Eukaryotic metabolism: Measuring compartment fluxes

Wahrheit, Judith; Nicolae, Averina; Heinzle, Elmar

doi:10.1002/biot.201100032

Cited by 50 publications

(45 citation statements)

References 141 publications

(127 reference statements)

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“…This adds a layer of complexity to understanding metabolism. Only the average labeling pattern and metabolite levels from all compartments within a cell can be measured using most current techniques (Figure 1d) [57,58]. …”

Section: Cellular Compartmentsmentioning

confidence: 99%

A roadmap for interpreting 13 C metabolite labeling patterns from cells

Buescher¹,

Antoniewicz²,

Boros³

et al. 2015

Current Opinion in Biotechnology

556

545

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Measuring intracellular metabolism has increasingly led to important insights in biomedical research. 13C tracer analysis, although less information-rich than quantitative 13C flux analysis that requires computational data integration, has been established as a time-efficient method to unravel relative pathway activities, qualitative changes in pathway contributions, and nutrient contributions. Here, we review selected key issues in interpreting 13C metabolite labeling patterns, with the goal of drawing accurate conclusions from steady state and dynamic stable isotopic tracer experiments.

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Section: Cellular Compartmentsmentioning

confidence: 99%

A roadmap for interpreting 13 C metabolite labeling patterns from cells

Buescher¹,

Antoniewicz²,

Boros³

et al. 2015

Current Opinion in Biotechnology

556

545

View full text Add to dashboard Cite

show abstract

“…10). These intricate network descriptions are a well-recognized challenge [6,[303][304][305][306][307][308] (Fig. 11) and the implications for modeling in multiple locations have been described [222] including labeling experiments to maximize information content from the labeling experiment [75].…”

Section: Addressing the Challenges Of Multicellular Eukaryotic Metabomentioning

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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“…Small fluxes through Thr aldolase and the glyoxylate cycle were supported by labeling data but did not change with the C:N conditions. Labeling in other parts of the metabolism was inspected for further evidence of compartmentation (Wahrheit et al, 2011;Zamboni, 2011). However, additional subcellular details, such as vacuolar pools or the duplication of cytosolic and plastidic components of glycolysis or pentose phosphate pathways, were not necessary to develop models that passed statistical criteria (sum of squared residuals [SSR] of 221, 228, and 209 for 13:1, 21:1, and 37:1 that are less than the upper 95% confidence cutoff of 298) and, therefore, were not included.…”

Section: Generation Of Flux Maps For Three C:n Ratios From Multiple Lmentioning

confidence: 99%

Carbon and Nitrogen Provisions Alter the Metabolic Flux in Developing Soybean Embryos

Allen

Young

2013

Plant Physiology

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Soybean (Glycine max) seeds store significant amounts of their biomass as protein, levels of which reflect the carbon and nitrogen received by the developing embryo. The relationship between carbon and nitrogen supply during filling and seed composition was examined through a series of embryo-culturing experiments. Three distinct ratios of carbon to nitrogen supply were further explored through metabolic flux analysis. Labeling experiments utilizing [U- ]asparagine were used to monitor differences in carbon allocation. The analyses revealed that: (1) protein concentration as a percentage of total soybean embryo biomass coincided with the carbon-to-nitrogen ratio; (2) altered nitrogen supply did not dramatically impact relative amino acid or storage protein subunit profiles; and (3) glutamine supply contributed 10% to 23% of the carbon for biomass production, including 9% to 19% of carbon to fatty acid biosynthesis and 32% to 46% of carbon to amino acids. Seed metabolism accommodated different levels of protein biosynthesis while maintaining a consistent rate of dry weight accumulation. Flux through ATP-citrate lyase, combined with malic enzyme activity, contributed significantly to acetyl-coenzyme A production. These fluxes changed with plastidic pyruvate kinase to maintain a supply of pyruvate for amino and fatty acids. The flux maps were independently validated by nitrogen balancing and highlight the robustness of primary metabolism.

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Eukaryotic metabolism: Measuring compartment fluxes

Cited by 50 publications

References 141 publications

A roadmap for interpreting 13 C metabolite labeling patterns from cells

A roadmap for interpreting 13 C metabolite labeling patterns from cells

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

Carbon and Nitrogen Provisions Alter the Metabolic Flux in Developing Soybean Embryos

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