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Cancer cells often respond to chemotherapeutic inhibitors by redirecting carbon flow in the central metabolism. To understand the metabolic redirections of inhibitor treatment on cancer cells, this study established a 13C-metabolic flux analysis (13C-MFA)-based method to evaluate metabolic redirection in MCF-7 breast cancer cells using mass spectrometry. A metabolic stationary state necessary for accurate 13C-MFA was confirmed during an 8–24 h window using low-dose treatments of various metabolic inhibitors. Further 13C-labeling experiments using [1-13C]glucose and [U-13C]glutamine, combined with gas chromatography-mass spectrometry (GC-MS) analysis of mass isotopomer distributions (MIDs), confirmed that an isotopic stationary state of intracellular metabolites was reached 24 h after treatment with paclitaxel (Taxol), an inhibitor of mitosis used for cancer treatment. Based on these metabolic and isotopic stationary states, metabolic flux distribution in the central metabolism of paclitaxel-treated MCF-7 cells was determined by 13C-MFA. Finally, estimations of the 95% confidence intervals showed that tricarboxylic acid cycle metabolic flux increased after paclitaxel treatment. Conversely, anaerobic glycolysis metabolic flux decreased, revealing metabolic redirections by paclitaxel inhibition. The gap between total regeneration and consumption of ATP in paclitaxel-treated cells was also found to be 1.2 times greater than controls, suggesting ATP demand was increased by paclitaxel treatment, likely due to increased microtubule polymerization. These data confirm that 13C-MFA can be used to investigate inhibitor-induced metabolic redirection in cancer cells. This will contribute to future pharmaceutical developments and understanding variable patient response to treatment.

The 13C-MFA experiments require an optimal design since the precision or confidence intervals of the estimated flux levels depends on factors such as the composition of 13C-labeled carbon sources, as well as the metabolic flux distribution of interest. In this study, useful compositions of 13C-labeled glucose for 13C-metabolic flux analysis (13C-MFA) of Escherichia coli are investigated using a computer simulation of the stable isotope labeling experiment. Following the generation of artificial mass spectra datasets of amino acid fragments using five literature-reported flux distributions of E. coli, the best fitted flux distribution and the 95% confidence interval were estimated by the 13C-MFA procedure. A comparison of the precision scores showed that [1, 2-13C]glucose and a mixture of [1-13C] and [U-13C]glucose at 8:2 are one of the best carbon sources for a precise estimation of flux levels of the pentose phosphate pathway, glycolysis and the TCA cycle. Although the precision scores of the anaplerotic and glyoxylate pathway reactions were affected by both the carbon source and flux distribution, it was also shown that the mixture of non-labeled, [1-13C], and [U-13C]glucose at 4:1:5 was specifically effective for the flux estimation of the glyoxylate pathway reaction. These findings were confirmed by wet 13C-MFA experiments.

Measurement of metabolic flux levels using stable isotope labeling has been successfully used to investigate metabolic redirection and reprogramming in living cells or tissues. The metabolic flux ratio between two reactions can be estimated from the 13 c-labeling patterns of a few metabolites combined with the knowledge of atom mapping in the complicated metabolic network. However, it remains unclear whether an observed change in the labeling pattern of the metabolites is sufficient evidence of a shift in flux ratio between two metabolic states. In this study, a data analysis method was developed for the quantitative assessment of metabolic reprogramming. the Metropolis-Hastings algorithm was used with an in silico metabolic model to generate a probability distribution of metabolic flux levels under a condition in which the 13 c-labeling pattern was observed. Reanalysis of literature data demonstrated that the developed method enables analysis of metabolic redirection using whole 13 c-labeling pattern data. Quantitative assessment by Cohen's effect size (d) enables a more detailed read-out of metabolic reprogramming information. the developed method will enable future applications of the metabolic isotopomer analysis to various targets, including cultured cells, whole tissues, and organs. Measurement of metabolic flux levels has been successfully used to investigate metabolic reprogramming or redirection in living cells or tissues of microbes, plants, and mammals 1-5. A series of flux analysis methodologies based on stable isotope labeling have been used for the comparison of metabolic flux distribution in distinct metabolic states 6-13. 13 C-metabolic flux analysis (13 C-MFA) is one of the most quantitative and sophisticated methods available to investigate flux distribution in the central carbon metabolism. There are several requirements for 13 C-MFA experiments (Requirement 1-5), as listed in Table 1 14-20. Recently, a more simplified method termed flux analysis or isotopomer analysis has been used for the mammalian cell analysis. The original methodology for the isotopomer-based analysis of local flux ratios was developed more than decades ago 21. It enables the qualitative survey of metabolic redirections that occur in cells, such as cancer and immune cells 22-28. A metabolic flux ratio between two pathways can be estimated from the 13 C-labeling patterns or the mass isotopomer distribution vector (MDV) of the specific metabolites measured by mass spectrometry 29,30. Isotopomer analysis has been often employed to investigate the reductive glutamine metabolism or the reverse reaction of isocitrate dehydrogenase (IDH reverse) in cancer cells 31-33 (Fig. 1a). The labeling patterns of citrate (Cit) have been measured in cells cultured in a medium containing [U-13 C]glutamine. This measurement is possible because the citrate molecule labeled with five 13 C atoms (Cit m5) is derived from IDH reverse or the reductive glutamine metabolism (Fig. 1a). On the other hand, the clockwise reaction of tricarboxylic acid (TC...

13 C-based metabolic flux analysis ( 13 C-MFA) is an essential tool for estimating intracellular metabolic flux levels in metabolic engineering and biology. In 13 C-MFA, a metabolic flux distribution that explains the observed isotope labeling data was computationally estimated using a non-linear optimization method. Herein, we report the development of mfapy, an open-source Python package developed for more flexibility and extensibility for 13 C-MFA. mfapy compels users to write a customized Python code by describing each step in the data analysis procedures of the isotope labeling experiments. The flexibility and extensibility provided by mfapy can support trial-and-error performance in the routine estimation of metabolic flux distributions, experimental design by computer simulations of 13 C-MFA experiments, and development of new data analysis techniques for stable isotope labeling experiments. mfapy is available to the public from the Github repository ( https://github.com/fumiomatsuda/mfapy ).