Temporal fluxomics reveals oscillations in TCA cycle flux throughout the mammalian cell cycle.

Eunyong Ahn,Dzmitry Mukha,Praveen Kumar,Amit Tzur,Tomer Shlomi

doi:10.15252/msb.20177763

Eunyong Ahn, Dzmitry Mukha + Show 3 more

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https://doi.org/10.15252/msb.20177763

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Abstract

Cellular metabolic demands change throughout the cell cycle. Nevertheless, a characterization of how metabolic fluxes adapt to the changing demands throughout the cell cycle is lacking. Here, we developed a temporal‐fluxomics approach to derive a comprehensive and quantitative view of alterations in metabolic fluxes throughout the mammalian cell cycle. This is achieved by combining pulse‐chase LC‐MS‐based isotope tracing in synchronized cell populations with computational deconvolution and metabolic flux modeling. We find that TCA cycle fluxes are rewired as cells progress through the cell cycle with complementary oscillations of glucose versus glutamine‐derived fluxes: Oxidation of glucose‐derived flux peaks in late G1 phase, while oxidative and reductive glutamine metabolism dominates S phase. These complementary flux oscillations maintain a constant production rate of reducing equivalents and oxidative phosphorylation flux throughout the cell cycle. The shift from glucose to glutamine oxidation in S phase plays an important role in cell cycle progression and cell proliferation.

Highlights

Cell cycle progression is tightly interlinked with cellular metabolism (Kaplon et al, 2015)
The parameters of the model were estimated by fitting a simulation of how the synchronized cell population progresses through the different phases of the cell cycle with corresponding FACS measurements, finding that cell–cell variability in the rate of cell cycle progression through the cell cycle is 11% (Fig 1B, Materials and Methods)
Addressing these challenges, we tracked synchronized cells for three complete cell cycles, performed LC-MS-based metabolomics and pulse-chase isotope tracing in the synchronized cells, and employed computational deconvolution techniques to reliably detect oscillations in metabolite concentrations and isotopic labeling dynamics

Summary

Introduction

Cell cycle progression is tightly interlinked with cellular metabolism (Kaplon et al, 2015). The availability of sufficient metabolic nutrients and intracellular energy status controls the ability of cells to enter and progress through cell cycle. Cellular energy status (ATP/ AMP ratio) was found to regulate canonical cell cycle signaling pathways via AMP-activated protein kinase (AMPK; Banko et al, 2011). The mammalian target of rapamycin (mTOR) plays a central role in regulating cell cycle progression and growth, integrating stimuli of amino acid, energy, and oxygen availability (Fingar & Blenis, 2004; Cuyas et al, 2014). Several metabolic enzymes were shown to directly regulate the cell cycle machinery, including PFKFB3 and PKM2, controlling the activity of cyclins and cyclin-dependent kinase (CDK) inhibitors in the nucleus (Yalcin et al, 2009; Yang et al, 2011, 2012)

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