Differential metabolic response of narrow leafed lupine (Lupinus angustifolius) leaves to infection with Colletotrichum lupini

Flavones and isoflavones are a major group of phenolic secondary metabolites which occur in leaves of narrow leafed lupine (Lupinus angustifolius) either as free aglycones or in a form of glycosides and malonyl-glycosides. Profiles of phenolic compounds in leaves of seedlings infected with anthracnose causing fungus Colletotrichum lupini were compared to those of healthy plants. A HPLC with diode array UV detector was used as the analytical method and identification of these secondary metabolites was confirmed with a HPLC/MSⁿ instrument. Isomers of several target compounds differing in the glycosilation and/or malonylation pattern were detected in the studied samples. However, the application of standard HPLC with C18 columns resulted in the co-elution of several glyconjugates in single chromatographic peaks whereas for isoflavonoid aglycones complete resolution was achieved. Lupine plants grown in a greenhouse were either sprayed with the C. lupini spore suspension or the suspension was spotted on to wounded leaves. Profiles of the isoflavones were altered in result to infection with both methods. In particular, the concentration of isoflavone free aglycones detected in extracts from diseased plants was substantially increased in all of the studied samples. However, the pattern of these compounds depended on the age of lupine leaves as well as on the method of infection. Synthesis of luteone and 2′-hydroxygenistein was enhanced in the youngest leaves of plants sprayed with spores as well as in wound-infected leaves. Wighteone synthesis was induced mainly in older leaves of plants sprayed with the spore suspension.     

Malabsorption syndrome with IgG(lambda) M component: response to chemotherapy.

A patient with malabsorption syndrome and steatorrhea was found to have IgG (lambda) M component in the blood and some extracellular deposition of IgG in the intestinal wall. There was no evidence of multiple myeloma. He responded favourably to intermittent courses of melphalan and prednisone.     

Organizational invariance and metabolic closure: analysis in terms of (M,R) systems

This article analyses the work of Robert Rosen on an interpretation of metabolic networks that he called (M,R) systems. His main contribution was an attempt to prove that metabolic closure (or metabolic circularity) could be explained in purely formal terms, but his work remains very obscure and we try to clarify his line of thought. In particular, we clarify the algebraic formulation of (M,R) systems in terms of mappings and sets of mappings, which is grounded in the metaphor of metabolism as a mathematical mapping. We define Rosen's central result as the mathematical expression in which metabolism appears as a mapping f that is the solution to a fixed-point functional equation. Crucially, our analysis reveals the nature of the mapping, and shows that to have a solution the set of admissible functions representing a metabolism must be drastically smaller than Rosen's own analysis suggested that it needed to be. For the first time, we provide a mathematical example of an (M,R) system with organizational invariance, and we analyse a minimal (three-step) autocatalytic set in the context of (M,R) systems. In addition, by extending Rosen's construction, we show how one might generate self-referential objects f with the remarkable property f(f)=f, where f acts in turn as function, argument and result. We conclude that Rosen's insight, although not yet in an easily workable form, represents a valuable tool for understanding metabolic networks.     

A priori analysis of metabolic flux identifiability from (13)C-labeling data

The (13)C-labeling technique was introduced in the field of metabolic engineering as a tool for determining fluxes that could not be found using the 'classical' method of flux balancing. An a priori flux identifiability analysis is required in order to determine whether a (13)C-labeling experiment allows the identification of all the fluxes. In this article, we propose a method for identifiability analysis that is based on the recently introduced 'cumomer' concept. The method improves upon previous identifiability methods in that it provides a way of systematically reducing the metabolic network on the basis of structural elements that constitute a network and to use the implicit function theorem to analytically determine whether the fluxes in the reduced network are theoretically identifiable for various types of real measurement data. Application of the method to a realistic flux identification problem shows both the potential of the method in yielding new, interesting conclusions regarding the identifiability and its practical limitations that are caused by the fact that symbolic calculations grow fast with the dimension of the studied system.     

Crystal structure of the resuscitation-promoting factor (DeltaDUF)RpfB from M. tuberculosis

Mycobacterium tuberculosis is able to establish a non-replicating state and survive in an intracellular habitat for years. Resuscitation of dormant M. tuberculosis bacteria is promoted by resuscitation-promoting factors (Rpfs), which are secreted from slowly replicating bacteria close to dormant bacteria. Here we report the crystal structure of a truncated form of RpfB (residues 194-362), the sole indispensable Rpf of the five Rpfs encoded in this bacterium genome. The structure, denoted as (DeltaDUF)RpfB, exhibits a comma-like shape formed by a lysozyme-like globular catalytic domain and an elongated G5 domain, which is widespread among cell surface binding proteins. The G5 domain, whose structure was previously uncharacterised, presents some peculiar features. The basic structural motif of this domain, which represents the tail of the comma-like structure, is a novel super-secondary-structure element, made of two beta-sheets interconnected by a pseudo-triple helix. This intricate organisation leads to the exposure of several backbone hydrogen-bond donors/acceptors. Mutagenesis analyses and solution studies indicate that this protein construct as well as the full-length form are elongated monomeric proteins. Although (DeltaDUF)RpfB does not self-associate, the exposure of structural elements (backbone H-bond donors/acceptors and hydrophobic side chains) that are usually buried in globular proteins is typically associated with adhesive properties. This suggests that the RpfB G5 domain has a cell-wall adhesive function, which allows the catalytic domain to be properly oriented for the cleavage reaction. Interestingly, sequence comparisons indicate that these structural features are also shared by G5 domains involved in biofilm formation.     

Bringing metabolic networks to life: integration of kinetic, metabolic, and proteomic data

Background

Translating a known metabolic network into a dynamic model requires reasonable guesses of all enzyme parameters. In Bayesian parameter estimation, model parameters are described by a posterior probability distribution, which scores the potential parameter sets, showing how well each of them agrees with the data and with the prior assumptions made.

Results

We compute posterior distributions of kinetic parameters within a Bayesian framework, based on integration of kinetic, thermodynamic, metabolic, and proteomic data. The structure of the metabolic system (i.e., stoichiometries and enzyme regulation) needs to be known, and the reactions are modelled by convenience kinetics with thermodynamically independent parameters. The parameter posterior is computed in two separate steps: a first posterior summarises the available data on enzyme kinetic parameters; an improved second posterior is obtained by integrating metabolic fluxes, concentrations, and enzyme concentrations for one or more steady states. The data can be heterogenous, incomplete, and uncertain, and the posterior is approximated by a multivariate log-normal distribution. We apply the method to a model of the threonine synthesis pathway: the integration of metabolic data has little effect on the marginal posterior distributions of individual model parameters. Nevertheless, it leads to strong correlations between the parameters in the joint posterior distribution, which greatly improve the model predictions by the following Monte-Carlo simulations.

Conclusion

We present a standardised method to translate metabolic networks into dynamic models. To determine the model parameters, evidence from various experimental data is combined and weighted using Bayesian parameter estimation. The resulting posterior parameter distribution describes a statistical ensemble of parameter sets; the parameter variances and correlations can account for missing knowledge, measurement uncertainties, or biological variability. The posterior distribution can be used to sample model instances and to obtain probabilistic statements about the model's dynamic behaviour.     

CONTROL: software for the analysis of the control of metabolic networks

The program CONTROL is based on metabolic control theory anduses the method developed by Reder (1988). In this theory, twosets of parameters are defined in the vicinity of a steady-state:the elasticity coefficients which describe the local behaviourof the isolated enzymes, and the control coefficients whichexpress the response of the whole metabolic network to perturbationsat a given step. The theory shows that relationships exist betweenthe control coefficients (summation relationships or structuralrelationships) and also between the two types of coefficients(control and elasticity coefficients: connectivity relationships).The program CONTROL is divided into two parts (sub-menus). Thefirst one calculates all the control coefficients (flux andconcentrations) of a metabolic network from the elasticity coefficients.Using the second menu, the symbolic relationships are obtainedbetween the control coefficients (summation relationships) andbetween the control coefficients and the elasticity coefficients(connectivity relationships). These two sub-menus can be appliedindependently to any metabolic network (to date limited to 19steps and 19 metabolites).     

Integrating data from biological experiments into metabolic networks with the DBE information system

Modern 'omics'-technologies result in huge amounts of data about life processes. For analysis and data mining purposes this data has to be considered in the context of the underlying biological networks. This work presents an approach for integrating data from biological experiments into metabolic networks by mapping the data onto network elements and visualising the data enriched networks automatically. This methodology is implemented in DBE, an information system that supports the analysis and visualisation of experimental data in the context of metabolic networks. It consists of five parts: (1) the DBE-Database for consistent data storage, (2) the Excel-Importer application for the data import, (3) the DBE-Website as the interface for the system, (4) the DBE-Pictures application for the up- and download of binary (e. g. image) files, and (5) DBE-Gravisto, a network analysis and graph visualisation system. The usability of this approach is demonstrated in two examples.