Reverse engineering the cancer metabolic network using flux analysis to understand drivers of human disease

Abstract

Metabolic dysfunction has reemerged as an essential hallmark of tumorigenesis, and metabolic phenotypes are increasingly being integrated into pre-clinical models of disease. The complexity of these metabolic networks requires systems-level interrogation, and metabolic flux analysis (MFA) with stable isotope tracing present a suitable conceptual framework for such systems. Here we review efforts to elucidate mechanisms through which metabolism influences tumor growth and survival, with an emphasis on applications using stable isotope tracing and MFA. Through these approaches researchers can now quantify pathway fluxes in various in vitro and in vivo contexts to provide mechanistic insights at molecular and physiological scales respectively. Knowledge and discoveries in cancer models are paving the way toward applications in other biological contexts and disease models. In turn, MFA approaches will increasingly help to uncover new therapeutic opportunities that enhance human health.

Keywords: Cancer; Metabolic flux analysis; Metabolism; Metabolomics; Mitochondria; Stable isotope tracing.

Figure 1. MFA applied to biological systems at different scales comes with a tradeoff in molecular resolution versus physiologic relevance

Use of metabolic flux analysis is technically feasible in many systems, but measurements in more physiologically complex systems come at a cost of molecular resolution. Integration of in vivo and in vitro MFA results will be important in the future as more therapeutic targets in metabolic pathways are identified.

Figure 2. Stable isotope tracing paradigm

Isotopologue or mass isotopomer distributions (MIDs) are the central measurement in metabolic flux analysis. Stable isotope variants (i.e. ¹³C, ¹⁵N, ²H) of carbohydrates, fatty acids, or amino acids are introduced into a biological system of interest. The labeled atoms of interest propagate throughout the metabolic network, and the biological matrix is sampled as needed. Mass spectrometry is used to measure isotope enrichment within individual metabolite pools to determine MIDs for all compounds of interest.

Figure 3. Tracing TCA metabolism using ¹³C glucose and glutamine

In this example, labeling on citrate and other intermediates from fully labeled [U-¹³C₆]glucose changes depending on routes used for anaplerosis and AcCoA generation. Oxidation of glucose-derived pyruvate by PDH results in M+2 citrate. Carboxylation through PC results in M+3 or M+5 citrate. [U-¹³C₅]glutamine oxidation or reduction results in M+4 and M+5 citrate, respectively. Taken together, relative flux changes in well-connected nodes (e.g. TCA cycles) result in measureable differences in labeling. Open circles depict ¹²C carbon atoms, filled circles depict ¹³C carbon atoms.

Figure 4. Metabolic pathways dysregulated in the context of disease

Glycolysis and the pentose phosphate pathway are fueled by glucose and generate biosynthetic intermediates, reducing equivalents, and ATP. Mitochondria are fueled by pyruvate, amino acids, and lipids, performing both anabolic and catabolic metabolism to generate energy. Serine, glycine, and folate-mediated one carbon metabolism are active in both cytosol and mitochondrial compartments. These pathways are connected orthogonally via cofactors and other disease- or tissue-specific pathways; as such, pathways beyond central carbon metabolism must be investigated in specific biological contexts.