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Review
. 2020 Aug:64:92-100.
doi: 10.1016/j.copbio.2019.11.003. Epub 2019 Dec 20.

Tracing metabolic flux through time and space with isotope labeling experiments

Affiliations
Review

Tracing metabolic flux through time and space with isotope labeling experiments

Doug K Allen et al. Curr Opin Biotechnol. 2020 Aug.

Abstract

Metabolism is dynamic and must function in context-specific ways to adjust to changes in the surrounding cellular and ecological environment. When isotopic tracers are used, metabolite flow (i.e. metabolic flux) can be quantified through biochemical networks to assess metabolic pathway operation. The cellular activities considered across multiple tissues and organs result in the observed phenotype and can be analyzed to discover emergent, whole-system properties of biology and elucidate misconceptions about network operation. However, temporal and spatial challenges remain significant hurdles and require novel approaches and creative solutions. We survey current investigations in higher plant and animal systems focused on dynamic isotope labeling experiments, spatially resolved measurement strategies, and observations from re-analysis of our own studies that suggest prospects for future work. Related discoveries will be necessary to push the frontier of our understanding of metabolism to suggest novel solutions to cure disease and feed a growing future world population.

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Conflict of interest statement

Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1.
Fig. 1.. Hierarchical clustering analysis (HCA) of dynamic 13C labeling in cyanobacterial metaboltes.
Target ions were clustered based on APE (left; JD Young unpublished). Pathways and metabolites were color-coded in the network diagram according to the HCA outputs (right). Abbreviations can be found in [2].
Fig. 2.
Fig. 2.. Nominal & HRMS of lipids.
A) Example of phosphatidylcholine (PC) structure. B) Isotopologue distribution of fatty acids at nominal mass resolution. C) HRMS-derived isotopologue distribution.
Fig. 3.
Fig. 3.. Identification of isotopologue peaks.
Change from a molecular mass [M]+ to a mass of [M+2]+ can be the result of substitution of various isotopes (i.e., 15N for 14N, 13C for 12C, 18O for 16O, 17O for 16O, or 2H for 1H) or can result from the addition of hydrogens to the molecule as in the case of changing the degree of unsaturation in a lipid. Here the differences in mass from substitution of the indicated isotope are shown. The change in mass from addition of two hydrogens (+H2) is also presented.
Fig. 4.
Fig. 4.. Carbon isotopomers for Calvin-Benson Cycle intermediates.
Isotopic labeling for ribulose 1,5-bisphosphate (RUBP) and sedoheptulose 7-phosphate (S7P) were derived through in silico modeling (DK Allen unpublished) using the INCA framework [1] and assuming no photorespiration. Positional carbon enrichments are indicated following the color scheme of the circles that represent individual carbon atoms. S7P carbon atoms 2 and 7 (orange and dark blue) are identical, as are atoms 4 and 5 (yellow and light blue). Qualitative enrichment patterns are consistent with well-known pathway bond rearrangements [8,9] but can vary with flux descriptions. Dotted gray lines and question marks within the metabolic network describe one of several hypothetical versions of the G6P shunt [–12] which is not included in the metabolite simulations. Abbreviations are based on [14] and canonical textbook descriptions.

References

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