Co-regulation of metabolic genes is better explained by flux coupling than by network distance - PubMed (original) (raw)

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

Richard A Notebaart et al. PLoS Comput Biol. 2008 Jan.

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.

PubMed Disclaimer

Conflict of interest statement

Competing interests. The authors have declared that no competing interests exist.

Figures

Figure 1

Figure 1. A Hypothetical Network with Metabolites (Nodes), Reactions (Arrows), and Exchange Reactions (Ex) with the Environment

Indicated 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.

Figure 2

Figure 2. The Average Level of Empirically Determined Flux Correlations for Different Flux Coupling Types (A) and at Different Network Distances (B)

Figure 3

Figure 3. The Effect of Flux Coupling and Network Distance on Operonic Organization in E. coli

(A) The fraction of intra-operonic gene pairs correlates with the type of flux coupling. The dashed baseline indicates the fraction of intra-operonic gene pairs expected by chance. (B) The effect of flux coupling on the fraction of intra-operonic gene pairs in different network distance groups: χ2 d=1 = 715.3, χ2 d=2,3,4 = 5347.3, χ2 d≥5 = 5022.3, d.f. = 2, and p < 10−155.

Figure 4

Figure 4. Transcription Factor (TF) Similarity Correlates with the Type of Flux Coupling

Figure 5

Figure 5. The Effect of Flux Coupling and Network Distance on Co-Expression for E. coli (A) and S. cerevisiae (B)

(A) The dashed baseline indicates the degree of co-expression between random gene pairs. The confidence interval of directionally coupled pairs at d ≥ 5 is absent, as it contains too few data points (n = 2) for reliable calculation. (B) Relative variance components (i.e., the fraction of total variance in co-expression explained by coupling and distance) were estimated by a general linear model where both flux coupling and distance were treated as random effects in an unbalanced factorial ANOVA design. Expected means squares were used for the estimation (Statistica 6.0, Statsoft). Flux coupling and network distance explain 16.8% and 7.3% of the variance in co-expression, respectively (interaction between the two factors explains 3.7%). A maximum likelihood estimation of variance components gave very similar results (coupling: 14%, distance: 7.1%, and interaction: 3.8%, Statistica 6.0, Statsoft). Note that the average network distance is ∼4.5.

Similar articles

Cited by

References

    1. Reed JL, Famili I, Thiele I, Palsson BO. Towards multidimensional genome annotation. Nat Rev Genet. 2006;7:130–141. - PubMed
    1. Ihmels J, Levy R, Barkai N. Principles of transcriptional control in the metabolic network of Saccharomyces cerevisiae . Nat Biotechnol. 2004;22:86–92. - PubMed
    1. Kharchenko P, Church GM, Vitkup D. Expression dynamics of a cellular metabolic network. Mol Syst Biol. 2005;1:16. - PMC - PubMed
    1. Patil KR, Nielsen J. Uncovering transcriptional regulation of metabolism by using metabolic network topology. Proc Natl Acad Sci U S A. 2005;102:2685–2689. - PMC - PubMed
    1. Schramm G, Zapatka M, Eils R, Konig R. Using gene expression data and network topology to detect substantial pathways, clusters and switches during oxygen deprivation of Escherichia coli. BMC Bioinformatics. 2007;8:149. - PMC - PubMed

Publication types

MeSH terms

Substances

LinkOut - more resources