How to measure metabolic fluxes: a taxonomic guide for 13C fluxomics

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Highlights

  • Tracer experiments, analytics and modeling constitute the 13C fluxomics triumvirate.

  • 13C fluxomics tools differ in complexity, information power and universality.

  • The biological question prescribes the choice of the modeling approach.

  • In turn, modeling poses unique challenges for experimental and analytical setups.

  • Standards will raise throughput and flexibility of 13C fluxomics.

Metabolic reaction rates (fluxes) contribute fundamentally to our understanding of metabolic phenotypes and mechanisms of cellular regulation. Stable isotope-based fluxomics integrates experimental data with biochemical networks and mathematical modeling to ‘measure’ the in vivo fluxes within an organism that are not directly observable. In recent years, 13C fluxomics has evolved into a technology with great experimental, analytical, and mathematical diversity. This review aims at establishing a unified taxonomy by means of which the various fluxomics methods can be compared to each other. By linking the developed modeling approaches to recent studies, their challenges and opportunities are put into perspective. The proposed classification serves as a guide for scientific ‘travelers’ who are striving to resolve research questions with the currently available 13C fluxomics toolset.

Section snippets

The world of metabolic fluxes

Metabolism is defined by the interaction of metabolites and enzymes. Metabolic fluxes provide a quantitative readout of cellular function and, thus, contribute to the understanding of cell growth and maintenance as well as the cell's responses to environmental changes [1]. Here, the term ‘metabolic flux’ is used as a synonym for all reaction and transport rates in living cells (the fluxome). The end-product of a fluxome study, the flux map, reveals how metabolism actually works in the cells’

Ingredients of 13C fluxomics

Isotopic tracers (2H, 13C, 14C, 15N and others) have been used since the 1930s to reveal metabolically active pathways as well as enzyme mechanisms in all types of organisms [6, 7, 8, 9, 10]. In the ‘omics’ age, the neologism ‘13C fluxomics’ was coint. It is defined by the detailed quantification  rather than topological elucidation  of intracellular metabolic flux rates with 13C isotope labeling experiments (ILEs) [11, 12]. Meanwhile, 13C fluxomics, which also trades under the name 13C metabolic

Tools of the trade: 13C fluxomics modeling frameworks

To calculate fluxes, several computational techniques are available that differ significantly in modeling complexity as well as the required data input and output (Figure 2). We propose a taxonomic guideline based on the presence of specific essential ingredients for a modeling framework. If an ingredient is missing, choosing a simpler framework makes modeling easier, but less biological detail can be described and analyzed. We start with the general and most advanced metabolic dynamic setup,

Fluxes at different levels of biological organization and complexity

Traditional fluxomics investigations dealt with highly controlled and standardized experimental scenarios, usually a single microorganism cultivated in a bioreactor. In fact, many biological systems involve much less controlled real-life environments. Complexity rises with biological organization levels from single cells to tissues and organs, and whole organisms. Modeling these systems is further complicated by the presence of physicochemical gradients and interactions among multiple cell

Concluding remarks

The absolute number and throughput of 13C fluxomics is still small compared to other ‘omics’ technologies (e.g., metabolomics > 1000samples/d [71]). New lab automation techniques enable flux measurements of central carbon metabolism in high throughput (20 studies/d), at least for single bacterial and yeast model applications [72]. The clue to increased throughput and resolution is standardized experimental and computational workflows that are transferable and robust. To date, the main stumbling

Acknowledgement

S.N. acknowledges the BMBF for his post-doctoral fellowship grant within the e:ToP initiative DynaMeTox (#031A271B).

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