A systematic investigation of Escherichia coli central carbon metabolism in response to superoxide stress

Abstract

Background

The cellular responses of bacteria to superoxide stress can be used to model adaptation to severe environmental changes. Superoxide stress promotes the excessive production of reactive oxygen species (ROS) that have detrimental effects on cell metabolic and other physiological activities. To antagonize such effects, the cell needs to regulate a range of metabolic reactions in a coordinated way, so that coherent metabolic responses are generated by the cellular metabolic reaction network as a whole. In the present study, we have used a quantitative metabolic flux analysis approach, together with measurement of gene expression and activity of key enzymes, to investigate changes in central carbon metabolism that occur in Escherichia coli in response to paraquat-induced superoxide stress. The cellular regulatory mechanisms involved in the observed global flux changes are discussed.

Results

Flux analysis based on nuclear magnetic resonance (NMR) and mass spectroscopy (MS) measurements and computation provided quantitative results on the metabolic fluxes redistribution of the E. coli central carbon network under paraquat-induced oxidative stress. The metabolic fluxes of the glycolytic pathway were redirected to the pentose phosphate pathway (PP pathway). The production of acetate increased significantly, the fluxes associated with the TCA cycle decreased, and the fluxes in the glyoxylate shunt increased in response to oxidative stress. These global flux changes resulted in an increased ratio of NADPH:NADH and in the accumulation of α-ketoglutarate.

Conclusions

Metabolic flux analysis provided a quantitative and global picture of responses of the E. coli central carbon metabolic network to oxidative stress. Systematic adjustments of cellular physiological state clearly occurred in response to changes in metabolic fluxes induced by oxidative stress. Quantitative flux analysis therefore could reveal the physiological state of the cell at the systems level and is a useful complement to molecular systems approaches, such as proteomics and transcription analyses.