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.     

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.     

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.     

Anatomic,Hematologic, and Biochemical Features of C57BL/6NCrl Mice Maintained on Chronic Oral Corticosterone

Metabolic syndrome is a condition that typically includes central obesity, insulin resistance, glucose intolerance, dyslipidemia, and hypertension. Disruption of the hypothalamic–pituitary–adrenal axis, a regulator of corticosterone secretion, occurs in some cases of metabolic syndrome and obesity, and Cushing hypercortisolemia is associated with obesity and metabolic disorders. We therefore assessed anatomic and clinical pathology in C57BL/6NCrl mice to evaluate the effects of chronic corticosterone in the drinking water at doses of 25, 50, and 100 μg/mL for 25 d. Treated mice developed obesity, glucose intolerance, electrolyte aberrations, and dyslipidemia that were dose-dependent and most severe in the 100-μg/mL treatment group. To evaluate return to normal function, additional C57BL/6NCrl mice received corticosterone-free water for 2 wk after the 25-d treatment period. According to results of gross examination, mice appeared to recover within days of exogenous corticosterone withdrawal; however, adrenal gland vacuolation and protein, lipid, and electrolyte abnormalities persisted. Together, these findings support chronic corticosterone exposure through the drinking water as a potentially useful, noninvasive method to induce some features of metabolic syndrome.Obesity and associated metabolic dysfunctions are an increasing public health concern in modern Western society. In humans, obesity and metabolic syndrome heighten the risk of developing debilitating and costly illness including diabetes, cardiovascular disease, stroke, and some forms of cancer.^2,20 Mounting evidence indicates that stress and associated hormones such as cortisol (corticosterone in rodents) contribute to the development of metabolic syndrome. Furthermore, regional glucocorticoid metabolism in adipocytes is proposed to be involved in the pathogenesis of metabolic syndrome.^{6,16,17,27,56} Cushing syndrome, iatrogenic hypercortisolemia, and metabolic syndrome share clinical and physiologic similarities, including central obesity, insulin resistance, glucose intolerance, dyslipidemia, and hypertension.^{1,2,31,35,41,46} How glucocorticoids contribute to the development of these problems remains unclear.Numerous clinical and experimental studies have linked stress, diet, and lifestyle choices to changes in risk factors associated with the development of metabolic disorders.^{1,3,7,10,21,33,36,42,55} How corticosterone influences this risk remains unclear. Although corticosterone has beneficial short-term effects, long-term corticosterone exposure can result in damage to the physiologic systems it protects acutely.²⁷ Disruption of this physiologic signal occurs in numerous disparate disorders, ranging from depression to Cushing syndrome.^16,22,36,54 Therefore, understanding the effects of chronic high corticosterone on metabolism and physiology is of key importance.To clarify how chronic treatment with corticosterone alters the physiology of an organism, we treated adrenally intact adult male mice with corticosterone in drinking water for 4 wk. Furthermore, we examined the return of physiology 2 wk after withdrawal of chronic corticosterone administration. We used this approach as a rapid (3- to 4-wk), noninvasive method of altering plasma corticosterone levels that enabled us to retain some integrity in the diurnal rhythm present in normal animals.We previously characterized the gross metabolic consequences of exogenous noninvasive corticosterone delivery in the drinking water.^20,28 In those studies, we found that high doses of corticosterone (100 μg/mL) resulted in rapid and dramatic hyperphagia; weight gain; increased adiposity; elevated plasma corticosterone, leptin, insulin, and triglyceride levels; and decreased homecage locomotion.²⁰ Moreover, several studies have shown that a lower dose of corticosterone (25 μg/mL) resulted in an intermediate phenotype in some of these measures but had no effect on others.^{12,14,20,23,28,38,42,47} As such, the high corticosterone dose results in a phenotype that satisfies most of the criteria for metabolic syndrome as defined by the National Heart, Lung, and Blood Institute and the American Heart Association.¹⁵ However, little information is available on the resulting histologic, hematologic, and serum chemical profiles associated with this treatment. We sought to more fully characterize this model to support selection of the model that most accurately reflects the human disease conditions under study. In-depth characterization of the model also provides more precise measurements of response to therapies intended to ameliorate the effects of the treatment.The current study provides a detailed examination of the physiologic effect of 3 dosages of corticosterone—low (25 μg/mL), intermediate (50 μg/mL), and high (100 μg/mL) doses—in drinking water. The goal was to extend the previous findings that established this regimen as a model of metabolic syndrome by exploring the detailed physiologic changes associated with this model and to assess whether and how treated mice recover after withdrawal of the corticosterone treatment. We propose that the physiologic changes observed in the mice treated with high-dose corticosterone approximate changes observed in human patients with metabolic syndrome and that these mice potentially serve as a model for hypercortisolemia and associated obesity. In addition, we hypothesized that 2 wk of recovery from corticosterone treatment would not completely resolve cellular and clinical pathologies characterized during treatment, given the numerous changes in physiology.     

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.     

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.     

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.     

Comparison of Biomarkers of Oxidative Stress and Cardiovascular Disease in Humans and Chimpanzees (Pan troglodytes)

In the oxidative stress hypothesis of aging, the aging process is the result of cumulative damage by reactive oxygen species. Humans and chimpanzees are remarkably similar; but humans live twice as long as chimpanzees and therefore are believed to age at a slower rate. The purpose of this study was to compare biomarkers for cardiovascular disease, oxidative stress, and aging between male chimpanzees and humans. Compared with men, male chimpanzees were at increased risk for cardiovascular disease because of their significantly higher levels of fibrinogen, IGF1, insulin, lipoprotein a, and large high-density lipoproteins. Chimpanzees showed increased oxidative stress, measured as significantly higher levels of 5-hydroxymethyl-2-deoxyuridine and 8-iso-prostaglandin F_2α, a higher peroxidizability index, and higher levels of the prooxidants ceruloplasmin and copper. In addition, chimpanzees had decreased levels of antioxidants, including α- and β-carotene, β-cryptoxanthin, lycopene, and tocopherols, as well as decreased levels of the cardiovascular protection factors albumin and bilirubin. As predicted by the oxidative stress hypothesis of aging, male chimpanzees exhibit higher levels of oxidative stress and a much higher risk for cardiovascular disease, particularly cardiomyopathy, compared with men of equivalent age. Given these results, we hypothesize that the longer lifespan of humans is at least in part the result of greater antioxidant capacity and lower risk of cardiovascular disease associated with lower oxidative stress.Abbreviations: 5OHmU, 5-hydroxymethyl-2-deoxyuridine; 8isoPGF_2α, 8-iso-prostaglandin F_2α; HDL, high-density lipoprotein; IGF1, insulin-like growth factor 1; LDL, low-density lipoprotein; ROS, reactive oxygen speciesAging is characterized as a progressive reduction in the capacity to withstand the stresses of everyday life and a corresponding increase in risk of mortality. According to the oxidative stress hypothesis of aging, much of the aging process can be accounted for as the result of cumulative damage produced by reactive oxygen species (ROS).^{6,21,28,41,97} Endogenous oxygen radicals (that is, ROS) are generated as a byproduct of normal metabolic reactions in the body and subsequently can cause extensive damage to proteins, lipids, and DNA.^6,41 Various prooxidant elements, in particular free transition metals, can catalyze these destructive reactions.⁶ The damage caused by ROS can be counteracted by antioxidant defense systems, but the imbalance between production of ROS and antioxidant defenses, over time, leads to oxidative stress and may contribute to the rate of aging.^28,97Oxidative stress has been linked to several age-related diseases including neurodegenerative diseases, ophthalmologic diseases, cancer, and cardiovascular disease.^21,28,97 Of these, cardiovascular disease remains the leading cause of adult death in the United States and Europe.⁷¹ In terms of cardiovascular disease, oxidative stress has been linked to atherosclerosis, hypertension, cardiomyopathy, and chronic heart failure in humans.^55,78,84 Increases in oxidant catalysts (prooxidants)—such as copper, iron, and cadmium—have been associated with hypertension, coronary artery disease, atherosclerosis, and sudden cardiac death.^98,102,106 Finally, both endogenous and exogenous antioxidants have been linked to decreased risk of cardiovascular disease, although the mechanisms behind this relationship are unclear.^11,52,53 However, the oxidative stress hypothesis of aging aims to explain not only the mechanism of aging and age-related diseases (such as cardiovascular disease) in humans but also the differences between aging rates and the manifestations of age-related diseases across species.The differences in antioxidant and ROS levels between animals and humans offer promise for increasing our understanding of human aging. Additional evidence supporting the oxidative stress hypothesis of aging has come from comparative studies linking differences in aging rates across taxa with both antioxidant and ROS levels.^{4,17-21,58,71,86,105} In mammals, maximum lifespan potential is positively correlated with both serum and tissue antioxidant levels.^{17,18,21,71,105} Research has consistently demonstrated that the rate of oxidative damage varies across species and is negatively correlated with maximum lifespan potential.^{4,19,20,58,71,86} However, few studies involved detailed comparisons of hypothesized biochemical indicators of aging and oxidative stress between humans and animals.⁶ This type of interspecies comparison has great potential for directly testing the oxidative stress hypothesis of aging.Much evolutionary and genetic evidence supports remarkable similarity between humans and chimpanzees.^95,100 Despite this similarity, humans have a lifespan of almost twice that of chimpanzees.^3,16,47 Most comparative primate aging research has focused on the use of a macaque model,^62,81,88 and several biochemical markers of age-related diseases have been identified in both humans and macaque monkeys.^{9,22,28,81,93,97} Several other species of monkeys have also been used in research addressing oxidative stress, antioxidant defenses, and maximum lifespan potential.^18,21,58,105 However, no study to date has examined biochemical indicators of oxidative stress and aging in chimpanzees and humans as a test of the oxidative stress hypothesis for aging. The purpose of this study is to compare biochemical markers for cardiovascular disease, oxidative stress, and aging directly between male chimpanzees and humans. Given the oxidative stress hypothesis for aging and the known role of oxidative stress in cardiovascular disease, we predict that chimpanzees will show higher levels of cardiovascular risk and oxidative stress than humans.     

Necroptosis is preceded by nuclear translocation of the signaling proteins that induce it

A signaling pathway that induces programmed necrotic cell death (necroptosis) was reported to be activated in cells by several cytokines and various pathogen components. The major proteins participating in that pathway are the protein kinases RIPK1 and RIPK3 and the pseudokinase mixed lineage kinase domain-like protein (MLKL). Recent studies have suggested that MLKL, once activated, mediates necroptosis by binding to cellular membranes, thereby triggering ion fluxes. However, our knowledge of both the sequence of molecular events leading to MLKL activation and the subcellular sites of these events is fragmentary. Here we report that the association of MLKL with the cell membrane in necroptotic death is preceded by the translocation of phosphorylated MLKL, along with RIPK1 and RIPK3, to the nucleus.Apart from the apoptotic cell death pathway that ligands of the tumor necrosis factor (TNF) family can activate, these ligands and various other inducers, including the interferons and various pathogen components, have in recent years been found also to trigger a signaling cascade that induces programmed necrotic death (necroptosis). This cascade encompasses sequential activation of the protein kinases RIPK1 and RIPK3 and the pseudokinase mixed lineage kinase domain-like protein (MLKL).^{1, 2, 3, 4, 5} RIPK3-mediated phosphorylation of MLKL triggers its oligomerization, which is necessary and sufficient for the induction of cell death,^{6, 7, 8} and can also trigger some non-deadly functions.⁹ MLKL was recently suggested to trigger cell death by binding to cellular membranes and initiating ion fluxes through them.^{6, 7, 8, 10} However, its exact molecular target in death induction is contentious.^{6, 8, 10, 11, 12} Current knowledge of the subcellular sites of MLKL action is based mainly on determination of the location of this protein close to the time of cell death. Here we present a detailed assessment of the cellular location of MLKL at different times following its activation. Our findings indicate that before cell death, MLKL translocates to the nucleus along with RIPK1 and RIPK3.     

The Fcp1-Wee1-Cdk1 axis affects spindle assembly checkpoint robustness and sensitivity to antimicrotubule cancer drugs

To grant faithful chromosome segregation, the spindle assembly checkpoint (SAC) delays mitosis exit until mitotic spindle assembly. An exceedingly prolonged mitosis, however, promotes cell death and by this means antimicrotubule cancer drugs (AMCDs), that impair spindle assembly, are believed to kill cancer cells. Despite malformed spindles, cancer cells can, however, slip through SAC, exit mitosis prematurely and resist killing. We show here that the Fcp1 phosphatase and Wee1, the cyclin B-dependent kinase (cdk) 1 inhibitory kinase, play a role for this slippage/resistance mechanism. During AMCD-induced prolonged mitosis, Fcp1-dependent Wee1 reactivation lowered cdk1 activity, weakening SAC-dependent mitotic arrest and leading to mitosis exit and survival. Conversely, genetic or chemical Wee1 inhibition strengthened the SAC, further extended mitosis, reduced antiapoptotic protein Mcl-1 to a minimum and potentiated killing in several, AMCD-treated cancer cell lines and primary human adult lymphoblastic leukemia cells. Thus, the Fcp1-Wee1-Cdk1 (FWC) axis affects SAC robustness and AMCDs sensitivity.The spindle assembly checkpoint (SAC) delays mitosis exit to coordinate anaphase onset with spindle assembly. To this end, SAC inhibits the ubiquitin ligase Anaphase-Promoting Complex/Cyclosome (APC/C) to prevent degradation of the anaphase inhibitor securin and cyclin B, the major mitotic cyclin B-dependent kinase 1 (cdk1) activator, until spindle assembly.¹ However, by yet poorly understood mechanisms, exceedingly prolonging mitosis translates into cell death induction.^{2, 3, 4, 5, 6, 7} Although mechanistic details are still missing on how activation of cell death pathways is linked to mitosis duration, prolongation of mitosis appears crucial for the ability of antimicrotubule cancer drugs (AMCDs) to kill cancer cells.^{2, 3, 4, 5, 6, 7} These drugs, targeting microtubules, impede mitotic spindle assembly and delay mitosis exit by chronically activating the SAC. Use of these drugs is limited, however, by toxicity and resistance. A major mechanism for resistance is believed to reside in the ability of cancer cells to slip through the SAC and exit mitosis prematurely despite malformed spindles, thus resisting killing by limiting mitosis duration.^{2, 3, 4, 5, 6, 7} Under the AMCD treatment, cells either die in mitosis or exit mitosis, slipping through the SAC, without or abnormally dividing.^{2, 3, 4} Cells that exit mitosis either die at later stages or survive and stop dividing or proliferate, giving rise to resistance.^{2, 3, 4} Apart from a role for p53, what dictates cell fate is still unknown; however, it appears that the longer mitosis is protracted, the higher the chances for cell death pathway activation are.^{2, 3, 4, 5, 6, 7}Although SAC is not required per se for killing,⁶ preventing SAC adaptation should improve the efficacy of AMCD by increasing mitosis duration.^{2, 3, 4, 5, 6, 7} Therefore, further understanding of the mechanisms by which cells override SAC may help to improve the current AMCD therapy. Several kinases are known to activate and sustain SAC, and cdk1 itself appears to be of primary relevance.^{1, 8, 9} By studying mitosis exit and SAC resolution, we recently reported a role for the Fcp1 phosphatase to bring about cdk1 inactivation.^{10, 11} Among Fcp1 targets, we identified cyclin degradation pathway components, such as Cdc20, an APC/C co-activator, USP44, a deubiquitinating enzyme, and Wee1.^{10, 11} Wee1 is a crucial kinase that controls the G2 phase by performing inhibitory phosphorylation of cdk1 at tyr-15 (Y15-cdk1). Wee1 is also in a feedback relationship with cdk1 itself that, in turn, can phosphorylate and inhibit Wee1 in an autoamplification loop to promote the G2-to-M phase transition.¹² At mitosis exit, Fcp1 dephosphorylated Wee1 at threonine 239, a cdk1-dependent inhibitory phosphorylation, to dampen down the cdk1 autoamplification loop, and Cdc20 and USP44, to promote APC/C-dependent cyclin B degradation.^{10, 11, 12} In this study we analysed the Fcp1 relevance in SAC adaptation and AMCD sensitivity.