Maciek R. Antoniewicz

Quantifying Reductive Carboxylation Flux of Glutamine to Lipid in a Brown Adipocyte Cell Line

We previously reported that glutamine was a major source of carbon for de novo fatty acid synthesis in a brown adipocyte cell line. The pathway for fatty acid synthesis from glutamine may follow either of two distinct pathways after it enters the citric acid cycle. The glutaminolysis pathway follows the citric acid cycle, whereas the reductive carboxylation pathway travels in reverse of the citric acid cycle from α-ketoglutarate to citrate. To quantify fluxes in these pathways we incubated brown adipocyte cells in [U-13C]glutamine or [5-13C]glutamine and analyzed the mass isotopomer distribution of key metabolites using models that fit the isotopomer distribution to fluxes. We also investigated inhibitors of NADP-dependent isocitrate dehydrogenase and mitochondrial citrate export. The results indicated that one third of glutamine entering the citric acid cycle travels to citrate via reductive carboxylation while the remainder is oxidized through succinate. The reductive carboxylation flux accounted for 90% of all flux of glutamine to lipid. The inhibitor studies were compatible with reductive carboxylation flux through mitochondrial isocitrate dehydrogenase. Total cell citrate and α-ketoglutarate were near isotopic equilibrium as expected if rapid cycling exists between these compounds involving the mitochondrial membrane NAD/NADP transhydrogenase. Taken together, these studies demonstrate a new role for glutamine as a lipogenic precursor and propose an alternative to the glutaminolysis pathway where flux of glutamine to lipogenic acetyl-CoA occurs via reductive carboxylation. These findings were enabled by a new modeling tool and software implementation (Metran) for global flux estimation.

A roadmap for interpreting (13)C metabolite labeling patterns from cells.

Measuring intracellular metabolism has increasingly led to important insights in biomedical research. (13)C tracer analysis, although less information-rich than quantitative (13)C 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 (13)C metabolite labeling patterns, with the goal of drawing accurate conclusions from steady state and dynamic stable isotopic tracer experiments.

Metabolic flux analysis of CHO cells at growth and non-growth phases using isotopic tracers and mass spectrometry.

Chinese hamster ovary (CHO) cells are the main platform for production of biotherapeutics in the biopharmaceutical industry. However, relatively little is known about the metabolism of CHO cells in cell culture. In this work, metabolism of CHO cells was studied at the growth phase and early stationary phase using isotopic tracers and mass spectrometry. CHO cells were grown in fed-batch culture over a period of six days. On days 2 and 4, [1,2-(13)C] glucose was introduced and the labeling of intracellular metabolites was measured by gas chromatography-mass spectrometry (GC-MS) at 6, 12 and 24h following the introduction of tracer. Intracellular metabolic fluxes were quantified from measured extracellular rates and (13)C-labeling dynamics of intracellular metabolites using non-stationary (13)C-metabolic flux analysis ((13)C-MFA). The flux results revealed significant rewiring of intracellular metabolic fluxes in the transition from growth to non-growth, including changes in energy metabolism, redox metabolism, oxidative pentose phosphate pathway and anaplerosis. At the exponential phase, CHO cell metabolism was characterized by a high flux of glycolysis from glucose to lactate, anaplerosis from pyruvate to oxaloacetate and from glutamate to α-ketoglutarate, and cataplerosis though malic enzyme. At the stationary phase, the flux map was characterized by a reduced flux of glycolysis, net lactate uptake, oxidative pentose phosphate pathway flux, and reduced rate of anaplerosis. The fluxes of pyruvate dehydrogenase and TCA cycle were similar at the exponential and stationary phase. The results presented here provide a solid foundation for future studies of CHO cell metabolism for applications such as cell line development and medium optimization for high-titer production of recombinant proteins.

Linking high-resolution metabolic flux phenotypes and transcriptional regulation in yeast modulated by the global regulator Gcn4p

Genome sequencing dramatically increased our ability to understand cellular response to perturbation. Integrating system-wide measurements such as gene expression with networks of protein protein interactions and transcription factor binding revealed critical insights into cellular behavior. However, the potential of systems biology approaches is limited by difficulties in integrating metabolic measurements across the functional levels of the cell despite their being most closely linked to cellular phenotype. To address this limitation, we developed a model-based approach to correlate mRNA and metabolic flux data that combines information from both interaction network models and flux determination models. We started by quantifying 5,764 mRNAs, 54 metabolites, and 83 experimental (13)C-based reaction fluxes in continuous cultures of yeast under stress in the absence or presence of global regulator Gcn4p. Although mRNA expression alone did not directly predict metabolic response, this correlation improved through incorporating a network-based model of amino acid biosynthesis (from r = 0.07 to 0.80 for mRNA-flux agreement). The model provides evidence of general biological principles: rewiring of metabolic flux (i.e., use of different reaction pathways) by transcriptional regulation and metabolite interaction density (i.e., level of pairwise metabolite-protein interactions) as a key biosynthetic control determinant. Furthermore, this model predicted flux rewiring in studies of follow-on transcriptional regulators that were experimentally validated with additional (13)C-based flux measurements. As a first step in linking metabolic control and genetic regulatory networks, this model underscores the importance of integrating diverse data types in large-scale cellular models. We anticipate that an integrated approach focusing on metabolic measurements will facilitate construction of more realistic models of cellular regulation for understanding diseases and constructing strains for industrial applications.Genome sequencing dramatically increased our ability to understand cellular response to perturbation. Integrating system-wide measurements such as gene expression with networks of protein–protein interactions and transcription factor binding revealed critical insights into cellular behavior. However, the potential of systems biology approaches is limited by difficulties in integrating metabolic measurements across the functional levels of the cell despite their being most closely linked to cellular phenotype. To address this limitation, we developed a model-based approach to correlate mRNA and metabolic flux data that combines information from both interaction network models and flux determination models. We started by quantifying 5,764 mRNAs, 54 metabolites, and 83 experimental 13C-based reaction fluxes in continuous cultures of yeast under stress in the absence or presence of global regulator Gcn4p. Although mRNA expression alone did not directly predict metabolic response, this correlation improved through incorporating a network-based model of amino acid biosynthesis (from r = 0.07 to 0.80 for mRNA-flux agreement). The model provides evidence of general biological principles: rewiring of metabolic flux (i.e., use of different reaction pathways) by transcriptional regulation and metabolite interaction density (i.e., level of pairwise metabolite-protein interactions) as a key biosynthetic control determinant. Furthermore, this model predicted flux rewiring in studies of follow-on transcriptional regulators that were experimentally validated with additional 13C-based flux measurements. As a first step in linking metabolic control and genetic regulatory networks, this model underscores the importance of integrating diverse data types in large-scale cellular models. We anticipate that an integrated approach focusing on metabolic measurements will facilitate construction of more realistic models of cellular regulation for understanding diseases and constructing strains for industrial applications.

Parallel labeling experiments with [1,2-13C]glucose and [U-13C]glutamine provide new insights into CHO cell metabolism

We applied a parallel labeling strategy using two isotopic tracers, [1,2-(13)C]glucose and [U-(13)C]glutamine, to determine metabolic fluxes in Chinese hamster ovary (CHO) cells. CHO cells were grown in parallel cultures over a period of six days with glucose and glutamine feeding. On days 2 and 5, isotopic tracers were introduced and (13)C-labeling of intracellular metabolites was measured by gas chromatography-mass spectrometry (GC-MS). Metabolites in glycolysis pathway reached isotopic steady state for [1,2-(13)C]glucose within 1.5h, and metabolites in the TCA cycle reached isotopic steady state for [U-(13)C]glutamine within 3h. Combined analysis of multiple data sets produced detailed flux maps at two key metabolic phases, exponential growth phase (day 2) and early stationary phase (day 5). Flux results revealed significant rewiring of intracellular metabolism in the transition from growth to non-growth, including changes in oxidative pentose phosphate pathway, anaplerosis, amino acid metabolism, and fatty acid biosynthesis. At the growth phase, de novo fatty acid biosynthesis correlated well with the lipid requirements for cell growth. However, surprisingly, at the non-growth phase the fatty acid biosynthesis flux remained high even though no new lipids were needed for cell growth. Additionally, we identified a discrepancy in the estimated TCA cycle flux obtained using traditional stoichiometric flux balancing and (13)C-metabolic flux analysis. Our results suggested that CHO cells produced additional metabolites from glucose that were not captured in previous metabolic models. Follow-up experiments with [U-(13)C]glucose confirmed that additional metabolites were accumulating in the medium that became M+3 and M+6 labeled.

Towards dynamic metabolic flux analysis in CHO cell cultures

Chinese hamster ovary (CHO) cells are the most widely used mammalian cell line for biopharmaceutical production, with a total global market approaching

Resolving the TCA cycle and pentose-phosphate pathway of Clostridium acetobutylicum ATCC 824: Isotopomer analysis, in vitro activities and expression analysis

100 billion per year. In the pharmaceutical industry CHO cells are grown in fed‐batch culture, where cellular metabolism is characterized by high glucose and glutamine uptake rates combined with high rates of ammonium and lactate secretion. The metabolism of CHO cells changes dramatically during a fed‐batch culture as the cells adapt to a changing environment and transition from exponential growth phase to stationary phase. Thus far, it has been challenging to study metabolic flux dynamics in CHO cell cultures using conventional metabolic flux analysis techniques that were developed for systems at metabolic steady state. In this paper we review progress on flux analysis in CHO cells and techniques for dynamic metabolic flux analysis. Application of these new tools may allow identification of intracellular metabolic bottlenecks at specific stages in CHO cell cultures and eventually lead to novel strategies for improving CHO cell metabolism and optimizing biopharmaceutical process performance.

Methods and advances in metabolic flux analysis: a mini-review.

Solventogenic clostridia are an important class of microorganisms that can produce various biofuels. One of the bottlenecks in engineering clostridia stems from the fact that central metabolic pathways remain poorly understood. Here, we utilized the power of (13) C-based isotopomer analysis to re-examine central metabolic pathways of Clostridium acetobutylicum ATCC 824. We demonstrate using [1,2-(13) C]glucose, MS analysis of intracellular metabolites, and enzymatic assays that C. acetobutylicum has a split TCA cycle where only Re-citrate synthase (CS) contributes to the production of α-ketoglutarate via citrate. Furthermore, we show that there is no carbon exchange between α-ketoglutarate and fumarate and that the oxidative pentose-phosphate pathway (oxPPP) is inactive. Dynamic gene expression analysis of the putative Re-CS gene (CAC0970), its operon, and all glycolysis, pentose-phosphate pathway, and TCA cycle genes identify genes and their degree of involvement in these core pathways that support the powerful primary metabolism of this industrial organism.

Tandem mass spectrometry: A novel approach for metabolic flux analysis

Metabolic flux analysis (MFA) is one of the pillars of metabolic engineering. Over the past three decades, it has been widely used to quantify intracellular metabolic fluxes in both native (wild type) and engineered biological systems. Through MFA, changes in metabolic pathway fluxes are quantified that result from genetic and/or environmental interventions. This information, in turn, provides insights into the regulation of metabolic pathways and may suggest new targets for further metabolic engineering of the strains. In this mini-review, we discuss and classify the various methods of MFA that have been developed, which include stoichiometric MFA, 13C metabolic flux analysis, isotopic non-stationary 13C metabolic flux analysis, dynamic metabolic flux analysis, and 13C dynamic metabolic flux analysis. For each method, we discuss key advantages and limitations and conclude by highlighting important recent advances in flux analysis approaches.

Measuring deuterium enrichment of glucose hydrogen atoms by gas chromatography mass spectrometry

The goal of metabolic flux analysis (MFA) is the accurate estimation of intracellular fluxes in metabolic networks. Here, we introduce a new method for MFA based on tandem mass spectrometry (MS) and stable-isotope tracer experiments. We demonstrate that tandem MS provides more labeling information than can be obtained from traditional full scan MS analysis and allows estimation of fluxes with better precision. We present a modeling framework that takes full advantage of the additional labeling information obtained from tandem MS for MFA. We show that tandem MS data can be computed for any network model, any compound and any tandem MS fragmentation using linear mapping of isotopomers. The inherent advantages of tandem MS were illustrated in two network models using simulated and literature data. Application of tandem MS increased the observability of the models and improved the precision of estimated fluxes by 2- to 5-fold compared to traditional MS analysis.