Uncovering transcriptional regulation of metabolism by using metabolic network topology

Abstract

Cellular response to genetic and environmental perturbations is often reflected and͞or 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.bioinformatics ͉ reporter metabolites ͉ metabolic subnetworks L inking the genome to its functioning metabolism is of substantial interest not only in studying human diseases (1) but also for identifying metabolic engineering targets in biotechnological applications (2, 3). Transcriptional analysis represents a high-throughput and genome-wide approach for linking the set of expressed genes to functional metabolism of the cell. Indeed, several studies using genome-wide gene-expression analysis have shown that transcriptional regulation plays an important role in regulating metabolism in response to perturbations (4-6). Although many statistical methods and clustering algorithms provide tools to analyze such transcriptomics data (7-9), these methods seldom provide insight into the regulatory architecture of the metabolic networks without intelligent analysis of the results (up͞down-regulation of genes of interest or correlation between genes of interest). This shortcoming is primarily due to the hypothesis that there may be all-to-all interactions among the genes being analyzed, resulting into many biologically nonsignificant results. One of the ways to address this problem is to integrate known biological interactions, e.g., protein-protein interactions, in the analysis of transcription data (10). Such an approach essentially reduces the degrees of freedom in data analysis by using knowledge of molecular interactions occurring in the cell. The organization and functioning of the cell can be ...

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].…”

“…These molecules [e.g. ATP, NAD(H), S-adenosylmethionine (SAM)] are responsible for most of the interconnectivity within the metabolic network [4]. A change in these metabolites does not allow tracking a perturbation.…”

“…These interactions are used to fine-tune the activity of metabolic modules, to detect network perturbations and to facilitate the functioning of metabolic pathways in signalling cascades. 4 are triggered by classic components of the metabolic network. A recent report showed that histone acetyltransferases (HATs) fall under metabolic control, as they are dynamically regulated by physiologic changes in acetylcoenzyme (Co)A concentration.…”

“…35 No. 4 anticipatory upregulation of purine biosynthesis and salvage enzymes [46]. Two intermediates, AICAR (5'-phosphoribosyl-5-amino-4-imidazole carboxamide) and SAICAR (succinyl-AICAR), initiate this regulatory adaptation (Figure 4).…”

“…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.…”