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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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The Average Level of Empirically Determined Flux Correlations for Different Flux Coupling Types (A) and at Different Network Distances (B)
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pcbi-0040026-g002: The Average Level of Empirically Determined Flux Correlations for Different Flux Coupling Types (A) and at Different Network Distances (B)

Mentions: Although phylogenetic [19] and metabolome [22] studies suggest that in silico predicted flux coupling relationships have strong physiological and evolutionary relevance, it remains unexamined how well this procedure can explain in vivo flux correlations. For example, is directional coupling a physiologically relevant category in the sense that these reactions show some, but not perfect flux correlations? An experimental study enabled us to calculate flux correlations between 120 reaction pairs over six conditions in the central carbon metabolism of E. coli [23]. Although none of these reaction pairs were fully coupled, we found a marked difference between the two other coupling groups: directionally coupled reaction pairs had, on average, much higher empirical flux correlations than uncoupled ones (Wilcox robust analysis of variance, ANOVA, p < 10−14, Figure 2A, see Methods).


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)

The Average Level of Empirically Determined Flux Correlations for Different Flux Coupling Types (A) and at Different Network Distances (B)
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-0040026-g002: The Average Level of Empirically Determined Flux Correlations for Different Flux Coupling Types (A) and at Different Network Distances (B)
Mentions: Although phylogenetic [19] and metabolome [22] studies suggest that in silico predicted flux coupling relationships have strong physiological and evolutionary relevance, it remains unexamined how well this procedure can explain in vivo flux correlations. For example, is directional coupling a physiologically relevant category in the sense that these reactions show some, but not perfect flux correlations? An experimental study enabled us to calculate flux correlations between 120 reaction pairs over six conditions in the central carbon metabolism of E. coli [23]. Although none of these reaction pairs were fully coupled, we found a marked difference between the two other coupling groups: directionally coupled reaction pairs had, on average, much higher empirical flux correlations than uncoupled ones (Wilcox robust analysis of variance, ANOVA, p < 10−14, Figure 2A, see Methods).

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