Metabolic flux analysis (MFA) is a powerful platform for studying biochemical reaction networks. Results are typically obtained by (1) introducing an isotopically labeled substrate into a cell culture at metabolic steady state, (2) allowing the system to reach isotopic steady state, (3) measuring relative labeling in metabolic intermediates and byproducts, and (4) computationally processing these measurements to estimate metabolic fluxes. An emerging area of interest is in the application of MFA to address fundamental physiological questions in higher organisms, especially plant and animal systems of specific agricultural or clinical significance. Because MFA has been traditionally applied to microbial systems, however, there are several features that limit its straightforward extension to higher organisms. In the case of animal studies, it is often difficult or impossible to maintain primary cells or whole animals in a fixed metabolic state long enough to achieve steady isotopic labeling. Plant systems growing under photoautotrophic conditions, on the other hand, only assimilate carbon from CO₂ and therefore produce a uniform steady-state ¹³C-labeling pattern in all metabolites, irrespective of flux distribution. In response to these and other drawbacks of the traditional MFA framework, we have developed an approach that involves repeated measurements of intracellular label enrichment during the transient period preceding isotopic steady state. Nonstationary Metabolic Flux Analysis (NMFA) is subsequently applied to leverage these measurements into quantitative flux estimates that characterize the metabolic phenotype. Recent advances by Antoniewicz et al. (2007) and Young et al. (2007) have made the NMFA computational problem tractable for systems of realistic size and complexity.

As an illustration of the NMFA approach, we report results from two experimental systems. First, NMFA has been applied to characterize time-dependent phenotypes in a rat hepatoma cell line (H4IIEC3) following exposure to varying levels of palmitate, oleate and amino acids. Preliminary studies have shown that elevated palmitate levels lead to apoptosis, while combined exposure to palmitate and oleate leads to excess lipid accumulation (steatosis) but does not trigger apoptosis. It is also possible to modulate the divergence between apoptotic and steatotic outcomes by varying the amino acid composition of the culture medium. Because the phenotypes are fully developed within 12-24 hours following fatty acid exposure, one cannot estimate fluxes during the important interval when the cells are transitioning from a normal to a diseased state using the traditional MFA approach. However, we have been able to examine this transition using NMFA and are beginning to uncover the mechanisms by which metabolic perturbations lead to phenotypic switching in this system. Second, we have applied NMFA to estimate fluxes in the prokaryotic cyanobacterium Synechocystis sp. PCC 6803 under photoautotrophic conditions. This represents the first time that photoautotrophic fluxes have been estimated using a ¹³C labeling experiment. We show that, even though the steady-state ¹³C distribution is insensitive to fluxes, transient measurements of isotope incorporation following a step change from unlabeled to labeled CO₂ can be used to estimate fluxes successfully.

References

Antoniewicz, M. R., Kelleher, J. K., Stephanopoulos, G., 2007. Elementary metabolite units (EMU): a novel framework for modeling isotopic distributions. Metab. Eng. 9, 68-86.

Young, J. D., Walther, J. L., Antoniewicz, M. R., Yoo, H., Stephanopoulos, G., 2007. An elementary metabolite unit (EMU) based method of isotopically nonstationary flux analysis. Biotechnol. Bioeng., accepted.