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.     

Polymorphisms and genetic linkage of histamine receptors

Histamine is a biogenic amine that plays an essential role in controlling many physiological functions, both in the central nervous system (CNS) and the peripheral nervous system (PNS). Most of these physiological effects are mediated through interactions with four histamine receptor subtypes, all of which are members of the larger family of rhodopsin-like class A G-protein coupled receptors (GPCRs) (Leurs et al., 2011; Lim et al., 2009). Here, we focus on the genetic variations and polymorphisms localized on the genes encoding for human histamine receptors where it provides an up to date collection of all polymorphisms found on genes encoding the histamine receptor subtypes and their association to diseases.     

Feeding the Probiotic Enterococcus faecium Strain NCIMB 10415 to Piglets Specifically Reduces the Number of Escherichia coli Pathotypes That Adhere to the Gut Mucosa

Feed supplementation with the probiotic Enterococcus faecium for piglets has been found to reduce pathogenic gut microorganisms. Since Escherichia coli is among the most important pathogens in pig production, we performed comprehensive analyses to gain further insight into the influence of E. faecium NCIMB 10415 on porcine intestinal E. coli. A total of 1,436 E. coli strains were isolated from three intestinal habitats (mucosa, digesta, and feces) of probiotic-supplemented and nonsupplemented (control) piglets. E. coli bacteria were characterized via pulsed-field gel electrophoresis (PFGE) for clonal analysis. The high diversity of E. coli was reflected by 168 clones. Multilocus sequence typing (MLST) was used to determine the phylogenetic backgrounds, revealing 79 sequence types (STs). Pathotypes of E. coli were further defined using multiplex PCR for virulence-associated genes. While these analyses discerned only a few significant differences in the E. coli population between the feeding groups, analyses distinguishing clones that were uniquely isolated in either the probiotic group only, the control group only, or both groups (shared group) revealed clear effects at the habitat level. Interestingly, extraintestinal pathogenic E. coli (ExPEC)-typical clones adhering to the mucosa were significantly reduced in the probiotic group. Our data show a minor influence of E. faecium on the overall population of E. coli in healthy piglets. In contrast, this probiotic has a profound effect on mucosa-adherent E. coli. This finding further substantiates a specific effect of E. faecium strain NCIMB 10415 in piglets against pathogenic E. coli in the intestine. In addition, these data question the relevance of data based on sampling fecal E. coli only.     

The WISER metadatabase: the key to more than 100 ecological datasets from European rivers, lakes and coastal waters

In ecological sciences, the role of metadata (i.e. key information about a dataset) to make existing datasets visible and discoverable has become increasingly important. Within the EU-funded WISER project (Water bodies in Europe: Integrative Systems to assess Ecological status and Recovery), we designed a metadatabase to allow scientists to find the optimal data for their analyses. An online questionnaire helped to collect metadata from the data providers and an online query tool (http://www.wiser.eu/results/meta-database/) facilitated data evaluation. The WISER metadatabase currently holds information on 114 datasets (22 river, 71 lake, 1 general freshwater and 20 coastal/transitional datasets), which also can be accessed by external scientists. We evaluate if generally used metadata standards (e.g. Darwin Core, ISO 19115, CSDGM, EML) are suitable for such specific purposes as WISER and suggest at least the linkage with standard metadata fields. Furthermore, we discuss whether the simple metadata documentation is enough for others to reuse a dataset and why there is still reluctance to publish both metadata and primary research data (i.e. time and financial constraints, misuse of data, abandoning intellectual property rights). We emphasise that metadata publication has major advantages as it makes datasets detectable by other scientists and generally makes a scientist’s work more visible.     

Synthesis of DNA oligonucleotides containing C5-ethynylbenzenesulfonamide-modified nucleotides (EBNA) by polymerases towards the construction of base functionalized nucleic acids

C5-Ethynylbenzenesulfonamide-modified nucleotide (EBNA) was investigated as substrate of various DNA polymerases. The experiments revealed that KOD, Phusion and Klenow DNA polymerases successfully accepted EBNA-T nucleotide as a substrate and yielded the fully extended DNA. KOD DNA polymerase was found to be the most efficient enzyme to furnish EBNA-T containing DNA in good yields. Phusion DNA polymerase efficiently amplified the template containing EBNA-T nucleotides by PCR.     

Synthesis and biological evaluation of potential threonine synthase inhibitors: Rhizocticin A and Plumbemycin A

Rhizocticins and Plumbemycins are natural phosphonate antibiotics produced by the bacterial strains Bacillus subtilis ATCC 6633 and Streptomyces plumbeus, respectively. Up to now, these potential threonine synthase inhibitors have only been synthesized under enzymatic catalysis. Here we report the chemical stereoselective synthesis of the non-proteinogenic (S,Z)-2-amino-5-phosphonopent-3-enoic acid [(S,Z)-APPA] and its use for the synthesis of Rhizocticin A and Plumbemycin A. In this work, (S,Z)-APPA was synthesized via the Still–Gennari olefination starting from Garner’s aldehyde. The Michaelis–Arbuzov reaction was used to form the phosphorus–carbon bond. Oligopeptides were prepared using liquid phase peptide synthesis (LPPS) and were tested against selected bacteria and fungi.     

Anchorless surface associated glycolytic enzymes from Lactobacillus plantarum 299v bind to epithelial cells and extracellular matrix proteins

An important criterion for the selection of a probiotic bacterial strain is its ability to adhere to the mucosal surface. Adhesion is usually mediated by proteins or other components located on the outer cell surface of the bacterium. In the present study we characterized the adhesive properties of two classical intracellular enzymes glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and enolase (ENO) isolated from the outer cell surface of the probiotic bacterium Lactobacillus plantarum 299v. None of the genes encoded signal peptides or cell surface anchoring motifs that could explain their extracellular location on the bacterial surface. The presence of the glycolytic enzymes on the outer surface was verified by western blotting using polyclonal antibodies raised against the specific enzymes. GAPDH and ENO showed a highly specific binding to plasminogen and fibronectin whereas GAPDH but not ENO showed weak binding to mucin. Furthermore, a pH dependent and specific binding of GAPDH and ENO to intestinal epithelial Caco-2 cells at pH 5 but not at pH 7 was demonstrated. The results showed that these glycolytic enzymes could play a role in the adhesion of the probiotic bacterium L. plantarum 299v to the gastrointestinal tract of the host. Finally, a number of probiotic as well non-probiotic Lactobacillus strains were analyzed for the presence of GAPDH and ENO on the outer surface, but no correlation between the extracellular location of these enzymes and the probiotic status of the applied strains was demonstrated.     

Selectively dispersed isotope labeling for protein structure determination by magic angle spinning NMR

The power of nuclear magnetic resonance spectroscopy derives from its site-specific access to chemical, structural and dynamic information. However, the corresponding multiplicity of interactions can be difficult to tease apart. Complimentary approaches involve spectral editing on the one hand and selective isotope substitution on the other. Here we present a new “redox” approach to the latter: acetate is chosen as the sole carbon source for the extreme oxidation numbers of its two carbons. Consistent with conventional anabolic pathways for the amino acids, [1-¹³C] acetate does not label α carbons, labels other aliphatic carbons and the aromatic carbons very selectively, and labels the carboxyl carbons heavily. The benefits of this labeling scheme are exemplified by magic angle spinning spectra of microcrystalline immunoglobulin binding protein G (GB1): the elimination of most J-couplings and one- and two-bond dipolar couplings provides narrow signals and long-range, intra- and inter-residue, recoupling essential for distance constraints. Inverse redox labeling, from [2-¹³C] acetate, is also expected to be useful: although it retains one-bond couplings in the sidechains, the removal of CA–CO coupling in the backbone should improve the resolution of NCACX spectra.