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Integration of metabolome data with metabolic networks reveals reporter reactions.

ABSTRACT: Interpreting quantitative metabolome data is a difficult task owing to the high connectivity in metabolic networks and inherent interdependency between enzymatic regulation, metabolite levels and fluxes. Here we present a hypothesis-driven algorithm for the integration of such data with metabolic network topology. The algorithm thus enables identification of reporter reactions, which are reactions where there are significant coordinated changes in the level of surrounding metabolites following environmental/genetic perturbations. Applicability of the algorithm is demonstrated by using data from Saccharomyces cerevisiae. The algorithm includes preprocessing of a genome-scale yeast model such that the fraction of measured metabolites within the model is enhanced, and thus it is possible to map significant alterations associated with a perturbation even though a small fraction of the complete metabolome is measured. By combining the results with transcriptome data, we further show that it is possible to infer whether the reactions are hierarchically or metabolically regulated. Hereby, the reported approach represents an attempt to map different layers of regulation within metabolic networks through combination of metabolome and transcriptome data.

SUBMITTER: Cakir T

PROVIDER: S-EPMC1682015 | biostudies-literature | 2006

REPOSITORIES: biostudies-literature

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Integration of metabolome data with metabolic networks reveals reporter reactions.

Cakir Tunahan T Patil Kiran Raosaheb KR Onsan Zeynep iIsen Zi Ulgen Kutlu Ozergin KO Kirdar Betül B Nielsen Jens J

Molecular systems biology 20061003

Interpreting quantitative metabolome data is a difficult task owing to the high connectivity in metabolic networks and inherent interdependency between enzymatic regulation, metabolite levels and fluxes. Here we present a hypothesis-driven algorithm for the integration of such data with metabolic network topology. The algorithm thus enables identification of reporter reactions, which are reactions where there are significant coordinated changes in the level of surrounding metabolites following e ...[more]

PMID: 17016516

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Project description:MotivationOne central goal of systems biology is to infer biochemical regulations from large-scale OMICS data. Many aspects of cellular physiology and organismal phenotypes can be understood as results of metabolic interaction network dynamics. Previously, we have proposed a convenient mathematical method, which addresses this problem using metabolomics data for the inverse calculation of biochemical Jacobian matrices revealing regulatory checkpoints of biochemical regulations. The proposed algorithms for this inference are limited by two issues: they rely on structural network information that needs to be assembled manually, and they are numerically unstable due to ill-conditioned regression problems for large-scale metabolic networks.ResultsTo address these problems, we developed a novel regression loss-based inverse Jacobian algorithm, combining metabolomics COVariance and genome-scale metabolic RECONstruction, which allows for a fully automated, algorithmic implementation of the COVRECON workflow. It consists of two parts: (i) Sim-Network and (ii) inverse differential Jacobian evaluation. Sim-Network automatically generates an organism-specific enzyme and reaction dataset from Bigg and KEGG databases, which is then used to reconstruct the Jacobian's structure for a specific metabolomics dataset. Instead of directly solving a regression problem as in the previous workflow, the new inverse differential Jacobian is based on a substantially more robust approach and rates the biochemical interactions according to their relevance from large-scale metabolomics data. The approach is illustrated by in silico stochastic analysis with differently sized metabolic networks from the BioModels database and applied to a real-world example. The characteristics of the COVRECON implementation are that (i) it automatically reconstructs a data-driven superpathway model; (ii) more general network structures can be investigated, and (iii) the new inverse algorithm improves stability, decreases computation time, and extends to large-scale models.Availability and implementationThe code is available in the website https://bitbucket.org/mosys-univie/covrecon.

Project description:BackgroundTranslating 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.ResultsWe 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 heterogeneous, 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.ConclusionWe 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.

Project description:Stalk lodging, or breakage of the stalk at or below the ear, is one of the vital factors causing substantial yield losses in maize (Zea mays. L). Lodging affects maize plants' physiological and molecular processes, eventually impacting plant growth and productivity. Despite this known fact, few researchers have investigated the genetic architecture underlying lodging in maize. Herein, through integrated transcriptome, metabolome, and phenotypic analyses of stalks of three diverse hybrid cultivars (highly resistant JNK738, mildly resistant JNK728, and lowly resistant XY335) at the tasseling (10 days to silking, 10 DTS) stage, we identified key genes and metabolic pathways modulating lodging resistance in maize. Based on the RNA-Seq analysis, a total of 10093 differentially expressed genes (DEGs) were identified from the comparison of the three varieties in pairs. Additionally, key lodging resistance-related metabolic pathways were obtained by KEGG enrichment analysis, and the DEGs were found predominantly enriched in phenylpropanoid and secondary metabolites biosynthesis pathways in the L_vs._H and M_vs._H comparison groups. Moreover, K-means analysis clustered the DEGs into clear and distinct expression profiles for each cultivar, with several functional and regulatory genes involved in the cell wall assembly, lignin biosynthetic process and hormone metabolic process being identified in the special clusters related to lodging resistance. Subsequently, integrating metabolome and transcriptome analyses revealed nine key lignin-associated metabolites that showed different expression trends in the three hybrid cultivars, among which L-phenylalanine and p-coumaric acid were regarded as differentially changed metabolites (DCMs). These two DCMs belonged to phenylalanine metabolism and biosynthesis pathways and were also supported by the RNA-Seq data. Furthermore, plant hormone signal transduction pathway-related genes encoding auxin, abscisic acid, jasmonates, and salicylic acid were differentially expressed in the three comparisons of lodging resistance, indicating these DEGs were valuable potential targets for improving maize lodging resistance. Finally, comparative physiological and qRT-PCR analyses results supported our transcriptome-based findings. Our research not only provides a preliminary theoretical basis and experimental ideas for an in-depth study of the regulatory networks involved in maize lodging resistance regulation but also opens up new avenues for molecular maize stalk lodging resistance breeding.

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Integration of metabolome data with metabolic networks reveals reporter reactions.

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Integration of metabolome data with metabolic networks reveals reporter reactions.

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