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.

Regulatory crosstalk of the metabolic network

“…Reporter metabolites crosstalk with the cellular regulome Patil and Nielsen identified reporter metabolites based on an enrichment analysis of transcriptional changes; they are nodes balanced by the transcriptome [4,5]. This definition can be extended, as more recent studies show that the metabolic flux is often controlled purely on a posttranslational basis [20,24].…”

“…However, one type of regulation appears to be common: representative network intermediates bind to and modulate sensor function and activity of transcription factors, translational regulators, chromatin, enzymes, RNA molecules, and ion channels. Significant transcriptional changes around these intermediates identified them as reporter metabolites [4,5]. The use of intermediates as activity reporters necessitates a modular structure of the metabolic network; a semi-independent flux of its substructures is an underlying premise for this type of regulation.…”

Industrial systems biology

“…Using transcriptome experimental data, predictions a priori of which metabolites are likely to be affected can be made, and serve as rational targets for additional inspection and metabolic engineering. This algorithm has been recently extended to include reporter reactions, whereby metabolome data are correlated with the metabolic reactions of the reconstructed S. cerevisiae genome-scale metabolic network model to identify those reactions around which a genetic or environmental perturbation confer metabolite changes (Cakir et al, 2006).…”

Systems Biology: The synergistic interplay between biology and mathematics

“…They used this approach to integrate gene expression data from S. cerevisiae with the metabolic network and identified metabolites in parts of the network which was perturbed suggesting that this approach could be used to analyze the mechanism of action of a perturbation given the expression data. Recently, Cakir et al (2006) used a similar approach to integrate metabolomic and transcriptional regulation data from 84 intracellular metabolites and analyzed data from a laboratory and an industrial strain of S. cerevisiae used for ethanol production. The results of this analysis allowed the discrimination of the dominant regulatory process in the two strains as metabolic regulation or transcriptional regulation providing valuable insights for potential engineering of these strains for improved biocatalytic performance.…”