppenheim, 1991; Miller, 1995; Gohlke et al., 2004). It thus appears that apoptotic removal of the surplus neurons generated during development mainly serves the purpose to optimize the size of the nervous system to be minimal, but sufficient.

A molecular substrate for the neurotrophic theory

Quantitatively, programmed cell death of neurons in the PNS and CNS is most dramatic when neurons start contacting the cells they innervate. Because experimental manipulations such as target excision typically lead to the death of essentially all innervating neurons (Oppenheim, 1991), the concept emerged that the fate of developing neurons is regulated by their targets. This notion is often referred to as the “neurotrophic theory” (Hamburger et al., 1981; Purves and Lichtman, 1984; Oppenheim, 1991), but it is important to realize that it evolved in the absence of direct mechanistic or molecular support (Purves, 1988). Originally described as a diffusible agent promoting nerve growth, the eponymous NGF later provided a strong and very appealing molecular foundation for this theory (Korsching and Thoenen, 1983; Edwards et al., 1989; Hamburger, 1992). The tyrosine kinase receptor tropomyosin receptor kinase A (TrkA), which was initially identified as an oncogene (Martin-Zanca et al., 1986), was fortuitously discovered to be the critical receptor necessary for the prevention of neuronal cell death by NGF (Klein et al., 1991). Both the remarkable expression pattern of TrkA in NGF-dependent neurons and the onset of its expression during development (Martin-Zanca et al., 1990) provided further additional support for the neurotrophic theory. However, for a surprisingly long time, the question was not asked as to why only specific populations of neurons strictly depend on NGF for survival, while others do not. Indeed, it was only recently shown that TrkA causes cell death of neurons by virtue of its mere expression, and that this death-inducing activity is prevented by addition of NGF (Nikoletopoulou et al., 2010). These findings thus indicate that TrkA acts as a “dependence receptor,” a concept introduced after observations that various cell types die when receptors are expressed in the absence of their cognate ligands (Bredesen et al., 2005; Tauszig-Delamasure et al., 2007). Accordingly, embryonic mouse sympathetic or sensory neurons survive in the absence of NGF when TrkA is deleted (Nikoletopoulou et al., 2010). The closely related neurotrophin receptor TrkC also acts as a dependence receptor (Tauszig-Delamasure et al., 2007; Nikoletopoulou et al., 2010). Here, it is interesting to note a series of older, convergent results indicating that deletion of neurotrophin-3 (NT3), the TrkC ligand, leads to a significantly larger loss of sensory and sympathetic neurons in the PNS than the deletion of TrkC (Tessarollo et al., 1997). This phenotypic discrepancy fits well with the idea that inactivation of the ligand of a dependence receptor is expected to yield a more profound phenotype than inactivation of the receptor itself (Tauszig-Delamasure et al., 2007). How TrkA and TrkC induce apoptosis remains to be fully elucidated. It seems that proteolysis is involved, either of TrkC itself (Tauszig-Delamasure et al., 2007), as was suggested for other dependence receptors (Bredesen et al., 2005), or by the proteolysis of the neurotrophin receptor p75NTR, which associates with both TrkA and TrkC (Fig. 2; Nikoletopoulou et al., 2010). Surprisingly, although TrkA and TrkC cause cell death, the structurally related TrkB receptor does not (Nikoletopoulou et al., 2010), a difference that appears to be accounted for by their differential localization in the cell membrane. TrkA and TrkC colocalize with p75NTR in lipid rafts, whereas TrkB, which also associates with p75NTR (Bibel et al., 1999), is excluded from lipid rafts (Fig. 2; unpublished data). Interestingly, the transmembrane domains of TrkA and TrkC are closely related, and differ clearly from that of TrkB. It turns out that a chimeric protein of TrkB with the transmembrane domain of TrkA causes cell death, which can be prevented by the addition of the TrkB ligand brain-derived neurotrophic factor (BDNF; unpublished data). The suggestion that the lipid raft localization of TrkA and TrkC is important for their death-inducing function is in line with a number of reports indicating that certain apoptotic proteins preferentially localize in lipid rafts in the plasma membrane. After activation of the extrinsic apoptosis pathway, translocation of the activated receptors to lipid rafts in the membrane is required for assembling the death-inducing signaling complex (DISC; Davis et al., 2007; Song et al., 2007). Indeed, regulators of the extrinsic pathway (e.g., cFLIP; Fig. 1) prevent this translocation, explaining how they attenuate cell death induction (Song et al., 2007). Similarly, the localization of the dependence receptor DCC (deleted in colorectal cancer) in lipid rafts is a prerequisite for its pro-apoptotic activity in absence of its ligand, Netrin-1 (Furne et al., 2006).Open in a separate windowFigure 2.TrkA and TrkC as dependence receptors: mode of action and contrast with TrkB. All Trk receptors associate with the pan-neurotrophin receptor p75NTR (Bibel et al., 1999). A critical step in the induction of apoptosis by TrkA is the release of the intracellular death domain of p75NTR by the protease γ-secretase (Nikoletopoulou et al., 2010), which is localized in lipid rafts (Urano et al., 2005). Our membrane fractionation studies indicate that while TrkA and TrkC associate with p75NTR in lipid rafts, TrkB associated with p75NTR is excluded from this membrane domain (unpublished data). The 24–amino acid transmembrane domain of the Trk receptors may be responsible for this differential localization (see text).Despite the fact that TrkB does not act as a dependence receptor, its activation by BDNF is required for the survival of several populations of cranial sensory neurons (Ernfors et al., 1995; Liu et al., 1995). It appears that other death-inducing receptors predispose these neurons to be eliminated, such as p75NTR, which is expressed at high levels in some of these ganglia, or TrkC in vestibular neurons (Stenqvist et al., 2005). This latter case is of special interest, as NT3 is known not to be required for the survival of these neurons (Stenqvist et al., 2005). In addition to inducing apoptosis in the absence of their ligand, TrkA and TrkC have long been recognized to have a pro-survival function similar to TrkB, as can be inferred from the loss of specific populations of peripheral sensory neurons in mutants lacking these receptors (Klein et al., 1994; Smeyne et al., 1994).

Cell death in the CNS

Although TrkA is primarily expressed in peripheral sympathetic and sensory neurons, it is also found in a small population of cholinergic neurons in the basal forebrain (Sobreviela et al., 1994), a proportion of which requires NGF for survival (Hartikka and Hefti, 1988; Crowley et al., 1994; Müller et al., 2012). Selective deletion of TrkA was recently shown not to cause the death of these neurons (Sanchez-Ortiz et al., 2012). This supports the notion that TrkA acts as a dependence receptor for this small population of CNS neurons, like for peripheral sensory and sympathetic neurons. TrkA activation by NGF is essential for the maturation, projections, and function of these neurons (Sanchez-Ortiz et al., 2012), as was previously described for sensory neurons in the PNS as well (Patel et al., 2000).Whether or not receptors other than TrkA act as dependence receptors in the CNS is an important open question, particularly because TrkB, which is expressed highly by most CNS neurons, does not act as a dependence receptor (Nikoletopoulou et al., 2010). In retrospect, the structural similarities between TrkA and TrkB, just like those between NGF and BDNF (Barde, 1989), have substantially misled the field by suggesting that BDNF would act in the CNS like NGF in the PNS. Adding to the confusion were early findings showing that BDNF supports the growth of spinal cord motoneurons in vitro or in vivo after axotomy (Oppenheim et al., 1992; Sendtner et al., 1992; Yan et al., 1992). However, in the absence of lesion, deletion of BDNF does not lead to significant losses of neurons in the developing or adult CNS (Ernfors et al., 1994a; Jones et al., 1994; Rauskolb et al., 2010), unlike the case in some populations of PNS neurons. The poor correlation of the role of BDNF in CNS development and in axotomy and in vitro experiments is surprising, especially because the role of NGF in vivo could in essence be recapitulated by in vitro experiments. Although the reasons for this discrepancy are not fully understood, the strong up-regulation of death-inducing molecules such as p75NTR after axotomy (Ernfors et al., 1989) may be a part of the explanation. At present, most of the growth factors promoting the survival of PNS neurons fail to show significant survival properties for developing neurons in the CNS, as for example was shown for NT3 (Ernfors et al., 1994b; Fariñas et al., 1994), glial cell line–derived neurotrophic factor (GDNF; Henderson et al., 1994), ciliary neurotrophic factor (CNTF; DeChiara et al., 1995), and several others.In the developing CNS, neuronal activity and neurotransmitter input seem to play a more significant role than single growth factors in regulating neuronal survival. In particular, it has been known for a long time that blocking synaptic transmission at the neuromuscular junction has a pro-survival effect on spinal cord motoneurons (Pittman and Oppenheim, 1978; Oppenheim et al., 2008). By contrast, surgical denervation of afferent connections leads to increased apoptosis of postsynaptic neurons (Okado and Oppenheim, 1984), whereas inhibiting glycinergic and GABAergic synaptic transmission has both pro- and anti-apoptotic effects on motoneurons (Banks et al., 2005). Throughout the developing brain, blocking glutamate-mediated synaptic transmission involving NMDA receptors markedly increases normally occurring neuronal death (Ikonomidou et al., 1999; Heck et al., 2008). The mechanism involves a reduction of neuronal expression of anti-apoptotic proteins, such as B-cell lymphoma 2 (BCL-2; Hansen et al., 2004). Conversely, a limited increase in neuronal activity leads to down-regulation of the pro-apoptotic genes BAX and caspase 9 (Léveillé et al., 2010), thereby reducing the propensity of these cells to initiate programmed cell death (Hardingham et al., 2002). In addition to directly modulating the expression of apoptotic proteins, neuronal activity affects the expression of several secreted growth factors, such as BDNF (Hardingham et al., 2002; Hansen et al., 2004) and GDNF (Léveillé et al., 2010). So, even though BDNF is not a major survival factor in the developing CNS, it appears to be critical for activity-dependent neuroprotection (Tremblay et al., 1999). A recent publication revealed that certain populations of neurons in the CNS do not follow the predictions of the neurotrophic theory and showed that apoptosis of cortical inhibitory neurons is independent of cues present in the developing cerebral cortex (Southwell et al., 2012). This study indicates that programmed cell death of a large proportion of interneurons in the CNS is regulated by intrinsic mechanisms that are largely resistant to the presence or absence of extrinsic cues (Dekkers and Barde, 2013).Taken together, even though the extent of naturally occurring cell death in the different regions of the CNS is not nearly as well characterized as in the PNS, let alone quantified, it appears that its regulation may significantly differ. Although single secreted neurotrophic factors seem to be largely dispensable for survival, neuronal activity and other intrinsic mechanisms drive the propensity of the neurons in the CNS to undergo apoptosis. An important open question in this context is a possible involvement of non-neuronal cells, such as glial cells (see Corty and Freeman, in this issue).

The apoptotic machinery as a regulator of connectivity

Activation of the executor caspases has been most studied in cell bodies and typically results in the demise of the entire cell (Williams et al., 2006). However, recent evidence shows that caspases are also activated locally in neuronal processes and branches destined to be eliminated, for example in axons overshooting their targets that are subsequently pruned back to establish the precise adult connectivity (Finn et al., 2000; Raff et al., 2002; Luo and O’Leary, 2005; Buss et al., 2006b). Initially, axonal degeneration and axon pruning were thought to be independent of caspases (Finn et al., 2000; Raff et al., 2002). Later work in Drosophila melanogaster (Kuo et al., 2006; Williams et al., 2006) and in mammalian neurons (Plachta et al., 2007; Nikolaev et al., 2009; Vohra et al., 2010) demonstrated that interfering with the apoptotic balance or the executor caspases can prevent or at least delay axonal degeneration. Simon et al. (2012) have found that a caspase 9 to caspase 3 cascade is crucial for axonal degeneration induced by NGF withdrawal, with caspase 6 activation playing a significant but subsidiary role. Upstream of the caspases, BCL-2 family members such as BAX and BCL-X_l are required (Nikolaev et al., 2009; Vohra et al., 2010). It is conceivable that the failure of ipRGCs in BAX-deficient mice to form appropriate connections to other cells in the retina (Chen et al., 2013) may be in part attributable to defective axonal degeneration. Surprisingly, Apaf1 appears not to be involved in this process (Cusack et al., 2013), suggesting that axon degeneration depends on the concerted activation of the intrinsic initiator complex in a different way from apoptosis.Strikingly, a series of recent studies showed that several caspases and components of the intrinsic pathway also affect normal synaptic physiology in adulthood (Fig. 3, A–D). Here, pro-apoptotic proteins are predominantly involved in weakening the synapses, whereas the anti-apoptotic proteins have been mainly associated with synaptic strengthening (Fig. 3 B). In particular, caspase 3 promotes long-term depression (LTD), a stimulation paradigm that results in a period of decreased synaptic transmission (Li et al., 2010), and also prevents long-term potentiation (LTP), the converse situation leading to strengthened synaptic transmission (Jo et al., 2011). Likewise, the proapoptotic BCL-2 family members BAX and BAD stimulate LTD (Jiao and Li, 2011). By contrast, the anti-apoptotic protein BCL-X_l increases synapse numbers and strength (H. Li et al., 2008), and the inhibitor of apoptosis protein (IAP) family member survivin was reported to be involved in LTP in the hippocampus (Iscru et al., 2013) and in activity-dependent gene regulation (O’Riordan et al., 2008).Open in a separate windowFigure 3.Canonical and noncanonical functions of the apoptotic machinery. (A) The apoptotic machinery is not only involved in eliminating cells destined to die, but is also a central player in refining neuronal connectivity, by regulating synaptic transmission and by generating the adult connectivity through axon pruning (Luo and O’Leary, 2005; Hyman and Yuan, 2012). But how the canonical and noncanonical roles of the apoptotic machinery are interlinked and spatially restricted is not well understood. (B) In the adult nervous system, the pro-apoptotic proteins BAX, caspase 9, and caspase 3 promote weakening of synapses (long-term depression [LTD]; Li et al., 2010; Jiao and Li, 2011; Jo et al., 2011), while the anti-apoptotic proteins Bcl-X_l and the IAP survivin promote synaptic strengthening (long-term potentiation [LTP]; Li et al., 2008a; Iscru et al., 2013). It is unclear how the activation of these pathways is restricted to a single synapse, but a recent review suggested that the proteasomal degradation of activated caspases may prevent their diffusion (Hyman and Yuan, 2012). (C) Caspase activation is now known to be required for axon pruning during development to generate the adult refined connectivity (Luo and O’Leary, 2005; Simon et al., 2012). Different pathways are activated depending on the stimulus leading to degeneration. Growth factor deprivation during development leads to activation the executor caspases 3 and 6 (Simon et al., 2012) through the intrinsic apoptotic pathway, although its core protein Apaf1 does not seem to be required for this process (Cusack et al., 2013). On the other hand, a traumatic injury leads to reduced influx of NMNAT2 into the axon, which negatively affects the stability and function of mitochondria and leads to an increased calcium concentration (Wang et al., 2012). The effector caspase, caspase 6, is dispensable for this form of axonal degeneration (Vohra et al., 2010; Simon et al., 2012). Regulatory proteins such as the IAPs and also the proteasome seem to play a role in limiting the extent of activation to the degenerating part of the axon (Wang et al., 2012; Cusack et al., 2013; Unsain et al., 2013). (D) Simplified schematic of the main pro- and anti-apoptotic components. DISC, death-induced signaling complex. IAP, inhibitor of apoptosis protein. See Fig. 1 for details.These findings indicate that the apoptotic machinery acts at different levels in the cell, ranging from driving sub-lethal degradation of a compartment (Fig. 3 C) and attenuating synaptic transmission at the neuronal network level (Fig. 3 B) to destroying the entire cell during development or in disease (Fig. 3 D). How the cell spatially restricts the extent of activation of the apoptotic machinery is yet unclear. For example, elimination of the somata of developing neurons after neurotrophin deprivation is preceded by axonal degeneration, but not all instances of axonal degeneration lead to the death of the neuron (Campenot, 1977; Raff et al., 2002). Local regulation of caspase activation by IAPs is well established as a means for ensuring the elimination of neuronal processes in D. melanogaster (Kuo et al., 2006; Williams et al., 2006). Recent findings suggest a similar role for IAP in mammalian neurons, where it limits caspase activation to the degenerating axon (Fig. 3 C; Cusack et al., 2013; Unsain et al., 2013). The spontaneous mutation Wallerian degeneration slow (WldS; Lunn et al., 1989) has been instrumental to understand that trauma-induced axon degeneration is a regulated process different from, and independent of, cell body degeneration (Wang et al., 2012), but also distinct from axon pruning (Hoopfer et al., 2006). Work on the chimeric protein encoded by the WldS mutation also led to the identification of the protein NMNAT2 (nicotinamide mononucleotide adenylyltransferase 2) as a labile axon survival factor (Gilley and Coleman, 2010). How the WldS chimeric protein and NMNAT2 result in axon protection is unclear, but several lines of evidence seem to converge on local regulation of mitochondrial function and motility (Avery et al., 2012; Fang et al., 2012).Related to the spatial limiting of apoptotic activity is the question of how a local source of neurotrophins leads to the rescue of a developing peripheral neuron. When neurons encounter a source of neurotrophins, only the receptors close to the target will be activated, whereas the others, located further away, are not. The cell, therefore, needs to integrate a pro-survival signal from the activated receptors, and death-inducing signals from the nonactivated dependence receptors. The continued signaling of activated neurotrophin receptors that are retrogradely transported to the soma (Grimes et al., 1996; Howe et al., 2001; Wu et al., 2001; Harrington et al., 2011) likely play a role in counteracting the pro-apoptotic signaling proximal to the source of neurotrophins. It will be interesting to investigate whether similar mechanisms play a role in axon pruning and traumatic axon degeneration as well.

Programmed cell death in the adult brain

Most of the nervous system becomes post-mitotic early in development. In rodents, two brain areas retain the capacity to generate new neurons in the adult: the sub-ventricular zone, which generates neurons that migrate toward the olfactory bulb, and the sub-granular zone of the dentate gyrus of the hippocampus, where neurons are generated that integrate locally. Similar to what is observed during embryonic development, these adult-generated neurons are produced in excess, and a large fraction undergoes apoptosis when contacting its designated targets (Petreanu and Alvarez-Buylla, 2002; Kempermann et al., 2003; Ninkovic et al., 2007). Preventing apoptosis of adult-generated neurons in the olfactory bulb only has limited functional consequences (Kim et al., 2007), whereas a similar maneuver in the dentate gyrus does lead to impaired performance in memory tasks (Kim et al., 2009). Why superfluous hippocampal neurons would need to be eliminated for proper function is a matter of speculation, but may be linked with the fact that these are excitatory projection neurons, whereas in the olfactory bulb only axon-less inhibitory granule cells are integrated. The extent of survival in both these areas critically depends on the activity of the neuronal network in which these newly born neurons have to integrate (Petreanu and Alvarez-Buylla, 2002; Kempermann et al., 2006; Ninkovic et al., 2007). In this context, BDNF, the expression level of which is well known to be regulated by network activity, supports the survival of young adult–generated neurons and possibly even stimulates the proliferation of neural progenitors (Y. Li et al., 2008; Waterhouse et al., 2012). Interestingly, in young adult mouse mutants that exhibit spontaneous epileptic seizures, significantly higher levels of BDNF have been measured (Lavebratt et al., 2006; Heyden et al., 2011). Concomitantly, the entire hippocampal formation is considerably enlarged by as much as 40% (Lavebratt et al., 2006; Angenstein et al., 2007), which in turn is dependent on the epileptic seizures (Lavebratt et al., 2006). Whether or not there is a causal relationship between increased BDNF levels and hippocampal volume remains to be established.

Conclusion

Now that is has become clear that action of the apoptotic machinery can be limited spatially and temporally, several questions need to be addressed: how do neurons integrate intrinsic and extrinsic pro- and anti-apoptotic signals; and how they are spatially restricted to allow degradation of a dendrite or axon, or modulation of synaptic transmission? Another important issue is the regulation of cell death by intrinsic mechanisms in the central nervous system of vertebrates, not least because programmed cell death is observed in the CNS in a number of neurodegenerative diseases (Vila and Przedborski, 2003). Indeed, several of the central apoptotic components discussed here are also involved in these disorders (Hyman and Yuan, 2012). New insights in the regulation of programmed cell death in the developing nervous system may therefore continue to help to better understand the pathophysiological mechanisms of neurodegenerative disorders.