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Optimal tracers for parallel labeling experiments and 13C metabolic flux analysis: A new precision and synergy scoring system.

ABSTRACT: 13C-Metabolic flux analysis (13C-MFA) is a widely used approach in metabolic engineering for quantifying intracellular metabolic fluxes. The precision of fluxes determined by 13C-MFA depends largely on the choice of isotopic tracers and the specific set of labeling measurements. A recent advance in the field is the use of parallel labeling experiments for improved flux precision and accuracy. However, as of today, no systemic methods exist for identifying optimal tracers for parallel labeling experiments. In this contribution, we have addressed this problem by introducing a new scoring system and evaluating thousands of different isotopic tracer schemes. Based on this extensive analysis we have identified optimal tracers for 13C-MFA. The best single tracers were doubly 13C-labeled glucose tracers, including [1,6-13C]glucose, [5,6-13C]glucose and [1,2-13C]glucose, which consistently produced the highest flux precision independent of the metabolic flux map (here, 100 random flux maps were evaluated). Moreover, we demonstrate that pure glucose tracers perform better overall than mixtures of glucose tracers. For parallel labeling experiments the optimal isotopic tracers were [1,6-13C]glucose and [1,2-13C]glucose. Combined analysis of [1,6-13C]glucose and [1,2-13C]glucose labeling data improved the flux precision score by nearly 20-fold compared to widely use tracer mixture 80% [1-13C]glucose +20% [U-13C]glucose.

SUBMITTER: Crown SB

PROVIDER: S-EPMC5891732 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Optimal tracers for parallel labeling experiments and 13C metabolic flux analysis: A new precision and synergy scoring system.

Crown Scott B SB Long Christopher P CP Antoniewicz Maciek R MR

Metabolic engineering 20160604

13C-Metabolic flux analysis (13C-MFA) is a widely used approach in metabolic engineering for quantifying intracellular metabolic fluxes. The precision of fluxes determined by 13C-MFA depends largely on the choice of isotopic tracers and the specific set of labeling measurements. A recent advance in the field is the use of parallel labeling experiments for improved flux precision and accuracy. However, as of today, no systemic methods exist for identifying optimal ...[more]

PMID: 27267409

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Project description:BackgroundMetabolic fluxes provide invaluable insight on the integrated response of a cell to environmental stimuli or genetic modifications. Current computational methods for estimating the metabolic fluxes from 13C isotopomer measurement data rely either on manual derivation of analytic equations constraining the fluxes or on the numerical solution of a highly nonlinear system of isotopomer balance equations. In the first approach, analytic equations have to be tediously derived for each organism, substrate or labelling pattern, while in the second approach, the global nature of an optimum solution is difficult to prove and comprehensive measurements of external fluxes to augment the 13C isotopomer data are typically needed.ResultsWe present a novel analytic framework for estimating metabolic flux ratios in the cell from 13C isotopomer measurement data. In the presented framework, equation systems constraining the fluxes are derived automatically from the model of the metabolism of an organism. The framework is designed to be applicable with all metabolic network topologies, 13C isotopomer measurement techniques, substrates and substrate labelling patterns. By analyzing nuclear magnetic resonance (NMR) and mass spectrometry (MS) measurement data obtained from the experiments on glucose with the model micro-organisms Bacillus subtilis and Saccharomyces cerevisiae we show that our framework is able to automatically produce the flux ratios discovered so far by the domain experts with tedious manual analysis. Furthermore, we show by in silico calculability analysis that our framework can rapidly produce flux ratio equations--as well as predict when the flux ratios are unobtainable by linear means--also for substrates not related to glucose.ConclusionThe core of 13C metabolic flux analysis framework introduced in this article constitutes of flow and independence analysis of metabolic fragments and techniques for manipulating isotopomer measurements with vector space techniques. These methods facilitate efficient, analytic computation of the ratios between the fluxes of pathways that converge to a common junction metabolite. The framework can been seen as a generalization and formalization of existing tradition for computing metabolic flux ratios where equations constraining flux ratios are manually derived, usually without explicitly showing the formal proofs of the validity of the equations.

Project description:Background¹³C-Metabolic flux analysis (¹³C-MFA) is a standard technique to probe cellular metabolism and elucidate in vivo metabolic fluxes. 13C-Tracer selection is an important step in conducting ¹³C-MFA, however, current methods are restricted to trial-and-error approaches, which commonly focus on an arbitrary subset of the tracer design space. To systematically probe the complete tracer design space, especially for complex systems such as mammalian cells, there is a pressing need for new rational approaches to identify optimal tracers.ResultsRecently, we introduced a new framework for optimal ¹³C-tracer design based on elementary metabolite units (EMU) decomposition, in which a measured metabolite is decomposed into a linear combination of so-called EMU basis vectors. In this contribution, we applied the EMU method to a realistic network model of mammalian metabolism with lactate as the measured metabolite. The method was used to select optimal tracers for two free fluxes in the system, the oxidative pentose phosphate pathway (oxPPP) flux and anaplerosis by pyruvate carboxylase (PC). Our approach was based on sensitivity analysis of EMU basis vector coefficients with respect to free fluxes. Through efficient grouping of coefficient sensitivities, simple tracer selection rules were derived for high-resolution quantification of the fluxes in the mammalian network model. The approach resulted in a significant reduction of the number of possible tracers and the feasible tracers were evaluated using numerical simulations. Two optimal, novel tracers were identified that have not been previously considered for ¹³C-MFA of mammalian cells, specifically [2,3,4,5,6-¹³C]glucose for elucidating oxPPP flux and [3,4-¹³C]glucose for elucidating PC flux. We demonstrate that ¹³C-glutamine tracers perform poorly in this system in comparison to the optimal glucose tracers.ConclusionsIn this work, we have demonstrated that optimal tracer design does not need to be a pure simulation-based trial-and-error process; rather, rational insights into tracer design can be gained through the application of the EMU basis vector methodology. Using this approach, rational labeling rules can be established a priori to guide the selection of optimal ¹³C-tracers for high-resolution flux elucidation in complex metabolic network models.

Dataset Information

Optimal tracers for parallel labeling experiments and 13C metabolic flux analysis: A new precision and synergy scoring system.

Publications

Optimal tracers for parallel labeling experiments and <sup>13</sup>C metabolic flux analysis: A new precision and synergy scoring system.

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