Summary Cancer cells consume glucose and secrete lactate in culture. It is unknown whether lactate contributes to energy metabolism in living tumors. We previously reported that human non-small cell lung cancers (NSCLC) oxidize glucose in the tricarboxylic acid (TCA) cycle. Here we show that lactate is also a TCA cycle carbon source for NSCLC. In human NSCLC, evidence of lactate utilization was most apparent in tumors with high 18fluorodeoxyglucose uptake and aggressive oncological behavior. Infusing human NSCLC patients with 13C-lactate revealed extensive labeling of TCA cycle metabolites. In mice, deleting monocarboxylate transporter-1 (MCT1) from tumor cells eliminated lactate-dependent metabolite labeling, confirming tumor-cell autonomous lactate uptake. Strikingly, directly comparing lactate and glucose metabolism in vivo indicated that lactate's contribution to the TCA cycle predominates. The data indicate that tumors, including bona fide human NSCLC, can use lactate as a fuel in vivo.

Measuring intracellular metabolism has increasingly led to important insights in biomedical research. 13C tracer analysis, although less information-rich than quantitative 13C flux analysis that requires computational data integration, has been established as a time-efficient method to unravel relative pathway activities, qualitative changes in pathway contributions, and nutrient contributions. Here, we review selected key issues in interpreting 13C metabolite labeling patterns, with the goal of drawing accurate conclusions from steady state and dynamic stable isotopic tracer experiments.

Understanding in vivo regulation of photoautotrophic metabolism is important for identifying strategies to improve photosynthetic efficiency or re-route carbon fluxes to desirable end products. We have developed an approach to reconstruct comprehensive flux maps of photoautotrophic metabolism by computational analysis of dynamic isotope labeling measurements and have applied it to determine metabolic pathway fluxes in the cyanobacterium Synechocystis sp. PCC6803. Comparison to a theoretically predicted flux map revealed inefficiencies in photosynthesis due to oxidative pentose phosphate pathway and malic enzyme activity, despite negligible photorespiration. This approach has potential to fill important gaps in our understanding of how carbon and energy flows are systemically regulated in cyanobacteria, plants, and algae.

13C flux analysis studies have become an essential component of metabolic engineering research. The scope of these studies has gradually expanded to include both isotopically steady-state and transient labeling experiments, the latter of which are uniquely applicable to photosynthetic organisms and slow-to-label mammalian cell cultures. Isotopomer network compartmental analysis (INCA) is the first publicly available software package that can perform both steady-state metabolic flux analysis and isotopically non-stationary metabolic flux analysis. The software provides a framework for comprehensive analysis of metabolic networks using mass balances and elementary metabolite unit balances. The generation of balance equations and their computational solution is completely automated and can be performed on networks of arbitrary complexity.

Cell metabolism can vary considerably over the course of a typical fed-batch antibody production process. However, the intracellular pathway alterations associated with various phases of growth and antibody production have yet to be fully elucidated using industrially relevant production hosts. Therefore, we performed (13)C labeling experiments and metabolic flux analysis (MFA) to characterize CHO cell metabolism during four separate phases of a fed-batch culture designed to closely represent industrial process conditions. First, we found that peak specific growth rate was associated with high lactate production and minimal TCA cycling. Conversely, we found that lactate metabolism switched from net production to net consumption as the culture transitioned from peak growth to peak antibody production. During the peak antibody production phase, energy was primarily generated through oxidative phosphorylation, which was also associated with elevated oxidative pentose phosphate pathway (oxPPP) activity. Interestingly, as TCA cycling and antibody production reached their peaks, specific growth rate continued to diminish as the culture entered stationary phase. However, TCA cycling and oxPPP activity remained high even as viable cell density began to decline. Overall, we found that a highly oxidative state of metabolism corresponded with peak antibody production, whereas peak cell growth was characterized by a highly glycolytic metabolic state.