Calcineurin B-Like Protein-Interacting Protein Kinase CIPK21 Regulates Osmotic and Salt Stress Responses in Arabidopsis

The role of calcium-mediated signaling has been extensively studied in plant responses to abiotic stress signals. Calcineurin B-like proteins (CBLs) and CBL-interacting protein kinases (CIPKs) constitute a complex signaling network acting in diverse plant stress responses. Osmotic stress imposed by soil salinity and drought is a major abiotic stress that impedes plant growth and development and involves calcium-signaling processes. In this study, we report the functional analysis of CIPK21, an Arabidopsis (Arabidopsis thaliana) CBL-interacting protein kinase, ubiquitously expressed in plant tissues and up-regulated under multiple abiotic stress conditions. The growth of a loss-of-function mutant of CIPK21, cipk21, was hypersensitive to high salt and osmotic stress conditions. The calcium sensors CBL2 and CBL3 were found to physically interact with CIPK21 and target this kinase to the tonoplast. Moreover, preferential localization of CIPK21 to the tonoplast was detected under salt stress condition when coexpressed with CBL2 or CBL3. These findings suggest that CIPK21 mediates responses to salt stress condition in Arabidopsis, at least in part, by regulating ion and water homeostasis across the vacuolar membranes.Drought and salinity cause osmotic stress in plants and severely affect crop productivity throughout the world. Plants respond to osmotic stress by changing a number of cellular processes (Xiong et al., 1999; Xiong and Zhu, 2002; Bartels and Sunkar, 2005; Boudsocq and Lauriére, 2005). Some of these changes include activation of stress-responsive genes, regulation of membrane transport at both plasma membrane (PM) and vacuolar membrane (tonoplast) to maintain water and ionic homeostasis, and metabolic changes to produce compatible osmolytes such as Pro (Stewart and Lee, 1974; Krasensky and Jonak, 2012). It has been well established that a specific calcium (Ca²⁺) signature is generated in response to a particular environmental stimulus (Trewavas and Malhó, 1998; Scrase-Field and Knight, 2003; Luan, 2009; Kudla et al., 2010). The Ca²⁺ changes are primarily perceived by several Ca²⁺ sensors such as calmodulin (Reddy, 2001; Luan et al., 2002), Ca²⁺-dependent protein kinases (Harper and Harmon, 2005), calcineurin B-like proteins (CBLs; Luan et al., 2002; Batistič and Kudla, 2004; Pandey, 2008; Luan, 2009; Sanyal et al., 2015), and other Ca²⁺-binding proteins (Reddy, 2001; Shao et al., 2008) to initiate various cellular responses.Plant CBL-type Ca²⁺ sensors interact with and activate CBL-interacting protein kinases (CIPKs) that phosphorylate downstream components to transduce Ca²⁺ signals (Liu et al., 2000; Luan et al., 2002; Batistič and Kudla, 2004; Luan, 2009). In several plant species, multiple members have been identified in the CBL and CIPK family (Luan et al., 2002; Kolukisaoglu et al., 2004; Pandey, 2008; Batistič and Kudla, 2009; Weinl and Kudla, 2009; Pandey et al., 2014). Involvement of specific CBL-CIPK pair to decode a particular type of signal entails the alternative and selective complex formation leading to stimulus-response coupling (D’Angelo et al., 2006; Batistič et al., 2010).Several CBL and CIPK family members have been implicated in plant responses to drought, salinity, and osmotic stress based on genetic analysis of Arabidopsis (Arabidopsis thaliana) mutants (Zhu, 2002; Cheong et al., 2003, 2007; Kim et al., 2003; Pandey et al., 2004, 2008; D’Angelo et al., 2006; Qin et al., 2008; Tripathi et al., 2009; Held et al., 2011; Tang et al., 2012; Drerup et al., 2013; Eckert et al., 2014). A few CIPKs have also been functionally characterized by gain-of-function approach in crop plants such as rice (Oryza sativa), pea (Pisum sativum), and maize (Zea mays) and were found to be involved in osmotic stress responses (Mahajan et al., 2006; Xiang et al., 2007; Yang et al., 2008; Tripathi et al., 2009; Zhao et al., 2009; Cuéllar et al., 2010).In this report, we examined the role of the Arabidopsis CIPK21 gene in osmotic stress response by reverse genetic analysis. The loss-of-function mutant plants became hypersensitive to salt and mannitol stress conditions, suggesting that CIPK21 is involved in the regulation of osmotic stress response in Arabidopsis. These findings are further supported by an enhanced tonoplast targeting of the cytoplasmic CIPK21 through interaction with the vacuolar Ca²⁺ sensors CBL2 and CBL3 under salt stress condition.     

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.     

Characterization of the Possible Roles for B Class MADS Box Genes in Regulation of Perianth Formation in Orchid

To investigate sepal/petal/lip formation in Oncidium Gower Ramsey, three paleoAPETALA3 genes, O. Gower Ramsey MADS box gene5 (OMADS5; clade 1), OMADS3 (clade 2), and OMADS9 (clade 3), and one PISTILLATA gene, OMADS8, were characterized. The OMADS8 and OMADS3 mRNAs were expressed in all four floral organs as well as in vegetative leaves. The OMADS9 mRNA was only strongly detected in petals and lips. The mRNA for OMADS5 was only strongly detected in sepals and petals and was significantly down-regulated in lip-like petals and lip-like sepals of peloric mutant flowers. This result revealed a possible negative role for OMADS5 in regulating lip formation. Yeast two-hybrid analysis indicated that OMADS5 formed homodimers and heterodimers with OMADS3 and OMADS9. OMADS8 only formed heterodimers with OMADS3, whereas OMADS3 and OMADS9 formed homodimers and heterodimers with each other. We proposed that sepal/petal/lip formation needs the presence of OMADS3/8 and/or OMADS9. The determination of the final organ identity for the sepal/petal/lip likely depended on the presence or absence of OMADS5. The presence of OMADS5 caused short sepal/petal formation. When OMADS5 was absent, cells could proliferate, resulting in the possible formation of large lips and the conversion of the sepal/petal into lips in peloric mutants. Further analysis indicated that only ectopic expression of OMADS8 but not OMADS5/9 caused the conversion of the sepal into an expanded petal-like structure in transgenic Arabidopsis (Arabidopsis thaliana) plants.The ABCDE model predicts the formation of any flower organ by the interaction of five classes of homeotic genes in plants (Yanofsky et al., 1990; Jack et al., 1992; Mandel et al., 1992; Goto and Meyerowitz, 1994; Jofuku et al., 1994; Pelaz et al., 2000, 2001; Theißen and Saedler, 2001; Pinyopich et al., 2003; Ditta et al., 2004; Jack, 2004). The A class genes control sepal formation. The A, B, and E class genes work together to regulate petal formation. The B, C, and E class genes control stamen formation. The C and E class genes work to regulate carpel formation, whereas the D class gene is involved in ovule development. MADS box genes seem to have a central role in flower development, because most ABCDE genes encode MADS box proteins (Coen and Meyerowitz, 1991; Weigel and Meyerowitz, 1994; Purugganan et al., 1995; Rounsley et al., 1995; Theißen and Saedler, 1995; Theißen et al., 2000; Theißen, 2001).The function of B group genes, such as APETALA3 (AP3) and PISTILLATA (PI), has been thought to have a major role in specifying petal and stamen development (Jack et al., 1992; Goto and Meyerowitz, 1994; Krizek and Meyerowitz, 1996; Kramer et al., 1998; Hernandez-Hernandez et al., 2007; Kanno et al., 2007; Whipple et al., 2007; Irish, 2009). In Arabidopsis (Arabidopsis thaliana), mutation in AP3 or PI caused identical phenotypes of second whorl petal conversion into a sepal structure and third flower whorl stamen into a carpel structure (Bowman et al., 1989; Jack et al., 1992; Goto and Meyerowitz, 1994). Similar homeotic conversions for petal and stamen were observed in the mutants of the AP3 and PI orthologs from a number of core eudicots such as Antirrhinum majus, Petunia hybrida, Gerbera hybrida, Solanum lycopersicum, and Nicotiana benthamiana (Sommer et al., 1990; Tröbner et al., 1992; Angenent et al., 1993; van der Krol et al., 1993; Yu et al., 1999; Liu et al., 2004; Vandenbussche et al., 2004; de Martino et al., 2006), from basal eudicot species such as Papaver somniferum and Aquilegia vulgaris (Drea et al., 2007; Kramer et al., 2007), as well as from monocot species such as Zea mays and Oryza sativa (Ambrose et al., 2000; Nagasawa et al., 2003; Prasad and Vijayraghavan, 2003; Yadav et al., 2007; Yao et al., 2008). This indicated that the function of the B class genes AP3 and PI is highly conserved during evolution.It has been thought that B group genes may have arisen from an ancestral gene through multiple gene duplication events (Doyle, 1994; Theißen et al., 1996, 2000; Purugganan, 1997; Kramer et al., 1998; Kramer and Irish, 1999; Lamb and Irish, 2003; Kim et al., 2004; Stellari et al., 2004; Zahn et al., 2005; Hernandez-Hernandez et al., 2007). In the gymnosperms, there was a single putative B class lineage that duplicated to generate the paleoAP3 and PI lineages in angiosperms (Kramer et al., 1998; Theißen et al., 2000; Irish, 2009). The paleoAP3 lineage is composed of AP3 orthologs identified in lower eudicots, magnolid dicots, and monocots (Kramer et al., 1998). Genes in this lineage contain the conserved paleoAP3- and PI-derived motifs in the C-terminal end of the proteins, which have been thought to be characteristics of the B class ancestral gene (Kramer et al., 1998; Tzeng and Yang, 2001; Hsu and Yang, 2002). The PI lineage is composed of PI orthologs that contain a highly conserved PI motif identified in most plant species (Kramer et al., 1998). Subsequently, there was a second duplication at the base of the core eudicots that produced the euAP3 and TM6 lineages, which have been subject to substantial sequence changes in eudicots during evolution (Kramer et al., 1998; Kramer and Irish, 1999). The paleoAP3 motif in the C-terminal end of the proteins was retained in the TM6 lineage and replaced by a conserved euAP3 motif in the euAP3 lineage of most eudicot species (Kramer et al., 1998). In addition, many lineage-specific duplications for paleoAP3 lineage have occurred in plants such as orchids (Hsu and Yang, 2002; Tsai et al., 2004; Kim et al., 2007; Mondragón-Palomino and Theißen, 2008, 2009; Mondragón-Palomino et al., 2009), Ranunculaceae, and Ranunculales (Kramer et al., 2003; Di Stilio et al., 2005; Shan et al., 2006; Kramer, 2009).Unlike the A or C class MADS box proteins, which form homodimers that regulate flower development, the ability of B class proteins to form homodimers has only been reported in gymnosperms and in the paleoAP3 and PI lineages of some monocots. For example, LMADS1 of the lily Lilium longiflorum (Tzeng and Yang, 2001), OMADS3 of the orchid Oncidium Gower Ramsey (Hsu and Yang, 2002), and PeMADS4 of the orchid Phalaenopsis equestris (Tsai et al., 2004) in the paleoAP3 lineage, LRGLOA and LRGLOB of the lily Lilium regale (Winter et al., 2002), TGGLO of the tulip Tulipa gesneriana (Kanno et al., 2003), and PeMADS6 of the orchid P. equestris (Tsai et al., 2005) in the PI lineage, and GGM2 of the gymnosperm Gnetum gnemon (Winter et al., 1999) were able to form homodimers that regulate flower development. Proteins in the euAP3 lineage and in most paleoAP3 lineages were not able to form homodimers and had to interact with PI to form heterodimers in order to regulate petal and stamen development in various plant species (Schwarz-Sommer et al., 1992; Tröbner et al., 1992; Riechmann et al., 1996; Moon et al., 1999; Winter et al., 2002; Kanno et al., 2003; Vandenbussche et al., 2004; Yao et al., 2008). In addition to forming dimers, AP3 and PI were able to interact with other MADS box proteins, such as SEPALLATA1 (SEP1), SEP2, and SEP3, to regulate petal and stamen development (Pelaz et al., 2000; Honma and Goto, 2001; Theißen and Saedler, 2001; Castillejo et al., 2005).Orchids are among the most important plants in the flower market around the world, and research on MADS box genes has been reported for several species of orchids during the past few years (Lu et al., 1993, 2007; Yu and Goh, 2000; Hsu and Yang, 2002; Yu et al., 2002; Hsu et al., 2003; Tsai et al., 2004, 2008; Xu et al., 2006; Guo et al., 2007; Kim et al., 2007; Chang et al., 2009). Unlike the flowers in eudicots, the nearly identical shape of the sepals and petals as well as the production of a unique lip in orchid flowers make them a very special plant species for the study of flower development. Four clades (1–4) of genes in the paleoAP3 lineage have been identified in several orchids (Hsu and Yang, 2002; Tsai et al., 2004; Kim et al., 2007; Mondragón-Palomino and Theißen, 2008, 2009; Mondragón-Palomino et al., 2009). Several works have described the possible interactions among these four clades of paleoAP3 genes and one PI gene that are involved in regulating the differentiation and formation of the sepal/petal/lip of orchids (Tsai et al., 2004; Kim et al., 2007; Mondragón-Palomino and Theißen, 2008, 2009). However, the exact mechanism that involves the orchid B class genes remains unclear and needs to be clarified by more experimental investigations.O. Gower Ramsey is a popular orchid with important economic value in cut flower markets. Only a few studies have been reported on the role of MADS box genes in regulating flower formation in this plant species (Hsu and Yang, 2002; Hsu et al., 2003; Chang et al., 2009). An AP3-like MADS gene that regulates both floral formation and initiation in transgenic Arabidopsis has been reported (Hsu and Yang, 2002). In addition, four AP1/AGAMOUS-LIKE9 (AGL9)-like MADS box genes have been characterized that show novel expression patterns and cause different effects on floral transition and formation in Arabidopsis (Hsu et al., 2003; Chang et al., 2009). Compared with other orchids, the production of a large and well-expanded lip and five small identical sepals/petals makes O. Gower Ramsey a special case for the study of the diverse functions of B class MADS box genes during evolution. Therefore, the isolation of more B class MADS box genes and further study of their roles in the regulation of perianth (sepal/petal/lip) formation during O. Gower Ramsey flower development are necessary. In addition to the clade 2 paleoAP3 gene OMADS3, which was previously characterized in our laboratory (Hsu and Yang, 2002), three more B class MADS box genes, OMADS5, OMADS8, and OMADS9, were characterized from O. Gower Ramsey in this study. Based on the different expression patterns and the protein interactions among these four orchid B class genes, we propose that the presence of OMADS3/8 and/or OMADS9 is required for sepal/petal/lip formation. Further sepal and petal formation at least requires the additional presence of OMADS5, whereas large lip formation was seen when OMADS5 expression was absent. Our results provide a new finding and information pertaining to the roles for orchid B class MADS box genes in the regulation of sepal/petal/lip formation.     

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.     

New-Onset Diabetes Mellitus After Transplantation in a Cynomolgus Macaque (Macaca fasicularis)

A 5.5-y-old intact male cynomolgus macaque (Macaca fasicularis) presented with inappetence and weight loss 57 d after heterotopic heart and thymus transplantation while receiving an immunosuppressant regimen consisting of tacrolimus, mycophenolate mofetil, and methylprednisolone to prevent graft rejection. A serum chemistry panel, a glycated hemoglobin test, and urinalysis performed at presentation revealed elevated blood glucose and glycated hemoglobin (HbA1c) levels (727 mg/dL and 10.1%, respectively), glucosuria, and ketonuria. Diabetes mellitus was diagnosed, and insulin therapy was initiated immediately. The macaque was weaned off the immunosuppressive therapy as his clinical condition improved and stabilized. Approximately 74 d after discontinuation of the immunosuppressants, the blood glucose normalized, and the insulin therapy was stopped. The animal''s blood glucose and HbA1c values have remained within normal limits since this time. We suspect that our macaque experienced new-onset diabetes mellitus after transplantation, a condition that is commonly observed in human transplant patients but not well described in NHP. To our knowledge, this report represents the first documented case of new-onset diabetes mellitus after transplantation in a cynomolgus macaque.Abbreviations: NODAT, new-onset diabetes mellitus after transplantationNew-onset diabetes mellitus after transplantation (NODAT, formerly known as posttransplantation diabetes mellitus) is an important consequence of solid-organ transplantation in humans.^{7-10,15,17,19,21,25-28,31,33,34,37,38,42} A variety of risk factors have been identified including increased age, sex (male prevalence), elevated pretransplant fasting plasma glucose levels, and immunosuppressive therapy.^{7-10,15,17,19,21,25-28,31,33,34,37,38,42} The relationship between calcineurin inhibitors, such as tacrolimus and cyclosporin, and the development of NODAT is widely recognized in human medicine.^{7-10,15,17,19,21,25-28,31,33,34,37,38,42} Cynomolgus macaques (Macaca fasicularis) are a commonly used NHP model in organ transplantation research. Cases of natural and induced diabetes of cynomolgus monkeys have been described in the literature;^14,43,45 however, NODAT in a macaque model of solid-organ transplantation has not been reported previously to our knowledge.     

Plasma membrane poration by opioid neuropeptides: a possible mechanism of pathological signal transduction

Neuropeptides induce signal transduction across the plasma membrane by acting through cell-surface receptors. The dynorphins, endogenous ligands for opioid receptors, are an exception; they also produce non-receptor-mediated effects causing pain and neurodegeneration. To understand non-receptor mechanism(s), we examined interactions of dynorphins with plasma membrane. Using fluorescence correlation spectroscopy and patch-clamp electrophysiology, we demonstrate that dynorphins accumulate in the membrane and induce a continuum of transient increases in ionic conductance. This phenomenon is consistent with stochastic formation of giant (~2.7 nm estimated diameter) unstructured non-ion-selective membrane pores. The potency of dynorphins to porate the plasma membrane correlates with their pathogenic effects in cellular and animal models. Membrane poration by dynorphins may represent a mechanism of pathological signal transduction. Persistent neuronal excitation by this mechanism may lead to profound neuropathological alterations, including neurodegeneration and cell death.Neuropeptides are the largest and most diverse family of neurotransmitters. They are released from axon terminals and dendrites, diffuse to pre- or postsynaptic neuronal structures and activate membrane G-protein-coupled receptors. Prodynorphin (PDYN)-derived opioid peptides including dynorphin A (Dyn A), dynorphin B (Dyn B) and big dynorphin (Big Dyn) consisting of Dyn A and Dyn B are endogenous ligands for the κ-opioid receptor. Acting through this receptor, dynorphins regulate processing of pain and emotions, memory acquisition and modulate reward induced by addictive substances.^{1, 2, 3, 4} Furthermore, dynorphins may produce robust cellular and behavioral effects that are not mediated through opioid receptors.^{5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29} As evident from pharmacological, morphological, genetic and human neuropathological studies, these effects are generally pathological, including cell death, neurodegeneration, neurological dysfunctions and chronic pain. Big Dyn is the most active pathogenic peptide, which is about 10- to 100-fold more potent than Dyn A, whereas Dyn B does not produce non-opioid effects.^{16, 17, 22, 25} Big Dyn enhances activity of acid-sensing ion channel-1a (ASIC1a) and potentiates ASIC1a-mediated cell death in nanomolar concentrations^{30, 31} and, when administered intrathecally, induces characteristic nociceptive behavior at femtomolar doses.^{17, 22} Inhibition of endogenous Big Dyn degradation results in pathological pain, whereas prodynorphin (Pdyn) knockout mice do not maintain neuropathic pain.^{22, 32} Big Dyn differs from its constituents Dyn A and Dyn B in its unique pattern of non-opioid memory-enhancing, locomotor- and anxiolytic-like effects.²⁵Pathological role of dynorphins is emphasized by the identification of PDYN missense mutations that cause profound neurodegeneration in the human brain underlying the SCA23 (spinocerebellar ataxia type 23), a very rare dominantly inherited neurodegenerative disorder.^{27, 33} Most PDYN mutations are located in the Big Dyn domain, demonstrating its critical role in neurodegeneration. PDYN mutations result in marked elevation in dynorphin levels and increase in its pathogenic non-opioid activity.^{27, 34} Dominant-negative pathogenic effects of dynorphins are not produced through opioid receptors.ASIC1a, glutamate NMDA (N-methyl-d-aspartate) and AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid)/kainate ion channels, and melanocortin and bradykinin B2 receptors have all been implicated as non-opioid dynorphin targets.^{5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 30, 31, 35, 36} Multiplicity of these targets and their association with the cellular membrane suggest that their activation is a secondary event triggered by a primary interaction of dynorphins with the membrane. Dynorphins are among the most basic neuropeptides.^{37, 38} The basic nature is also a general property of anti-microbial peptides (AMPs) and amyloid peptides that act by inducing membrane perturbations, altering membrane curvature and causing pore formation that disrupts membrane-associated processes including ion fluxes across the membrane.³⁹ The similarity between dynorphins and these two peptide groups in overall charge and size suggests a similar mode of their interactions with membranes.In this study, we dissect the interactions of dynorphins with the cell membrane, the primary event in their non-receptor actions. Using fluorescence imaging, correlation spectroscopy and patch-clamp techniques, we demonstrate that dynorphin peptides accumulate in the plasma membrane in live cells and cause a profound transient increase in cell membrane conductance. Membrane poration by endogenous neuropeptides may represent a novel mechanism of signal transduction in the brain. This mechanism may underlie effects of dynorphins under pathological conditions including chronic pain and tissue injury.     

Reproductive Value and Fluctuating Selection in an Age-Structured Population

Fluctuations in age structure caused by environmental stochasticity create autocorrelation and transient fluctuations in both population size and allele frequency, which complicate demographic and evolutionary analyses. Following a suggestion of Fisher, we show that weighting individuals of different age by their reproductive value serves as a filter, removing temporal autocorrelation in population demography and evolution due to stochastic age structure. Assuming weak selection, random mating, and a stationary distribution of environments with no autocorrelation, we derive a diffusion approximation for evolution of the reproductive value weighted allele frequency. The expected evolution obeys an adaptive topography defined by the long-run growth rate of the population. The expected fitness of a genotype is its Malthusian fitness in the average environment minus the covariance of its growth rate with that of the population. Simulations of the age-structured model verify the accuracy of the diffusion approximation. We develop statistical methods for measuring the expected selection on the reproductive value weighted allele frequency in a fluctuating age-structured population.THE evolutionary dynamics of age-structured populations were formalized by Charlesworth (1980, 1994) and Lande (1982) on the basis of earlier ideas of Fisher (1930, 1958), Medawar (1946, 1952), and Hamilton (1966), showing that the strength of selection on genes affecting the vital rates of survival or fecundity depends on their age of action (reviewed by de Jong 1994; Charlesworth 2000). Fisher defined the reproductive value of individuals of a given age as their expected contribution to future population growth, determined by the age-specific vital rates. This has the property that in a constant environment the total reproductive value in a population always increases at a constant rate. The total population size, however, undergoes transient fluctuations as the stable age distribution is approached, and the total population size only asymptotically approaches a constant growth rate (Caswell 2001).Environmental stochasticity creates continual fluctuations in age structure, producing temporal autocorrelation in population size and in allele frequencies, which seriously complicate demographic and evolutionary analyses. Fisher (1930, 1958, p. 35) suggested for analysis of genetic evolution that individuals should be weighted by their reproductive value to compensate for deviations from the stable age distribution. Here we apply this suggestion to study weak fluctuating selection in an age-structured population in a stochastic environment.One of the central conceptual paradigms of evolutionary biology was described by Wright (1932). His adaptive topography represents a population as a point on a surface of population mean fitness as a function of allele frequencies. Assuming weak selection, random mating, and loose linkage (implying approximate Hardy–Weinberg equilibrium within loci and linkage eqilibrium among loci), natural selection in a constant environment causes the population to evolve uphill of the mean fitness surface (Wright 1937, 1945, 1969; Arnold et al. 2001; Gavrilets 2004). Evolution by natural selection thus tends to increase the mean fitness of a population in a constant environment.Lande (2007, 2008) generalized Wright''s adaptive topography to a stochastic environment, allowing density-dependent population growth but assuming density-independent selection, showing that the expected evolution maximizes the long-run growth rate of the population at low density, . Here r is population growth rate at low density in the average environment and is the environmental variance in population growth rate among years, which are standard parameters in stochastic demography (Cohen 1977, 1979; Tuljapurkar 1982; Caswell 2001; Lande et al. 2003). In this model of stochastic evolution the adaptive topography describing the expected evolution is derived by expressing r and as functions of allele frequencies with parameters being the mean Malthusian fitnesses of the genotypes and their temporal variances and covariances. These results are based on diffusion approximations for the coupled stochastic processes of population size and allele frequencies in a fluctuating environment.Diffusion approximations are remarkably accurate for many problems in evolution and ecology (Crow and Kimura 1970; Lande et al. 2003). Because a diffusion process is subject to white noise with no temporal autocorrelation, the approximation is most accurate if the noise in the underlying biological process is approximately uncorrelated among years. Despite temporal autocorrelation in total population size produced by age-structure fluctuations, the stochastic demography of age-structured populations over timescales of a generation or more can nevertheless be accurately approximated by a diffusion process (Tuljapurkar 1982; Lande and Orzack 1988; Engen et al. 2005a, 2007). The success of the diffusion approximation for total population size occurs because the noise in the total reproductive value is nearly white, with no temporal autocorrelation to first order, and the log of total population size fluctuates around the log of reproductive value with a return time to equilibrium on the order of a few generations (Engen et al. 2007). Hence the diffusion approximation is well suited to describe the stochastic dynamics of total reproductive value as well as total population size.This article extends Lande''s (2008) model of fluctuating selection without age structure by deriving a diffusion approximation for the evolution of an age-structured population in a stochastic environment. Assuming weak selection at all ages, random mating, and a stationary distribution of environments with no temporal autocorrelation, we show that the main results of the model remain valid, provided that the model parameters are expressed in terms of means, variances, and covariances of age-specific vital rates and that allele frequencies are defined by weighting individuals of different age by their reproductive value, as suggested by Fisher (1930, 1958). We perform simulations to verify the accuracy of the diffusion approximation and outline statistical methods for estimating the expected selection acting on the reproductive value weighted allele frequency.     

TRAF2 is a biologically important necroptosis suppressor

Tumor necrosis factor α (TNFα) triggers necroptotic cell death through an intracellular signaling complex containing receptor-interacting protein kinase (RIPK) 1 and RIPK3, called the necrosome. RIPK1 phosphorylates RIPK3, which phosphorylates the pseudokinase mixed lineage kinase-domain-like (MLKL)—driving its oligomerization and membrane-disrupting necroptotic activity. Here, we show that TNF receptor-associated factor 2 (TRAF2)—previously implicated in apoptosis suppression—also inhibits necroptotic signaling by TNFα. TRAF2 disruption in mouse fibroblasts augmented TNFα–driven necrosome formation and RIPK3-MLKL association, promoting necroptosis. TRAF2 constitutively associated with MLKL, whereas TNFα reversed this via cylindromatosis-dependent TRAF2 deubiquitination. Ectopic interaction of TRAF2 and MLKL required the C-terminal portion but not the N-terminal, RING, or CIM region of TRAF2. Induced TRAF2 knockout (KO) in adult mice caused rapid lethality, in conjunction with increased hepatic necrosome assembly. By contrast, TRAF2 KO on a RIPK3 KO background caused delayed mortality, in concert with elevated intestinal caspase-8 protein and activity. Combined injection of TNFR1-Fc, Fas-Fc and DR5-Fc decoys prevented death upon TRAF2 KO. However, Fas-Fc and DR5-Fc were ineffective, whereas TNFR1-Fc and interferon α receptor (IFNAR1)-Fc were partially protective against lethality upon combined TRAF2 and RIPK3 KO. These results identify TRAF2 as an important biological suppressor of necroptosis in vitro and in vivo.Apoptotic cell death is mediated by caspases and has distinct morphological features, including membrane blebbing, cell shrinkage and nuclear fragmentation.^{1, 2, 3, 4} In contrast, necroptotic cell death is caspase-independent and is characterized by loss of membrane integrity, cell swelling and implosion.^{1, 2, 5} Nevertheless, necroptosis is a highly regulated process, requiring activation of RIPK1 and RIPK3, which form the core necrosome complex.^{1, 2, 5} Necrosome assembly can be induced via specific death receptors or toll-like receptors, among other modules.^{6, 7, 8, 9} The activated necrosome engages MLKL by RIPK3-mediated phosphorylation.^{6, 10, 11} MLKL then oligomerizes and binds to membrane phospholipids, forming pores that cause necroptotic cell death.^{10, 12, 13, 14, 15} Unchecked necroptosis disrupts embryonic development in mice and contributes to several human diseases.^{7, 8, 16, 17, 18, 19, 20, 21, 22}The apoptotic mediators FADD, caspase-8 and cFLIP suppress necroptosis.^{19, 20, 21, 23, 24} Elimination of any of these genes in mice causes embryonic lethality, subverted by additional deletion of RIPK3 or MLKL.^{19, 20, 21, 25} Necroptosis is also regulated at the level of RIPK1. Whereas TNFα engagement of TNFR1 leads to K63-linked ubiquitination of RIPK1 by cellular inhibitor of apoptosis proteins (cIAPs) to promote nuclear factor (NF)-κB activation,²⁶ necroptosis requires suppression or reversal of this modification to allow RIPK1 autophosphorylation and consequent RIPK3 activation.^{2, 23, 27, 28} CYLD promotes necroptotic signaling by deubiquitinating RIPK1, augmenting its interaction with RIPK3.²⁹ Conversely, caspase-8-mediated CYLD cleavage inhibits necroptosis.²⁴TRAF2 recruits cIAPs to the TNFα-TNFR1 signaling complex, facilitating NF-κB activation.^{30, 31, 32, 33} TRAF2 also supports K48-linked ubiquitination and proteasomal degradation of death-receptor-activated caspase-8, curbing apoptosis.³⁴ TRAF2 KO mice display embryonic lethality; some survive through birth but have severe developmental and immune deficiencies and die prematurely.^{35, 36} Conditional TRAF2 KO leads to rapid intestinal inflammation and mortality.³⁷ Furthermore, hepatic TRAF2 depletion augments apoptosis activation via Fas/CD95.³⁴ TRAF2 attenuates necroptosis induction in vitro by the death ligands Apo2L/TRAIL and Fas/CD95L.³⁸ However, it remains unclear whether TRAF2 regulates TNFα-induced necroptosis—and if so—how. Our present findings reveal that TRAF2 inhibits TNFα necroptotic signaling. Furthermore, our results establish TRAF2 as a biologically important necroptosis suppressor in vitro and in vivo and provide initial insight into the mechanisms underlying this function.     

Induction of COX-2-PGE2 synthesis by activation of the MAPK/ERK pathway contributes to neuronal death triggered by TDP-43-depleted microglia

Neuroinflammation is a striking hallmark of amyotrophic lateral sclerosis (ALS) and other neurodegenerative disorders. Previous studies have shown the contribution of glial cells such as astrocytes in TDP-43-linked ALS. However, the role of microglia in TDP-43-mediated motor neuron degeneration remains poorly understood. In this study, we show that depletion of TDP-43 in microglia, but not in astrocytes, strikingly upregulates cyclooxygenase-2 (COX-2) expression and prostaglandin E2 (PGE2) production through the activation of MAPK/ERK signaling and initiates neurotoxicity. Moreover, we find that administration of celecoxib, a specific COX-2 inhibitor, greatly diminishes the neurotoxicity triggered by TDP-43-depleted microglia. Taken together, our results reveal a previously unrecognized non-cell-autonomous mechanism in TDP-43-mediated neurodegeneration, identifying COX-2-PGE2 as the molecular events of microglia- but not astrocyte-initiated neurotoxicity and identifying celecoxib as a novel potential therapy for TDP-43-linked ALS and possibly other types of ALS.Amyotrophic lateral sclerosis (ALS) is an adult-onset neurodegenerative disease characterized by the degeneration of motor neurons in the brain and spinal cord.¹ Most cases of ALS are sporadic, but 10% are familial. Familial ALS cases are associated with mutations in genes such as Cu/Zn superoxide dismutase 1 (SOD1), TAR DNA-binding protein 43 (TARDBP) and, most recently discovered, C9orf72. Currently, most available information obtained from ALS research is based on the study of SOD1, but new studies focusing on TARDBP and C9orf72 have come to the forefront of ALS research.^{1, 2} The discovery of the central role of the protein TDP-43, encoded by TARDBP, in ALS was a breakthrough in ALS research.^{3, 4, 5} Although pathogenic mutations of TDP-43 are genetically rare, abnormal TDP-43 function is thought to be associated with the majority of ALS cases.¹ TDP-43 was identified as a key component of the ubiquitin-positive inclusions in most ALS patients and also in other neurodegenerative diseases such as frontotemporal lobar degeneration,^{6, 7} Alzheimer''s disease (AD)^{8, 9} and Parkinson''s disease (PD).^{10, 11} TDP-43 is a multifunctional RNA binding protein, and loss-of-function of TDP-43 has been increasingly recognized as a key contributor in TDP-43-mediated pathogenesis.^{5, 12, 13, 14}Neuroinflammation, a striking and common hallmark involved in many neurodegenerative diseases, including ALS, is characterized by extensive activation of glial cells including microglia, astrocytes and oligodendrocytes.^{15, 16} Although numerous studies have focused on the intrinsic properties of motor neurons in ALS, a large amount of evidence showed that glial cells, such as astrocytes and microglia, could have critical roles in SOD1-mediated motor neuron degeneration and ALS progression,^{17, 18, 19, 20, 21, 22} indicating the importance of non-cell-autonomous toxicity in SOD1-mediated ALS pathogenesis.Very interestingly, a vital insight of neuroinflammation research in ALS was generated by the evidence that both the mRNA and protein levels of the pro-inflammatory enzyme cyclooxygenase-2 (COX-2) are upregulated in both transgenic mouse models and in human postmortem brain and spinal cord.^{23, 24, 25, 26, 27, 28, 29} The role of COX-2 neurotoxicity in ALS and other neurodegenerative disorders has been well explored.^{30, 31, 32} One of the key downstream products of COX-2, prostaglandin E2 (PGE2), can directly mediate COX-2 neurotoxicity both in vitro and in vivo.^{33, 34, 35, 36, 37} The levels of COX-2 expression and PGE2 production are controlled by multiple cell signaling pathways, including the mitogen-activated protein kinase (MAPK)/ERK pathway,^{38, 39, 40} and they have been found to be increased in neurodegenerative diseases including AD, PD and ALS.^{25, 28, 32, 41, 42, 43, 44, 45, 46} Importantly, COX-2 inhibitors such as celecoxib exhibited significant neuroprotective effects and prolonged survival or delayed disease onset in a SOD1-ALS transgenic mouse model through the downregulation of PGE2 release.²⁸Most recent studies have tried to elucidate the role of glial cells in neurotoxicity using TDP-43-ALS models, which are considered to be helpful for better understanding the disease mechanisms.^{47, 48, 49, 50, 51} Although the contribution of glial cells to TDP-43-mediated motor neuron degeneration is now well supported, this model does not fully suggest an astrocyte-based non-cell autonomous mechanism. For example, recent studies have shown that TDP-43-mutant astrocytes do not affect the survival of motor neurons,^{50, 51} indicating a previously unrecognized non-cell autonomous TDP-43 proteinopathy that associates with cell types other than astrocytes.Given that the role of glial cell types other than astrocytes in TDP-43-mediated neuroinflammation is still not fully understood, we aim to compare the contribution of microglia and astrocytes to neurotoxicity in a TDP-43 loss-of-function model. Here, we show that TDP-43 has a dominant role in promoting COX-2-PGE2 production through the MAPK/ERK pathway in primary cultured microglia, but not in primary cultured astrocytes. Our study suggests that overproduction of PGE2 in microglia is a novel molecular mechanism underlying neurotoxicity in TDP-43-linked ALS. Moreover, our data identify celecoxib as a new potential effective treatment of TDP-43-linked ALS and possibly other types of ALS.     

The Effect of Nonindependent Mate Pairing on the Effective Population Size

The effective population size (N_e) quantifies the effectiveness of genetic drift in finite populations. When generations overlap, theoretical expectations for N_e typically assume that the sampling of offspring genotypes from a given individual is independent among successive breeding events, even though this is not true in many species, including humans. To explore the effects on N_e of nonindependent mate pairing across breeding events, we simulated the genetic drift of mitochondrial DNA, autosomal DNA, and sex chromosome DNA under three mating systems. Nonindependent mate pairing across breeding seasons has no effect when all adults mate pair for life, a small or moderate effect when females reuse stored sperm, and a large effect when there is intense male–male competition for reproduction in a harem social system. If adult females reproduce at a constant rate irrespective of the type of mate pairing, the general effect of nonindependent mate pairing is to decrease N_e for paternally inherited components of the genome. These findings have significant implications for the relative N_e values of different genomic regions, and hence for the expected levels of DNA sequence diversity in these regions.THE effective population size (N_e) is a fundamental parameter of population genetics, which quantifies the effect of genetic drift, the stochastic change in allele frequencies over time in a population of finite size (Wright 1931). The magnitude of N_e affects both the level of genetic variability within a population and the efficiency with which populations retain mildly beneficial mutations and purge mildly deleterious ones. This influences a myriad of genetic phenomena, such as the level of DNA sequence polymorphism, the rate of substitution of nonsynonymous and functional noncoding nucleotide positions, the abundance of transposable elements, levels of variation, and the rate of evolution of gene expression, the persistence of duplicate genes, and genome size and organization (Lynch 2007; Charlesworth 2009). There are a variety of definitions of N_e; here we use the definition in terms of the mean coalescence time of a pair of neutral alleles, which is given by 2N_e (Charlesworth 2009). This definition has the useful feature that the expected pairwise nucleotide site diversity under the widely used infinite sites model is equal to 4N_eμ, where μ is the neutral mutation rate (Kimura 1971).As a result of differences in their ploidy level and mode of inheritance, autosomal DNA (aDNA), the X chromosome (xDNA), the Y chromosome (yDNA), and maternally transmitted organelle DNA such as mitochondrial DNA (mtDNA) generally have a different N_e values. Under certain conditions, such as constant population size, discrete generations, a Poisson distribution of reproductive success, and a sex ratio equal to one, the relative N_e values of these genomic regions (N_e-a, N_e-x, N_e-mt, and N_e-y) are expected to be 4:3:1:1 (Charlesworth 2009). This is because aDNA is biparentally inherited and diploid; xDNA is biparentally inherited, diploid in females, and haploid in males (with female heterogamety, the reverse applies to the Z chromosome), and yDNA (the W chromosome, with female heterogamety) and mtDNA are both effectively uniparentally inherited and haploid in most species.However, several characteristics of natural populations, such as unequal numbers of males and females and nonrandom variation in reproductive success, can affect the value of N_e, even for populations with discrete generations (reviewed in Caballero 1994; Hedrick 2007; Charlesworth 2009). In addition, natural selection at sites linked to neutral markers also has the potential to increase N_e (under balancing selection) (Charlesworth 2006) or to decrease N_e (with background selection or selective sweeps) (Hudson and Kaplan 1988; Charlesworth et al. 1993). Because the nature of natural selection varies in different genomic regions, especially in relation to the rate of recombination, N_e may also vary among unlinked regions with the same ploidy and mode of inheritance, for example, different portions of an autosomal chromosome (Gossmann et al. 2011). In natural populations, these factors can skew the relative N_e values away from the 4:3:1:1 expectation. Even when the effects of natural selection and “nonideal” demography are ignored, the 4:3:1:1 relation still has a large variance when applied to individual loci (Hudson and Turelli 2003).When generations overlap, an additional source of possible deviations from these idealized relations arises from variation among individuals in survival and reproductive success among breeding seasons (Felsenstein 1971; Hill 1972, 1979; Johnson 1977) and from sex differences in demographic parameters and stochastic changes in population size (Engen et al. 2007). In contrast, the effect of a high variance in reproductive success caused by male–male competition is lessened when generations overlap for many breeding seasons (Nunney 1993; Charlesworth 2001). Conversely, overlapping generations with nonindependent mate pairing across breeding seasons could increase the variance in reproductive success. For example, in humans, paternity is correlated with paternal confidence in paternity (Anderson 2006), and married individuals tend to repeatedly produce offspring with each other more frequently than expected by chance. Nonindependent mate pairing among breeding seasons occurs in many other species as well—for example, long-term pair bonding in prairie voles (DeVries et al. 1995), harems in gorillas (Gatti et al. 2004), and sperm storage in fruit flies (Neubaum and Wolfner 1999).Current theoretical models that allow calculation of the effective population size with overlapping generations and age structure make several simplifying assumptions, notably constant sizes of each age class, sufficiently large numbers of individuals in each age class that second-order terms in their reciprocals can be neglected, and independent sampling of offspring genotypes from the same individual reproducing at different times (Hill 1972; Nunney 1991, 1993; Caballero 1994; Charlesworth 1994, 2001). The latter assumption in particular makes it difficult to provide accurate expressions for species such as humans and Drosophila, which reproduce nonindependently because of long-term pair bonds and sperm storage, respectively (Charlesworth 2001).The goal of this study is therefore to explore the consequences of nonindependence of reproductive events across time in different social systems and with different age structures, using simulations of genetic drift in two types of age-structured populations, under different scenarios of independent and nonindependent mate pairing among breeding events. We have explored how these scenarios affect the relative values of N_e-a, N_e-x, N_e-mt, and N_e-y using the infinite alleles model of mutation (Kimura and Crow 1964), with particular emphasis on comparisons of similar mating systems that differ in the extent of nonindependence among breeding events.     

Vascular Occlusions in Grapevines with Pierce’s Disease Make Disease Symptom Development Worse

Vascular occlusions are common structural modifications made by many plant species in response to pathogen infection. However, the functional role(s) of occlusions in host plant disease resistance/susceptibility remains controversial. This study focuses on vascular occlusions that form in stem secondary xylem of grapevines (Vitis vinifera) infected with Pierce’s disease (PD) and the impact of occlusions on the hosts’ water transport and the systemic spread of the causal bacterium Xylella fastidiosa in infected vines. Tyloses are the predominant type of occlusion that forms in grapevine genotypes with differing PD resistances. Tyloses form throughout PD-susceptible grapevines with over 60% of the vessels in transverse sections of all examined internodes becoming fully blocked. By contrast, tylose development was mainly limited to a few internodes close to the point of inoculation in PD-resistant grapevines, impacting only 20% or less of the vessels. The extensive vessel blockage in PD-susceptible grapevines was correlated to a greater than 90% decrease in stem hydraulic conductivity, compared with an approximately 30% reduction in the stems of PD-resistant vines. Despite the systemic spread of X. fastidiosa in PD-susceptible grapevines, the pathogen colonized only 15% or less of the vessels in any internode and occurred in relatively small numbers, amounts much too small to directly block the vessels. Therefore, we concluded that the extensive formation of vascular occlusions in PD-susceptible grapevines does not prevent the pathogen’s systemic spread in them, but may significantly suppress the vines’ water conduction, contributing to PD symptom development and the vines’ eventual death.Pierce’s disease (PD) of grapevines (Vitis vinifera), currently jeopardizing the wine and table grape industries in the southern United States and California, as well as in many other countries, is a vascular disease caused by the xylem-limited bacterium Xylella fastidiosa (Hopkins, 1989; Varela et al., 2001). The pathogen is transmitted mostly via xylem sap-feeding sharpshooters (e.g. Homalodisca vitripennis; Redak et al., 2004) and inhabits, proliferates, and spreads within the vessel system of a host grapevine (Fry and Milholland, 1990a; Hill and Purcell, 1995). PD symptom development in grapevines depends on the interactions between the pathogen and the host vine’s xylem tissue, through which the pathogen may achieve its systemic spread (Purcell and Hopkins, 1996; Krivanek and Walker, 2005; Pérez-Donoso et al., 2010; Sun et al., 2011). Since the path for this spread is the host’s xylem system, xylem tissue and its vessels have become the major focus for studying potential X. fastidiosa-host vine interactions at the cellular or tissue levels (Fry and Milholland, 1990b; Stevenson et al., 2004a; Sun et al., 2006, 2007; Thorne et al., 2006).One major issue related to this host-pathogen interaction is the relationship of a vine’s xylem anatomy to the X. fastidiosa population’s spread. Sun et al. (2006) did a detailed anatomical analysis of the stem secondary xylem, especially the vessel system. Stevenson et al. (2004b) described xylem connection patterns between a stem and the attached leaves. Other studies reported the presence of open continuous vessels connecting stems and leaves, which represent conduits that might facilitate the pathogen’s stem-to-leaf movement (Thorne et al., 2006; Chatelet et al., 2006, 2011). Chatelet et al. (2011) also suggested that vessel size and ray density were the two xylem features that were most relevant to the restriction of X. fastidiosa’s movement. These studies indicate the importance of understanding the grapevine’s xylem anatomy in order to characterize the grapevine host’s susceptibility or resistance to PD.Another focus of PD-related xylem studies is the tylose, a developmental modification that has important impacts on a vessel’s role in water transport and, potentially, its availability as a path for X. fastidiosa’s systemic spread through a vine. Tyloses are outgrowths into a vessel lumen from living parenchyma cells that are adjacent to the vessel and can transfer solutes into the transpiration stream via vessel-parenchyma (V-P) pit pairs (Esau, 1977). Tylose development involves the expansion of the portions of the parenchyma cell’s wall that are shared with the neighboring vessels, specifically the so-called pit membranes (PMs). Intensive tylose development may eventually block the affected vessel (Sun et al., 2006). Since tyloses occur in the vessel system of PD-infected grapevines (Esau, 1948; Mollenhauer and Hopkins, 1976; Stevenson et al., 2004a; Krivanek et al., 2005) that is also the avenue of X. fastidiosa’s spread and water transport, a great deal of effort has been made to understand tyloses and their possible relations to grapevine PD as well as to diseases caused by other vascular system-localized pathogens. One major aspect is to clarify the process of tylose development itself, in which an open vessel may be gradually sealed (Sun et al., 2006, 2008). Our investigations of the initiation of tylose formation in grapevines have identified ethylene as an important factor (Pérez-Donoso et al., 2007; Sun et al., 2007). In terms of the relationship of tyloses to grapevine PD, studies have so far led to several controversial viewpoints that are discussed below (Mollenhauer and Hopkins, 1976; Fry and Milholland, 1990b; Stevenson et al., 2004a; Krivanek et al., 2005). However, more convincing evidence is still needed to support any of them.Another issue potentially relevant to PD symptom development is the possibility that X. fastidiosa cells and/or their secretions contribute to the blockage of water transport in host vines. The bacteria secrete an exopolysaccharide (Roper et al., 2007a) that contributes to the formation of cellular aggregates. Accumulations of X. fastidiosa cells embedded in an exopolysaccharide matrix (occasionally identified as biofilms, gums, or gels) have been reported in PD-infected grapevines (Mollenhauer and Hopkins, 1974; Fry and Milholland, 1990a; Newman et al., 2003; Stevenson et al., 2004b). However, a more detailed investigation is still needed to clarify if and to what extent these aggregates affect water transport in infected grapevines.The xylem tissue in which X. fastidiosa spreads can be classified as primary xylem or secondary xylem, being derived from procambium or vascular cambium, respectively. Primary xylem is located in and responsible for material transport and structural support in young organs (i.e. leaves, young stems, and roots), while secondary xylem is the conductive and supportive tissue in more mature stems and roots (Esau, 1977). It should be noted that most of the earlier experimental results have been based on examinations of leaves (petioles or veins) or young stems of grapevines, which contain mostly primary xylem with little or no secondary xylem. However, X. fastidiosa’s systemic spread generally occurs after introduction during the insect vector’s feeding from an internode of one shoot. The pathogen then moves upward along that shoot and also downward toward the shoot base. The downward movement allows the bacteria to enter the vine’s other shoots via the shared trunk and then move upward (Stevenson et al., 2004a; Sun et al., 2011). These upward and downward bacterial movements occur through stems that contain significant amounts of secondary xylem but relatively dysfunctional primary xylem. Secondary and primary xylem show some major differences in the structure and arrangement of their cell components (Esau, 1977). In terms of the vessel system that is the path of X. fastidiosa’s spread, the secondary xylem has a large number of much bigger vessels with scalariform (ladder-like) PMs (and pit pairs) as the sole intervessel (I-V) PM type, compared with the primary xylem, which contains only a limited number of smaller vessels with multiple types of I-V PMs (Esau, 1948; Sun et al., 2006). Vessels in secondary xylem are also different from those in primary xylem in forming vessel groups and in the number of parenchyma cells associated with a vessel (as seen in transverse sections of xylem tissue). These features of secondary xylem can affect the initial entry and subsequent I-V movement of the pathogen and the formation of vascular occlusions, respectively, in stems containing significant amounts of secondary xylem. Recently, the X. fastidiosa population size only in stems with secondary xylem was found to correlate with the grapevine’s resistance to PD (Baccari and Lindow, 2011), indicating an important role of stem secondary xylem in determining a host vine’s disease resistance. Despite these facts, little is known about the pathogen-grapevine interactions in the stem secondary xylem and their possible impacts on disease development.This study addresses X. fastidiosa-grapevine interactions in stem secondary xylem and examines the resulting impacts on overall vine physiology, with a primary focus on vine water transport. We have made use of grapevine genotypes displaying different PD resistances and explored whether differences in the pathogen’s induction of vascular occlusions occur among the genotypes and, if so, how the differences impact X. fastidiosa’s systemic spread. Our overall, longer-term aim is to elucidate the functional role of vascular occlusions in PD development, an understanding that we view to be essential for identifying effective approaches for controlling this devastating disease.