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United States Department of Agriculture

Agricultural Research Service

Title: Synergy between 13C-metabolic flux analysis and flux balance analysis for understanding metabolic adaption to anaerobiosis in e. coli

item Chen, Xuewen -
item Alonso, Ana -
item Allen, Douglas
item Reed, Jennifer -
item Shachar-Hill, Yair -

Submitted to: Metabolic Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: November 16, 2010
Publication Date: December 1, 2010
Citation: Chen, X., Alonso, A.P., Allen, D.K., Reed, J.L., Shachar-Hill, Y. 2010. Synergy between 13C-metabolic flux analysis and flux balance analysis for understanding metabolic adaption to anaerobiosis in E. coli. Metabolic Engineering. 13:38-48.

Interpretive Summary: Biotechnology, including genetic engineering, offers the potential to rationally alter living systems to produce valuable compounds for mankind. However, often the results from such attempts are unexpected and inconsistent with our preconceived understanding, reflecting our lack of knowledge about how organisms function as a whole, i.e. at a systems level. The flow of metabolites through a metabolic pathway, or flux, represents a direct readout of cellular metabolism and thus provides insights into how to rationally engineer living systems. We have employed two complementary methods of flux analysis to better understand the value of each, the synergy that can be gained by their coordinated use, and to shed light on primary metabolism that governs cellular physiology. One method focuses upon genome-level descriptions of metabolism and employees an objective function (e.g. the model is optimized to maximize biomass production) and can therefore be used in a predictive fashion but may give multiple solutions. The second and related method relies upon experimental data obtained from labeling compounds with isotopes. This method results in a unique solution and does not require the assumption of an objective function but is not predictive. The complementary and synergistic use of these approaches are highlighted in this study with a model system, contributing to our understanding of metabolism. Ultimately the results of this study benefit our understanding how cellular physiology may be rationally manipulated for crop improvement strategies in the future.

Technical Abstract: Genome-based Flux Balance Analysis (FBA, constraints based flux analysis) and steady state isotopic-labeling-based Metabolic Flux Analysis (MFA) are complimentary approaches to predicting and measuring the operation and regulation of metabolic networks. Here a genome-derived model of E. coli metabolism (Reed et al., 2003) was used for both FBA and 13C-MFA analyses of aerobic and anaerobic growth of wild type Mg1655 cells cultured on glucose in minimal medium. Substrate uptake and product secretion rates were quantified using NMR and enzymatic assays and the labeling of amino- organic- and fatty-acids and several phosphorylated metabolic intermediates were measured using gas and liquid chromatographic mass spectrometry methods. These data were used with an isotopic network model to obtain and validate maps of carbon and energy flows through metabolism. The flux maps reveal that the maintenance ATP consumption is about two-fold higher under anaerobic (12.6 mmol ATP/g/h) than aerobic conditions (7.1 mmol ATP/g/h). The increased ATP utilization is consumed by ATP synthase to secrete excessive protons accumulated in fermentation, explaining a previous finding that introducing Formate Hydrogen Lyase leads to increased growth rates. The combination of FBA and MFA results also reveal that the TCA cycle is incomplete in aerobically growing E. coli on glucose in defined media and that submaximal growth is due to limited oxidative phosphorylation. FBA was successful in predicting product secretion rates in aerobic culture if both glucose and oxygen uptake measurement were constrained, but had limited ability to predict internal fluxes. While the fluxes established from MFA were within the FBA feasible range, the values of MFA derived fluxes differ substantially from the most-frequently predicted values of internal fluxes yielded from sampling the feasible space. Both rational strain design and physiological insight from flux analysis are likely to benefit substantially from the combined use of both methods

Last Modified: 11/29/2015
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