Accurate Assessment of Amino Acid Mass Isotopomer Distributions for Metabolic Flux Analysis

Antoniewicz, Maciek R.; Kelleher, Joanne K.; Stephanopoulos, Gregory

doi:10.1021/ac0708893

Cited by 238 publications

(258 citation statements)

References 27 publications

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“…. Measured intensities were corrected for baseline noise, and mass isotopomer distributions were obtained by integration (19). Mass isotopomer data are expressed as fractional abundances, i.e.…”

Section: Methodsmentioning

confidence: 99%

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

Yoo

Antoniewicz

Stephanopoulos

et al. 2008

Journal of Biological Chemistry

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284

211

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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 ␣-ketoglutarate to citrate. To quantify fluxes in these pathways we incubated brown adipocyte cells in [U-13 C]glutamine or [5-13 C]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 ␣-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.Glutamine is utilized at a high rate by rapidly growing cells, including almost all cultured cell lines, where it is required at super-physiological concentrations of 2-4 mM for optimal growth (1). Recently, we evaluated the role of glutamine as a substrate for lipogenesis in a transformed wild type (WT) 5 and IRS-1 knock-out brown adipose cell lines developed by Kahn and co-workers (2). Using isotopomer spectral analysis (ISA) we found that WT cells utilized glutamine for over 40% of their lipogenic acetyl-CoA (3). Glutamine was the largest precursor for lipogenic carbon, supplying more acetyl-CoA units than glucose or any other single source. This unexpected result led us to investigate glutamine metabolism in brown fat cell lines in more detail.The pathway for glutamine utilization in rapidly dividing cell is generally described as "glutaminolysis" where glutamine enters the citric acid cycle (CAC) as ␣-ketoglutarate traversing the cycle to oxaloacetate and exits as pyruvate or aspartate (1, 4). Pyruvate then could be converted to acetyl-CoA and citrate in the lipogenic pathway. A major argument for glutaminolysis is the need for a large supply of anaplerotic substrates for rapidly growing cells, which explains the high rate of glutamine utilization (5). An alternative to glutaminolysis is the reductive carboxylation path...

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Section: Methodsmentioning

confidence: 99%

“…The model included reversible reactions between ␣-ketoglutarate and citrate; malate and fumarate; malate and oxaloacetate; and glutamine, glutamate, and ␣-ketoglutarate. The model was implemented with a software tool, Metran, based on the concepts described elsewhere (19,20).…”

Section: The Contribution Of [U-13 C]glutamine To Palmitate Synthesismentioning

confidence: 99%

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

Yoo

Antoniewicz

Stephanopoulos

et al. 2008

Journal of Biological Chemistry

Self Cite

284

211

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“…Lipid, protein, carbohydrate, and ribose in RNA contents were measured experimentally under photoautotrophic and heterotrophic conditions following the method described previously (Antoniewicz et al, 2007(Antoniewicz et al, , 2011Long and Antoniewicz, 2014). C. vulgaris UTEX 395 was grown in a 250-mL bottle with 200 mL of Bold's basal medium (BBM) at 24°C (Guarnieri et al, 2013) and cycling of 14/10-h light/dark at10,000 lx and a gas flow of 1% CO 2 (12 mL min 21 ).…”

Section: Biomass Composition and Experimental Datamentioning

confidence: 99%

Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions

Zuñiga

Huelsman

et al. 2016

Plant Physiol.

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The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine.

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“…Using isotopically labeled substrate in a targeted approach is an important area of metabolomics called "fluxomics" (62). Mass spectroscopy-based (LC and GC-MS) fluxomics can track stable isotope-labeled elements, such as 2 H, 13 C, and 15 N, to reproducibly identify their compounds in various biological processes.…”

Section: Metabolomics As Predictive Biomarkersmentioning

confidence: 99%

Metabolomics: Applications and Promise in Mycobacterial Disease

Mirsaeidi

Banoei²,

Winston

et al. 2015

Annals ATS

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Until recently, the study of mycobacterial diseases was trapped in culture-based technology that is more than a century old. The use of nucleic acid amplification is changing this, and powerful new technologies are on the horizon. Metabolomics, which is the study of sets of metabolites of both the bacteria and host, is being used to clarify mechanisms of disease, and can identify changes leading to better diagnosis, treatment, and prognostication of mycobacterial diseases. Metabolomic profiles are arrays of biochemical products of genes in their environment. These complex patterns are biomarkers that can allow a more complete understanding of cell function, dysfunction, and perturbation than genomics or proteomics. Metabolomics could herald sweeping advances in personalized medicine and clinical trial design, but the challenges in metabolomics are also great. Measured metabolite concentrations vary with the timing within a condition, the intrinsic biology, the instruments, and the sample preparation. Metabolism profoundly changes with age, sex, variations in gut microbial flora, and lifestyle. Validation of biomarkers is complicated by measurement accuracy, selectivity, linearity, reproducibility, robustness, and limits of detection. The statistical challenges include analysis, interpretation, and description of the vast amount of data generated. Despite these drawbacks, metabolomics provides great opportunity and the potential to understand and manage mycobacterial diseases.

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Accurate Assessment of Amino Acid Mass Isotopomer Distributions for Metabolic Flux Analysis

Cited by 238 publications

References 27 publications

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

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

Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions

Metabolomics: Applications and Promise in Mycobacterial Disease

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