Application of Metabolic Flux Analysis to Plants

Dieuaide-Noubhani, Martine; Alonso, Ana Paula

doi:10.1007/978-1-62703-688-7_1

Cited by 13 publications

(10 citation statements)

References 101 publications

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“…The metabolomics study hence revealed the occurrence of key pathways involved in oil production in pennycress embryos. However, the relative contribution of each of these pathways to the synthesis of fatty acids (in terms of carbon skeletons, energy, and reductant), and the potential bottlenecks can only be determined by measuring the in vivo metabolic fluxes ( Alonso et al , 2010 a ; Dieuaide-Noubhani and Alonso, 2014 ).…”

Section: Discussionmentioning

confidence: 99%

Metabolite fingerprinting of pennycress (Thlaspi arvenseL.) embryos to assess active pathways during oil synthesis

Tsogtbaatar

Cocuron

Sonera

et al. 2015

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Section: Discussionmentioning

confidence: 99%

Metabolite fingerprinting of pennycress (Thlaspi arvenseL.) embryos to assess active pathways during oil synthesis

Tsogtbaatar

Cocuron

Sonera

et al. 2015

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“…These methods were initially developed to study steady state metabolism in other systems [e.g., [178][179][180][181][182][183]] but extensive resources for application in plants have now been described in books [184][185][186] and special issues of Phytochemistry [187] and the Journal of Experimental Botany edited by Kruger and Ratcliffe [188]. In each case the isotopic labeling experiment is assessed with a computer model that describes mass-balanced atom transitions of the biochemical reactions [43,[189][190][191].…”

Section: Computational Metabolic Flux Analysis Using Stable Isotopesmentioning

confidence: 99%

Tracking the metabolic pulse of plant lipid production with isotopic labeling and flux analyses: Past, present and future

Allen

Bates

Tjellström

2015

Progress in Lipid Research

100

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Metabolism is comprised of networks of chemical transformations, organized into integrated biochemical pathways that are the basis of cellular operation, and function to sustain life. Metabolism, and thus life, is not static. The rate of metabolites transitioning through biochemical pathways (i.e., flux) determines cellular phenotypes, and is constantly changing in response to genetic or environmental perturbations. Each change evokes a response in metabolic pathway flow, and the quantification of fluxes under varied conditions helps to elucidate major and minor routes, and regulatory aspects of metabolism. To measure fluxes requires experimental methods that assess the movements and transformations of metabolites without creating artifacts. Isotopic labeling fills this role and is a long-standing experimental approach to identify pathways and quantify their metabolic relevance in different tissues or under different conditions. The application of labeling techniques to plant science is however far from reaching it potential. In light of advances in genetics and molecular biology that provide a means to alter metabolism, and given recent improvements in instrumentation, computational tools and available isotopes, the use of isotopic labeling to probe metabolism is becoming more and more powerful. We review the principal analytical methods for isotopic labeling with a focus on seminal studies of pathways and fluxes in lipid metabolism and carbon partitioning through central metabolism. Central carbon metabolic steps are directly linked to lipid production by serving to generate the precursors for fatty acid biosynthesis and lipid assembly. Additionally some of the ideas for labeling techniques that may be most applicable for lipid metabolism in the future were originally developed to investigate other aspects of central metabolism. We conclude by describing recent advances that will play an important future role in quantifying flux and metabolic operation in plant tissues.

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“…Though 13 C-MFA can determine intracellular fluxes, extensive instrumentation and specialized expertise is required to carry out the experiment and the number of fluxes that can be accurately and uniquely determined is limited [ 7 ]. Moreover, in some situations, such as autotrophic growth, 13 C-MFA is not capable of intracellular flux determination, and isotopically nonstationary MFA (INST-MFA), an even more challenging method both experimentally and computationally, is required [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

E-Flux2 and SPOT: Validated Methods for Inferring Intracellular Metabolic Flux Distributions from Transcriptomic Data

et al. 2016

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BackgroundSeveral methods have been developed to predict system-wide and condition-specific intracellular metabolic fluxes by integrating transcriptomic data with genome-scale metabolic models. While powerful in many settings, existing methods have several shortcomings, and it is unclear which method has the best accuracy in general because of limited validation against experimentally measured intracellular fluxes.ResultsWe present a general optimization strategy for inferring intracellular metabolic flux distributions from transcriptomic data coupled with genome-scale metabolic reconstructions. It consists of two different template models called DC (determined carbon source model) and AC (all possible carbon sources model) and two different new methods called E-Flux2 (E-Flux method combined with minimization of l2 norm) and SPOT (Simplified Pearson cOrrelation with Transcriptomic data), which can be chosen and combined depending on the availability of knowledge on carbon source or objective function. This enables us to simulate a broad range of experimental conditions. We examined E. coli and S. cerevisiae as representative prokaryotic and eukaryotic microorganisms respectively. The predictive accuracy of our algorithm was validated by calculating the uncentered Pearson correlation between predicted fluxes and measured fluxes. To this end, we compiled 20 experimental conditions (11 in E. coli and 9 in S. cerevisiae), of transcriptome measurements coupled with corresponding central carbon metabolism intracellular flux measurements determined by 13C metabolic flux analysis (13C-MFA), which is the largest dataset assembled to date for the purpose of validating inference methods for predicting intracellular fluxes. In both organisms, our method achieves an average correlation coefficient ranging from 0.59 to 0.87, outperforming a representative sample of competing methods. Easy-to-use implementations of E-Flux2 and SPOT are available as part of the open-source package MOST (http://most.ccib.rutgers.edu/).ConclusionOur method represents a significant advance over existing methods for inferring intracellular metabolic flux from transcriptomic data. It not only achieves higher accuracy, but it also combines into a single method a number of other desirable characteristics including applicability to a wide range of experimental conditions, production of a unique solution, fast running time, and the availability of a user-friendly implementation.

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Application of Metabolic Flux Analysis to Plants

Cited by 13 publications

References 101 publications

Metabolite fingerprinting of pennycress (Thlaspi arvenseL.) embryos to assess active pathways during oil synthesis

Metabolite fingerprinting of pennycress (Thlaspi arvenseL.) embryos to assess active pathways during oil synthesis

Tracking the metabolic pulse of plant lipid production with isotopic labeling and flux analyses: Past, present and future

E-Flux2 and SPOT: Validated Methods for Inferring Intracellular Metabolic Flux Distributions from Transcriptomic Data

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