Abstract A14: 13C metabolomic and fluxomic study of human B-cell lymphoma metabolic network: Quantifying glutaminolysis and reductive carboxylation
Authors: Alexander A. Shestov, Lili Guo, Seung-Cheol Lee, David S. Nelson, Ian A. Blair, Mariusz A. Wasik, Jerry D. Glickson
Journal: Cancer Research (2016)

Abstract Intracellular 13C LC-MS and MRS isotopomer data coupled with metabolic modeling allows quantitative determination of metabolic fluxes and other metabolic parameters in vivo/in situ (1). Utilizing these methods, we have determined how an mTOR inhibitor, rapamycin, alters glycolysis, the TCA cycle, the pentose phosphate pathway (PPP) and glutamine metabolism in B-cell lymphoma cell lines. This analysis has revealed previously unknown pharmacological activities for rapamycin. DLCL2 (germinal-center type of diffuse large B-cell lymphoma) and Ramos (Burkitt’s lymphoma resembling activated ABC type of B-cells) cells were incubated in glucose and glutamine free DMEM containing 10% fetal bovine serum, 5 mM unlabeled or 13C-labelled glucose, 2 mM unlabeled or [U-13C5, 15N2] glutamine and rapamycin for 6 h. The steady-state labeling patterns of TCA cycle metabolites (LC-MS) and temporal profiles for labeled lactate and glutamate (NMR) were monitored. Data were analyzed with compartmentalized metabolic network models that included glucose, lactate and glutamine transport, glycolysis, TCA cycle, PPP, α-ketoglutarate-glutamate and oxaloacetate-aspartate exchange, pyruvate carboxylase activity, anaplerosis at the succinyl-CoA level, pyruvate recycling through malic enzyme, lactate dehydrogenase activity, glutaminolysis, reductive carboxylation and de novo fatty acids synthesis. Isotope balance equations were derived for every network metabolite. The resulted sets of differential equations were solved to determine flux through various metabolic pathways by isotopomer/cumomer analysis (2). Statistics for the obtained parameters/fluxes was calculated by Monte Carlo simulations including experimental noise levels. We analyzed changes in expression of ~10,000 metabolic genes in DLCL2 and Ramos cells induced by rapamycin (by RNA-seq). Anticipated related alterations indicate that mTOR inhibition diminishes glycolysis, TCA cycle activity, de novo fatty acid biosynthesis, pyruvate cycling and PPP. Glutaminolisys was also affected due to gene PPAT expression (responsible for enzymatic glutamate production from glutamine). Metabolic Flux Analysis revealed that in DLCL2 rapamycin produced dramatic metabolic changes in reductive carboxylation (41% decrease), glutaminolysis (95% decrease), mitochondrial pyruvate carrier (50% increase) and pyruvate carboxylase (520% increase) activities. Further study of these effects is required, yet these data strongly suggest that multiple pathways are affected and, potentially, these metabolic signatures may serve as key biomarkers of effective mTOR inhibition. Our genomic and proteomic analysis indicates that the expression level of a large number of enzymes in human lymphoma cells has been modified by rapamycin-mediated mTOR inhibition. These affected enzymes are involved in eight metabolic pathways: glycolysis, pyruvate cycling/metabolism, TCA cycle, glutaminolysis, fatty acid metabolism, cholesterol metabolism, phospholipid metabolism and the pentose shunt. Information available from 13C multiplets in 13C spectra and MID of numerous metabolites significantly increases precision of calculated metabolic fluxes and allows estimation of contributions of different pathways to particular metabolite labeling patterns. The new modeling approaches developed here provide more effecient methods for investigation of detailed mechanisms of cancer metabolism and therefore enable effective identification of targets for combinatorial therapy. Dynamic high-resolution MR spectra and LC-MS mass-isotopomers are very sensitive to changing/adding biochemical pathways and flux values, and metabolic modeling allows one to check precisely the feasibility of assumed general bionetworks and alterations of particular metabolic pathways. References: 1. DeBerardinis RJ, Mancuso A. et al., PNAS 2007, 104, 19345. 2. Shestov AA, Valette J. et al, Neurochem Res 2012,37, 2388. Acknowledgements: This work was supported by NIH grants 5R01CA129544-02 and R01-CA172820 Note: This abstract was not presented at the conference. Citation Format: Alexander A. Shestov, Lili Guo, Seung-Cheol Lee, David S. Nelson, Ian A. Blair, Mariusz A. Wasik, Jerry D. Glickson. 13C metabolomic and fluxomic study of human B-cell lymphoma metabolic network: Quantifying glutaminolysis and reductive carboxylation. [abstract]. In: Proceedings of the Fourth AACR International Conference on Frontiers in Basic Cancer Research; 2015 Oct 23-26; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2016;76(3 Suppl):Abstract nr A14.