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Co-regulation of metabolic genes is better explained by flux coupling than by network distance.

Notebaart RA, Teusink B, Siezen RJ, Papp B - PLoS Comput. Biol. (2008)

Bottom Line: After validating the computational method with empirical data on flux correlations, we confirm that genes coupled by their enzymatic fluxes not only show similar expression patterns, but also share transcriptional regulators and frequently reside in the same operon.Moreover, the type of flux coupling can explain refined properties of the regulatory network that are ignored by simple graph-theoretical indices.Our results underline the importance of studying functional states of cellular networks to define physiologically relevant associations between genes and should stimulate future developments of novel functional genomic tools.

View Article: PubMed Central - PubMed

Affiliation: Center for Molecular and Biomolecular Informatics (NCMLS), Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands.

ABSTRACT
To what extent can modes of gene regulation be explained by systems-level properties of metabolic networks? Prior studies on co-regulation of metabolic genes have mainly focused on graph-theoretical features of metabolic networks and demonstrated a decreasing level of co-expression with increasing network distance, a naïve, but widely used, topological index. Others have suggested that static graph representations can poorly capture dynamic functional associations, e.g., in the form of dependence of metabolic fluxes across genes in the network. Here, we systematically tested the relative importance of metabolic flux coupling and network position on gene co-regulation, using a genome-scale metabolic model of Escherichia coli. After validating the computational method with empirical data on flux correlations, we confirm that genes coupled by their enzymatic fluxes not only show similar expression patterns, but also share transcriptional regulators and frequently reside in the same operon. In contrast, we demonstrate that network distance per se has relatively minor influence on gene co-regulation. Moreover, the type of flux coupling can explain refined properties of the regulatory network that are ignored by simple graph-theoretical indices. Our results underline the importance of studying functional states of cellular networks to define physiologically relevant associations between genes and should stimulate future developments of novel functional genomic tools.

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A Hypothetical Network with Metabolites (Nodes), Reactions (Arrows), and Exchange Reactions (Ex) with the EnvironmentIndicated are three types of flux coupling between reactions that are located at distance 1 (directly connected by one node): i) A-B: directionally coupled, ii) B-C: fully coupled, and iii) C-D: uncoupled.
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pcbi-0040026-g001: A Hypothetical Network with Metabolites (Nodes), Reactions (Arrows), and Exchange Reactions (Ex) with the EnvironmentIndicated are three types of flux coupling between reactions that are located at distance 1 (directly connected by one node): i) A-B: directionally coupled, ii) B-C: fully coupled, and iii) C-D: uncoupled.

Mentions: Since the functional state (phenotype) of metabolic networks is best represented by the actual flux distribution [11,12], one might expect that the correlation between reaction fluxes across network states would provide a sound and biochemically relevant measure of functional dependence between enzyme-encoding genes [13]. Therefore, we hypothesized that dynamic functional associations (i.e., correlations) between fluxes, rather than static topological properties of a metabolic network, could capture true functional associations between genes and consequently would provide refined insights into the modes of transcriptional regulation of metabolism. Recently, computationally tractable frameworks have been developed to determine genome-scale functional associations between metabolic genes on the basis of their coherent use of reactions (also referred to as “correlated reaction sets” or “flux coupling”, see Figure 1) [13–16]. Prior studies initialized the integration of gene regulation with flux coupling and concluded that genes with correlated reactions often show signs of co-regulation [14,17–19]. However, these studies did not explore the regulatory consequences of the differences in the degree of flux coupling. Moreover, it remains unknown to what extent flux dependencies relate to graph-theoretical properties of metabolic networks with respect to gene regulation.


Co-regulation of metabolic genes is better explained by flux coupling than by network distance.

Notebaart RA, Teusink B, Siezen RJ, Papp B - PLoS Comput. Biol. (2008)

A Hypothetical Network with Metabolites (Nodes), Reactions (Arrows), and Exchange Reactions (Ex) with the EnvironmentIndicated are three types of flux coupling between reactions that are located at distance 1 (directly connected by one node): i) A-B: directionally coupled, ii) B-C: fully coupled, and iii) C-D: uncoupled.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2211535&req=5

pcbi-0040026-g001: A Hypothetical Network with Metabolites (Nodes), Reactions (Arrows), and Exchange Reactions (Ex) with the EnvironmentIndicated are three types of flux coupling between reactions that are located at distance 1 (directly connected by one node): i) A-B: directionally coupled, ii) B-C: fully coupled, and iii) C-D: uncoupled.
Mentions: Since the functional state (phenotype) of metabolic networks is best represented by the actual flux distribution [11,12], one might expect that the correlation between reaction fluxes across network states would provide a sound and biochemically relevant measure of functional dependence between enzyme-encoding genes [13]. Therefore, we hypothesized that dynamic functional associations (i.e., correlations) between fluxes, rather than static topological properties of a metabolic network, could capture true functional associations between genes and consequently would provide refined insights into the modes of transcriptional regulation of metabolism. Recently, computationally tractable frameworks have been developed to determine genome-scale functional associations between metabolic genes on the basis of their coherent use of reactions (also referred to as “correlated reaction sets” or “flux coupling”, see Figure 1) [13–16]. Prior studies initialized the integration of gene regulation with flux coupling and concluded that genes with correlated reactions often show signs of co-regulation [14,17–19]. However, these studies did not explore the regulatory consequences of the differences in the degree of flux coupling. Moreover, it remains unknown to what extent flux dependencies relate to graph-theoretical properties of metabolic networks with respect to gene regulation.

Bottom Line: After validating the computational method with empirical data on flux correlations, we confirm that genes coupled by their enzymatic fluxes not only show similar expression patterns, but also share transcriptional regulators and frequently reside in the same operon.Moreover, the type of flux coupling can explain refined properties of the regulatory network that are ignored by simple graph-theoretical indices.Our results underline the importance of studying functional states of cellular networks to define physiologically relevant associations between genes and should stimulate future developments of novel functional genomic tools.

View Article: PubMed Central - PubMed

Affiliation: Center for Molecular and Biomolecular Informatics (NCMLS), Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands.

ABSTRACT
To what extent can modes of gene regulation be explained by systems-level properties of metabolic networks? Prior studies on co-regulation of metabolic genes have mainly focused on graph-theoretical features of metabolic networks and demonstrated a decreasing level of co-expression with increasing network distance, a naïve, but widely used, topological index. Others have suggested that static graph representations can poorly capture dynamic functional associations, e.g., in the form of dependence of metabolic fluxes across genes in the network. Here, we systematically tested the relative importance of metabolic flux coupling and network position on gene co-regulation, using a genome-scale metabolic model of Escherichia coli. After validating the computational method with empirical data on flux correlations, we confirm that genes coupled by their enzymatic fluxes not only show similar expression patterns, but also share transcriptional regulators and frequently reside in the same operon. In contrast, we demonstrate that network distance per se has relatively minor influence on gene co-regulation. Moreover, the type of flux coupling can explain refined properties of the regulatory network that are ignored by simple graph-theoretical indices. Our results underline the importance of studying functional states of cellular networks to define physiologically relevant associations between genes and should stimulate future developments of novel functional genomic tools.

Show MeSH
Related in: MedlinePlus