(197g) Isotopically Nonstationary Proteinogenic MFA As a Tool for Analysis of Tumor Cell Metabolism

Understanding the metabolic consequences of specific genetic and therapeutic perturbations is of critical interest in cancer research. Metabolic flux analysis (MFA) is a tool that allows for the quantification of intracellular fluxes of many organisms and cell types. The application of MFA to understanding the metabolism of cancer has been increasing in recent years, but it still remains a challenging task. Traditional MFA requires isotopic steady state of either protein extracts or intracellular metabolites prior to extraction. This requirement, especially for protein extracts, makes its application difficult in certain situations: mainly with cells that have a low growth rate either due to normal physiology or through genetic or chemical inhibition. Isotopically nonstationary MFA (INST-MFA) can be used to overcome this requirement using measurements of labeling during the transient period prior to steady state. INST-MFA allows for shorter experiments to be conducted, and it returns similar, and sometimes better, precision of the estimated fluxes.

In our recent work, we show that traditional methods of achieving proteinogenic steady-state via multiple re-platings of an in vitro cell culture are invalid. Reintroduction of unlabeled material during each re-plating creates a false isotopic steady-state. Our INST-MFA method gives a better approximation of what the true isotopic steady-state labeling would be, and it generates additional time-course data that is used to better estimate the fluxes in our model. Experiments starting at low cell densities show that a higher level of labeling is achieved when cells are grown continuously in a single flask rather than when multiple re-platings are conducted. Our INST-MFA method requires samples to be acquired approximately every half doubling of the cell culture. Isotopic labeling is then measured using mass spectrometry. To simulate the intracellular fluxes, we use an in-house program written in Matlab called Isotopomer Network Compartmental Analysis (INCA). We compared, using a highly proliferative B-cell line, the traditional steady-state method to our transient method. We found that, using a simpler model of metabolism within INCA, the steady-state methodology gave erroneous flux estimates that we disproved using additional tracer studies and protein expression analysis. A more complex model of central metabolism, including amino acid catabolism, gave results that were similar in both instances. This provided a validation of the proteinogenic INST-MFA method. We then applied INST-MFA to compare the flux maps generated for a B-cell model of lymphoma under two different expression levels of the c-Myc oncoprotein.