Metabolite essentiality elucidates robustness of Escherichia coli metabolism

Abstract

Complex biological systems are very robust to genetic and environmental changes at all levels of organization. Many biological functions of Escherichia coli metabolism can be sustained against single-gene or even multiple-gene mutations by using redundant or alternative pathways. Thus, only a limited number of genes have been identified to be lethal to the cell. In this regard, the reactioncentric gene deletion study has a limitation in understanding the metabolic robustness. Here, we report the use of flux-sum, which is the summation of all incoming or outgoing fluxes around a particular metabolite under pseudo-steady state conditions, as a good conserved property for elucidating such robustness of E. coli from the metabolite point of view. The functional behavior, as well as the structural and evolutionary properties of metabolites essential to the cell survival, was investigated by means of a constraints-based flux analysis under perturbed conditions. The essential metabolites are capable of maintaining a steady flux-sum even against severe perturbation by actively redistributing the relevant fluxes. Disrupting the flux-sum maintenance was found to suppress cell growth. This approach of analyzing metabolite essentiality provides insight into cellular robustness and concomitant fragility, which can be used for several applications, including the development of new drugs for treating pathogens. cellular robustness ͉ flux-sum ͉ metabolic networks A vailability of the complete genome sequences for well characterized organisms has led to the reconstruction of genome-scale metabolic networks, which represent a complex web of metabolites and their interconversions catalyzed by the gene products. Robustness, the inherent property of metabolic networks, enables the maintenance of cellular functions under various internally and externally perturbed conditions. This robustness has been experimentally demonstrated such that even the disruption of a considerable portion of genes could not affect the cell viability (1, 2). Although studies on the topological and functional properties of metabolic networks have achieved much progress (3-6), they still provide only a limited understanding of metabolic robustness. The conventional attempt to study such robustness relies on the identification of the genes or reactions indispensable to a cell. However, universal metabolic pathways across species, such as the tricarboxylic acid (TCA) cycle or glycolytic pathways, have relatively few lethal reactions (1, 7). This fact indicates that the more important a reaction is, the higher is the chance to have a backup pathway (7). Thus, the functionally important reactions are not necessarily lethal, and this point places a limitation to the reaction-centric approach with studying lethality by observing the gene deletion effects. In this regard, we have investigated the interplay between cellular robustness and the underlying metabolism from the metabolite point of view, and how the robustness can be accomplished at the level of the metabol...

Conditional Silencing of the <i>Escherichia coli pykF</i> Gene Results from Artificial Convergent Transcription Protected from Rho-Dependent Termination

“…Switching from the bacterial growth stage to bioconversion of substrates into desired products could be based on metabolically inducible transcription of recombinant pathway genes [Farmer and Liao, 2000;Imaizumi et al, 2007]. On the other hand, the silencing of some key genes could lead to a significant redistribution of the precursors into a desired metabolic branch as the result of a compensatory strategy [Kim et al, 2007], thus enabling the cell to maintain a stable state when exposed to various types of perturbations. At the very least, the deletions of some Escherichia coli genes involved in central metabolism [Mascarenhas et al, 1991] and/or global regulation [Tatarko and Romeo, 2001] of highly engineered strains lead to a greater increase in the performance of the strain than the more obvious genetic manipulations that amplify genes directly involved in a recombinant pathway.…”

Network-based approaches for anticancer therapy

“…In addition to protein networks, disruption of the networks containing essential metabolites (hub metabolites) could cause severe cellular damage that would negatively impact cell survival and growth. However, when the processes involving nonessential metabolites are interfered with, cellular functions are not significantly affected (23). These data demonstrate that targeting hubs can cause considerable damage to an overall cellular network compared to targeting non-hub molecules.…”

The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli

“…Such studies will be further enabled by the recent availability of a comprehensive single-gene knock-out library for E. coli 95 (for example 19,53 ). Implications for examining network essentiality in E. coli include determining network essentiality in similar organisms 39,48,53,58 , deciphering network makeup and enzyme dispensability (i.e., measures of robustness) 46,58,60 , aiding in metabolic network annotation, validation and refinement 44 , and even rescuing knock-out strains through additional gene deletions 63 , to name a few. The predictive capability of the E. coli GEM, as demonstrated by these studies, has been instrumental in the adaptation of its use.…”

“…A range of computational studies have sought to understand phenotypes through determining the essential genes 19,46,51,53,63 , metabolites 44,60 and reactions 39,47,48,58 in the E. coli metabolic network. A common benchmark for examining GEM predictive ability is to determine the agreement with growth phenotype data from knock-out collections of E. coli.…”

“…(B) Recent studies utilizing the reconstruction in a prospective manner have aimed to use the current biochemical and genetic information included in the metabolic network along with additional data types to drive biological discovery, such as predicting genes encoding for orphan reactions 32,33,[35][36][37] . (C) Utilizing the reconstruction in phenotypic studies, computational analyses have examined gene 19,46,51,53,63 , metabolite 44,60 and reaction 39,47,48,58 essentiality along with considering thermodynamics 19,40,47,49,52,54,55,57,61 to make better predictions about the physiological state (i.e., the active pathways) of the cell for a given environmental condition. (D) The E. coli reconstructions have been used to analyze and interpret the intrinsic properties of biological networks.…”