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Estimating Metabolic Fluxes Using a Maximum Network Flexibility Paradigm.

ABSTRACT: Genome-scale metabolic networks can be modeled in a constraint-based fashion. Reaction stoichiometry combined with flux capacity constraints determine the space of allowable reaction rates. This space is often large and a central challenge in metabolic modeling is finding the biologically most relevant flux distributions. A widely used method is flux balance analysis (FBA), which optimizes a biologically relevant objective such as growth or ATP production. Although FBA has proven to be highly useful for predicting growth and byproduct secretion, it cannot predict the intracellular fluxes under all environmental conditions. Therefore, alternative strategies have been developed to select flux distributions that are in agreement with experimental "omics" data, or by incorporating experimental flux measurements. The latter, unfortunately can only be applied to a limited set of reactions and is currently not feasible at the genome-scale. On the other hand, it has been observed that micro-organisms favor a suboptimal growth rate, possibly in exchange for a more "flexible" metabolic network. Instead of dedicating the internal network state to an optimal growth rate in one condition, a suboptimal growth rate is used, that allows for an easier switch to other nutrient sources. A small decrease in growth rate is exchanged for a relatively large gain in metabolic capability to adapt to changing environmental conditions.Here, we propose Maximum Metabolic Flexibility (MMF) a computational method that utilizes this observation to find the most probable intracellular flux distributions. By mapping measured flux data from central metabolism to the genome-scale models of Escherichia coli and Saccharomyces cerevisiae we show that i) indeed, most of the measured fluxes agree with a high adaptability of the network, ii) this result can be used to further reduce the space of feasible solutions iii) this reduced space improves the quantitative predictions made by FBA and contains a significantly larger fraction of the measured fluxes compared to the flux space that was reduced by a uniform sampling approach and iv) MMF can be used to select reactions in the network that contribute most to the steady-state flux space. Constraining the selected reactions improves the quantitative predictions of FBA considerably more than adding an equal amount of flux constraints, selected using a more naïve approach. Our method can be applied to any cell type without requiring prior information.MMF is freely available as a MATLAB plugin at: http://cs.ru.nl/~wmegchel/mmf.

SUBMITTER: Megchelenbrink W

PROVIDER: S-EPMC4601694 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Estimating Metabolic Fluxes Using a Maximum Network Flexibility Paradigm.

Megchelenbrink Wout W Rossell Sergio S Huynen Martijn A MA Notebaart Richard A RA Marchiori Elena E

PloS one 20151012 10

<h4>Motivation</h4>Genome-scale metabolic networks can be modeled in a constraint-based fashion. Reaction stoichiometry combined with flux capacity constraints determine the space of allowable reaction rates. This space is often large and a central challenge in metabolic modeling is finding the biologically most relevant flux distributions. A widely used method is flux balance analysis (FBA), which optimizes a biologically relevant objective such as growth or ATP production. Although FBA has pro ...[more]

PMID: 26457579

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Project description:Environmental fluctuations in the availability of nutrients lead to intricate metabolic strategies. "Candidatus Accumulibacter phosphatis," a polyphosphate-accumulating organism (PAO) responsible for enhanced biological phosphorus removal (EBPR) from wastewater treatment systems, is prevalent in aerobic/anaerobic environments. While the overall metabolic traits of these bacteria are well described, the nonavailability of isolates has led to controversial conclusions on the metabolic pathways used. In this study, we experimentally determined the redox cofactor preferences of different oxidoreductases in the central carbon metabolism of a highly enriched "Ca Accumulibacter phosphatis" culture. Remarkably, we observed that the acetoacetyl coenzyme A reductase engaged in polyhydroxyalkanoate (PHA) synthesis is NADH preferring instead of showing the generally assumed NADPH dependency. This allows rethinking of the ecological role of PHA accumulation as a fermentation product under anaerobic conditions and not just a stress response. Based on previously published metaomics data and the results of enzymatic assays, a reduced central carbon metabolic network was constructed and used for simulating different metabolic operating modes. In particular, scenarios with different acetate-to-glycogen consumption ratios were simulated, which demonstrated optima using different combinations of glycolysis, glyoxylate shunt, or branches of the tricarboxylic acid (TCA) cycle. Thus, optimal metabolic flux strategies will depend on the environment (acetate uptake) and on intracellular storage compound availability (polyphosphate/glycogen). This NADH-related metabolic flexibility is enabled by the NADH-driven PHA synthesis. It allows for maintaining metabolic activity under various environmental substrate conditions, with high carbon conservation and lower energetic costs than for NADPH-dependent PHA synthesis. Such (flexible) metabolic redox coupling can explain the competitiveness of PAOs under oxygen-fluctuating environments.IMPORTANCE Here, we demonstrate how microbial storage metabolism can adjust to a wide range of environmental conditions. Such flexibility generates a selective advantage under fluctuating environmental conditions. It can also explain the different observations reported in PAO literature, including the capacity of "Ca Accumulibacter phosphatis" to act like glycogen-accumulating organisms (GAOs). These observations stem from slightly different experimental conditions, and controversy arises only when one assumes that metabolism can operate only in a single mode. Furthermore, we also show how the study of metabolic strategies is possible when combining omics data with functional cofactor assays and modeling. Genomic information can only provide the potential of a microorganism. The environmental context and other complementary approaches are still needed to study and predict the functional expression of such metabolic potential.

Dataset Information

Estimating Metabolic Fluxes Using a Maximum Network Flexibility Paradigm.

Publications

Estimating Metabolic Fluxes Using a Maximum Network Flexibility Paradigm.

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