Comprehensive analysis of metabolic pathways through the combined use of multiple isotopic tracers

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2006.Includes bibliographical references (p. 287-294).Metabolic Flux Analysis (MFA) has emerged as a tool of great significance for metabolic engineering and the analysis of human metabolic diseases. An important limitation of MFA, as carried out via stable isotope labeling and GC/MS measurements, is the large number of isotopomer equations that need to be solved. This restriction reduces the ability of MFA to fully utilize the power of multiple isotopic tracers in elucidating the physiology of complex biological networks. Here, we present a novel framework for modeling isotopic distributions that significantly reduces the number of system variables without any loss of information. The elementary metabolite units (EMU) framework is based on a highly efficient decomposition algorithm identifies the minimum amount of information needed to simulate isotopic labeling within a reaction network using knowledge of atomic transitions occurring in the network reactions. The developed computational and experimental methodologies were applied to two biological systems of major industrial and medical significance. First, we describe the analysis of metabolic fluxes in E. coli in a fed-batch fermentation for overproduction of 1,3-propanediol (PDO).(cont.) A dynamic 13C-labeling experiment was performed and nonstationary intracellular fluxes (with confidence intervals) were determined by fitting labeling patterns of 191 cellular amino acids and 8 external fluxes to a detailed network model of E. coli. We established for the first time detailed time profiles of in vivo fluxes. Flux results confirmed the genotype of the organism and provided further insight into the physiology of PDO overproduction in E. coli. Second, we describe the analysis of metabolic fluxes in the pathway of gluconeogenesis in cultured primary hepatocytes, i.e. isolated liver cells. We applied multiple 13C and 2H-labeled tracers and measured isotopomer distributions of glucose fragments. From this overdetermined data set we estimated net and exchange fluxes in the gluconeogenesis pathway. We identified limitations in current methods to estimate gluconeogenesis in vivo, and developed a novel [U-13C,2Hs]glycerol method that allows accurate analysis of gluconeogenesis fluxes independent of the assumption of isotopic steady-state and zonation of tracers. The developed methodologies have wide implications for in vivo studies of glucose metabolism in Type II diabetes, and other metabolic diseases.by Maciek Robert Antoniewicz.Ph.D

Differential effects of insulin signaling on individual carbon fluxes for fatty acid synthesis in brown adipocytes

Considering the major role of insulin signaling on fatty acid synthesis via stimulation of lipogenic enzymes, differential effects of insulin signaling on individual carbon fluxes for fatty acid synthesis have been investigated by comparing the individual lipogenic fluxes in WT and IRS-1 knockout (IRS-1 KO) brown adipocytes. Results from experiments on WT and IRS-1 KO cells incubated with [5-¹³C] glutamine were consistent with the existence of reductive carboxylation pathway. Analysis of isotopomer distribution of nine metabolites related to the lipogenic routes from glucose and glutamine in IRS-1 KO cells using [U-¹³C] glutamine as compared to that in WT cells indicated that flux through reductive carboxylation pathway was diminished while flux through conventional TCA cycle was stimulated due to absence of insulin signaling in IRS-1 KO cells. This observation was confirmed by quantitative estimation of individual lipogenic fluxes in IRS-1 KO cells and their comparison with fluxes in WT cells. Thus, these results suggest that glutamine’s substantial contribution to fatty acid synthesis can be directly manipulated by controlling the flux through reductive carboxylation of alpha-ketoglutarate to citrate using hormone (insulin).Singapore-MIT Alliance (SMA

Tandem Mass Spectrometry for 13C Metabolic Flux Analysis: Methods and Algorithms Based on EMU Framework

In the past two decades, 13C metabolic flux analysis (13C-MFA) has matured into a powerful and widely used scientific tool in metabolic engineering and systems biology. Traditionally, metabolic fluxes have been determined from measurements of isotopic labeling by means of mass spectrometry (MS) or nuclear magnetic resonance (NMR). In recent years, tandem MS has emerged as a new analytical technique that can provide additional information for high-resolution quantification of metabolic fluxes in complex biological systems. In this paper, we present recent advances in methods and algorithms for incorporating tandem MS measurements into existing 13C-MFA approaches that are based on the elementary metabolite units (EMU) framework. Specifically, efficient EMU-based algorithms are presented for simulating tandem MS data, tracing isotopic labeling in biochemical network models and for correcting tandem MS data for natural isotope abundances

Elucidating dipeptide utilization and metabolism by CHO cells for improved cell culture performance

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Dissecting the genetic and metabolic mechanisms of adaptation to the knockout of a major metabolic enzyme in Escherichia coli

Unraveling the mechanisms of microbial adaptive evolution following genetic or environmental challenges is of fundamental interest in biological science and engineering. When the challenge is the loss of a metabolic enzyme, adaptive responses can also shed significant insight into metabolic robustness, regulation, and areas of kinetic limitation. In this study, whole-genome sequencing and highresolution C-13-metabolic flux analysis were performed on 10 adaptively evolved pgi knockouts of Escherichia coli. Pgi catalyzes the first reaction in glycolysis, and its loss results in major physiological and carbon catabolism pathway changes, including an 80% reduction in growth rate. Following adaptive laboratory evolution (ALE), the knockouts increase their growth rate by up to 3.6-fold. Through combined genomic-fluxomic analysis, we characterized the mutations and resulting metabolic fluxes that enabled this fitness recovery. Large increases in pyridine cofactor transhydrogenase flux, correcting imbalanced production of NADPH and NADH, were enabled by direct mutations to the transhydrogenase genes sthA and pntAB. The phosphotransferase system component crr was also found to be frequently mutated, which corresponded to elevated flux from pyruvate to phosphoenolpyruvate. The overall energy metabolism was found to be strikingly robust, and what have been previously described as latently activated Entner-Doudoroff and glyoxylate shunt pathways are shown here to represent no real increases in absolute flux relative to the wild type. These results indicate that the dominant mechanism of adaptation was to relieve the rate-limiting steps in cofactor metabolism and substrate uptake and to modulate global transcriptional regulation from stress response to catabolism

Evolution of <i>E. coli</i> on [U^-13C] Glucose Reveals a Negligible Isotopic Influence on Metabolism and Physiology

13C-Metabolic flux analysis (13C-MFA) traditionally assumes that kinetic isotope effects from isotopically labeled compounds do not appreciably alter cellular growth or metabolism, despite indications that some biochemical reactions can be non-negligibly impacted. Here, populations of Escherichia coli were adaptively evolved for ~1000 generations on uniformly labeled 13C-glucose, a commonly used isotope for 13C-MFA. Phenotypic characterization of these evolved strains revealed ~40% increases in growth rate, with no significant difference in fitness when grown on either labeled (13C) or unlabeled (12C) glucose. The evolved strains displayed decreased biomass yields, increased glucose and oxygen uptake, and increased acetate production, mimicking what is observed after adaptive evolution on unlabeled glucose. Furthermore, full genome re-sequencing revealed that the key genetic changes underlying these phenotypic alterations were essentially the same as those acquired during adaptive evolution on unlabeled glucose. Additionally, glucose competition experiments demonstrated that the wild-type exhibits no isotopic preference for unlabeled glucose, and the evolved strains have no preference for labeled glucose. Overall, the results of this study indicate that there are no significant differences between 12C and 13C-glucose as a carbon source for E. coli growth

Tracking amino acid metabolism in CHO cell cultures using stable isotope labeling assisted metabolomics

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Elucidating amino acid metabolism in CHO cells

CHO cells require complex media for cell growth and protein production. The major components of industrial media are amino acids, however, relatively little is known about the metabolism of amino acids in CHO cell cultures. Here, we applied advanced 13C-flux analysis tools to elucidate the metabolic flow of the amino acids in a fed-batch CHO culture that overproduced IgG. Carbon flows were tracked throughout the growth phase and changes in metabolism were quantified when cells transitioned from growth phase to stationary phase. In addition, we quantified how changes in amino acids profiles in the medium translated to changes in cell growth, protein production and product quality attributes. To trace each amino acid individually, custom media formulations were used, where each medium formulation was depleted of a specific amino acid. A labeled 13C variant of the depleted amino acid was then added to the medium at the desired concentration. CHO cells were then grown in fed-batch culture. As the cells metabolized the labeled amino acids, this resulted in a redistribution of 13C-atoms which we quantified using GC-MS for both extracellular metabolites (including lactate, amino acids and the IgG product) and intracellular metabolites (including free intracellular metabolites, cell proteins, lipids and carbohydrates). We then estimated metabolic fluxes using state-of-the-art 13C-metabolic flux analysis. This allowed us to calculate the fraction of each amino acid that was used for cell growth, protein production, lactate formation and energy generation. We also investigated the effects of labeling in both the batch and fed-batch stationary phase. Finally, we investigated the effects of varying amino acid concentrations. Each 13C-labeled amino acid was added to the medium at a lower or higher concentration compared to the base medium. 13C-metabolic flux analysis was again performed and changes in fluxes were compared in order to determine the precise impacts of amino acid concentration changes on the flux profiles. Taking all of this data together, we are now building a predictive kinetic model that relates how the metabolism of CHO cells can be predicted from amino acid profiles. In future work, model predictions will be experimentally validated as a means of optimizing the amino acid composition of industrial culture media