Metabolic pathway analysis of yeast strengthens the bridge between transcriptomics and metabolic networks

Abstract

Central carbon metabolism of the yeast Saccharomyces cerevisiae was analyzed using metabolic pathway analysis tools. Elementary flux modes for three substrates (glucose, galactose, and ethanol) were determined using the catabolic reactions occurring in yeast. Resultant elementary modes were used for gene deletion phenotype analysis and for the analysis of robustness of the central metabolism and network functionality. Control-effective fluxes, determined by calculating the efficiency of each mode, were used for the prediction of transcript ratios of metabolic genes in different growth media (glucose-ethanol and galactose-ethanol). A high correlation was obtained between the theoretical and experimental expression levels of 38 genes when ethanol and glucose media were considered. Such analysis was shown to be a bridge between transcriptomics and fluxomics. Control-effective flux distribution was found to be promising in in silico predictions by incorporating functionality and regulation into the metabolic network structure.

Yeast as a Prototype for Systems Biology

“…This reflects a marked shift in the utilization of different metabolic modes, which are the likely superpositions of other EFMs. In fact, a very strong correlation was observed between the EFMs, as determined for yeast grown on different carbon sources, and the transcript measurements from microarray experiments [17]. Above all, the method of determining the control-effective fluxes to calculate the theoretical transcript values and correlating them with the experimentally derived transcript ratios demonstrates the importance of flexibility in metabolic networks.…”

Quantification of metabolism in Saccharomyces cerevisiae under hyperosmotic conditions using elementary mode analysis

“…However, in the context of osmotic stress, only the central carbon metabolism is of primary interest. The metabolic model for the central carbon metabolism based on information provided by the literature [5,29,30] includes glycolytic reactions, the TCA cycle, and the pentose phosphate pathway (see Table S2 of the Electronic supplementary material, ESM). As our main focus is on the distribution of carbon into the biomass and glycerol, we do not consider the compartmentalization of the TCA cycle.…”

“…As our main focus is on the distribution of carbon into the biomass and glycerol, we do not consider the compartmentalization of the TCA cycle. The biomass is represented in terms of equivalent stoichiometric quantities of biosynthetic precursors [5]. The metabolism incorporates the uptakes of glucose and oxygen as substrates, while ethanol, acetate, glycerol and carbon dioxide form the products.…”

Elementary Mode Analysis: A Useful Metabolic Pathway Analysis Tool for Reprograming Microbial Metabolic Pathways

“…The ability to identify all feasible EMs inherent to a metabolic network with unique properties makes EMA a useful metabolic pathway analysis tool for a wide range of applications including systematic characterization of cellular phenotypes (Cakir et al 2004(Cakir et al , 2007Carlson 2007 ;Stelling et al 2002 ;Wessely et al 2011 ) , robustness (Larhlimi et al 2011 ;Min et al 2011 ;Stelling et al 2002Stelling et al , 2004 , fragility (Behre et al 2008 ;Klamt 2006 ;Klamt and Gilles 2004 ;Tepper and Shlomi 2010 ;Wilhelm et al 2004 ) , phenotypic behavior discovery Kenanov et al 2010 ;Rügen et al 2012 ;Schauble et al 2011 ) , identi fi cation of pharmaceutical or therapeutic targets (Beuster et al 2011 ) , and rational strain design for metabolic engineering applications (Table 2.2 ). Several comprehensive reviews have discussed some of these applications in detail (Medema et al 2012 ;Schaeuble et al 2011 ;Schuster et al 1999Schuster et al , 2006 ) so this chapter will focus on the reprogramming of microbial metabolic pathways for rational strain design and the metabolic pathway evolution of designed strains.…”