Uncovering transcriptional regulation of metabolism by using metabolic network topology

Abstract

Cellular response to genetic and environmental perturbations is often reflected and/or mediated through changes in the metabolism, because the latter plays a key role in providing Gibbs free energy and precursors for biosynthesis. Such metabolic changes are often exerted through transcriptional changes induced by complex regulatory mechanisms coordinating the activity of different metabolic pathways. It is difficult to map such global transcriptional responses by using traditional methods, because many genes in the metabolic network have relatively small changes at their transcription level. We therefore developed an algorithm that is based on hypothesis-driven data analysis to uncover the transcriptional regulatory architecture of metabolic networks. By using information on the metabolic network topology from genome-scale metabolic reconstruction, we show that it is possible to reveal patterns in the metabolic network that follow a common transcriptional response. Thus, the algorithm enables identification of so-called reporter metabolites (metabolites around which the most significant transcriptional changes occur) and a set of connected genes with significant and coordinated response to genetic or environmental perturbations. We find that cells respond to perturbations by changing the expression pattern of several genes involved in the specific part(s) of the metabolism in which a perturbation is introduced. These changes then are propagated through the metabolic network because of the highly connected nature of metabolism.

Fig. 1.

Illustration of the proposed algorithm for identifying reporter metabolites and subnetwork structures signifying transcriptionally regulated modules. A metabolic network (set of reactions) is converted to bipartite (metabolic) and unipartite (enzyme-interaction) graph representations. Gene-expression data from a particular experiment then is used to identify highly regulated metabolites (reporter metabolites) and significantly correlated subnetworks in the enzyme-interaction graph.

Fig. 2.

Parts of S. cerevisiae metabolism that are represented in the subnetwork identified for the Δgdh1 data set. Genes present in the subnetwork are shown in boxes.