Unknown

Dataset Information

0

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


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.

SUBMITTER: Ahn E 

PROVIDER: S-EPMC5731346 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

altmetric image

Publications

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

Ahn Eunyong E   Kumar Praveen P   Mukha Dzmitry D   Tzur Amit A   Shlomi Tomer T  

Molecular systems biology 20171106 11


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 metab  ...[more]

Similar Datasets

| S-EPMC4635072 | biostudies-literature
| S-EPMC6586781 | biostudies-literature
| S-EPMC5129964 | biostudies-literature
| S-EPMC6440987 | biostudies-literature
| S-EPMC7959529 | biostudies-literature
| S-EPMC7852548 | biostudies-literature
| S-EPMC4842982 | biostudies-literature
| S-EPMC4191801 | biostudies-literature
| S-EPMC4730144 | biostudies-literature
| S-EPMC2799800 | biostudies-literature