Estimation Of Metabolic Fluxes Research Articles

Abstract 18: The polyamine-hypusine axis is a promising avenue to improve T-cell metabolism and persistence for adoptive cell therapy

Abstract There are significant barriers to the application of Adoptive Cell Therapy (ACT) to many cancer types. One of these barriers is a result of poor metabolic fitness of T cells, leading to poor proliferation, persistence, and dampened anti-tumor function. Leveraging T-cell metabolism to improve their function is becoming increasingly important to make significant strides in immunotherapy. Stat5a has been shown to be a master regulator of energy and metabolism in T cells, highlighting it as a promising avenue to improve T-cell metabolism for ACT. We have established that the expression of Constitutively Active Signal Transducer and Activator of Transcription 5A (CASTAT5) on tumor-specific CD4+ T cells improves their persistence, polyfunctionality, and anti-tumor effects in a mouse model. This study further examines Stat5a and its downstream metabolic targets in the context of ACT. We carried out scRNAseq analysis and found that Stat5a directly interacts with genes involved in polyamine metabolism. More specifically, Stat5a activation increases the expression of ornithine decarboxylase (Odc1), spermidine synthase (Srm), deoxyhypusine synthase (Dhps), and deoxyhypusine hydroxylase (Dohh) in a subset of CD4+ T cells expressing CASTAT5. Cleavage Under Targets and Tagmentation (Cut&Tag) analysis of histone H3K27ac also demonstrated increased H3K27ac at promoter and enhancer regions of polyamine pathway genes in CASTAT5 compared to control CD4+ T cells. To further examine the function of Stat5a, the murine pro-B cell line, Ba/F3, is being used as a model to study Stat5a-mediated signaling. We have found that upon withdrawal of IL-3 from Ba/F3 cells, Stat5a activation is significantly downregulated. Our scRNA-seq-based metabolic flux estimation showed a significant reduction in metabolic fluxes and related genes, including polyamines, following IL-3 withdrawal. Correspondingly, Stat5a ChIP-Seq and Cut&Tag analyses of H3K27ac revealed Stat5a binding to active enhancer regions in the presence of IL-3 that are lost following its withdrawal. Untargeted metabolomics confirmed the marked Stat5a-mediated reduction in metabolite abundance after IL-3 deprivation, in a time-dependent manner. The polyamine-hypusine axis revealed in our studies is a promising avenue to improve T-cell function. This axis has been shown to regulate T-cell function, and potentially modulate mitochondrial respiration and T-cell activation. Hence, our studies have identified polyamine supplementation and gene modulation as promising strategies to improve the metabolism of Adoptive T cells in a Stat5a-mediated manner. Further studies will evaluate the effectiveness of this avenue in improving T-cell function, improving their efficacy for ACT, and improving patient outcomes. Citation Format: Mercy Kehinde-Ige, Ogacheko Okoko, Gang Zhou, Huidong Shi. The polyamine-hypusine axis is a promising avenue to improve T-cell metabolism and persistence for adoptive cell therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 18.

Abstract 5352: Targeting DNA methylation in T cells to improve the efficacy of immunotherapy

Abstract T-cells are critical mediators of immunity and immunologic memory. Their cell fates are regulated in part through epigenetic mechanisms, including DNA methylation. Recent genome-wide methylation analyses have revealed dynamic alterations in the methylome at various stages of development and differentiation of T cells. At single cell level, it is not easy to simultaneously collect RNA-seq and RBBS methylation profiling. An important task is to understand the expression change of which genes and pathways are regulated by DNA methylations, especially for the ones that are associated with functional variations in the T cells from tumor microenvironment.In this study, we developed a computational approach based on our recently developed metabolic flux estimation to estimate cell-wise global DNA methylation activity level by using scRNA-seq data. We also hypothesize that the global DNA methylation activity level in one cell determines most of the DNA methylation level in gene-specific DNA methylations. Hence, the dependency between gene-specific DNA methylation and expression could be imputed by the dependency between predicted global DNA methylation input level and the gene expression. We validated our method to impute cell-wise global DNA methylation level by using four independent sets of paired gene expression and DNA methylation data. Noted, our prediction of global DNA methylation activity is from a pure metabolic perspective. We found that two metabolic reaction rates, named metabolic flux from methionine to SAM and SAM to SAH, purely predicted by using gene expression data can accurately impute DNA methylation activity in all validating data sets. Our method enables further identification of the disease/cell context specific contributor of DNA methylation, i.e., the genes high contribution to DNA methylations in each individual cell or cell groups. We applied our method on scRNA-seq data of different T cell types extracted from TME of lung, liver, and colon cancer. We have seen that exhausted T cells, especially the ones with decreased Granzymes and PRF1 are associated with increased global DNA methylation level and related genes, suggesting the potential clinical implications in targeting DNA methylation to improve the efficacy of immunotherapies. Citation Format: Pengtao Dang, Xiao Wang, Haiqi Zhu, Jia Wang, Tingbo Guo, Xinyu Zhou, Paveethran Swaminathan, Chi Zhang, Sha Cao. Targeting DNA methylation in T cells to improve the efficacy of immunotherapy. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5352.

Validation of carbon isotopologue distribution measurements by GC-MS and application to 13C-metabolic flux analysis of the tricarboxylic acid cycle in Brassica napus leaves.

The estimation of metabolic fluxes in photosynthetic organisms represents an important challenge that has gained interest over the last decade with the development of 13C-Metabolic Flux Analysis at isotopically non-stationary steady-state. This approach requires a high level of accuracy for the measurement of Carbon Isotopologue Distribution in plant metabolites. But this accuracy has still not been evaluated at the isotopologue level for GC-MS, leading to uncertainties for the metabolic fluxes calculated based on these fragments. Here, we developed a workflow to validate the measurements of CIDs from plant metabolites with GC-MS by producing tailor-made E. coli standard extracts harboring a predictable binomial CID for some organic and amino acids. Overall, most of our TMS-derivatives mass fragments were validated with these standards and at natural isotope abundance in plant matrices. Then, we applied this validated MS method to investigate the light/dark regulation of plant TCA cycle by incorporating U-13C-pyruvate to Brassica napus leaf discs. We took advantage of pathway-specific isotopologues/isotopomers observed between two and six hours of labeling to show that the TCA cycle can operate in a cyclic manner under both light and dark conditions. Interestingly, this forward cyclic flux mode has a nearly four-fold higher contribution for pyruvate-to-citrate and pyruvate-to-malate fluxes than the phosphoenolpyruvate carboxylase (PEPc) flux reassimilating carbon derived from some mitochondrial enzymes. The contribution of stored citrate to the mitochondrial TCA cycle activity was also questioned based on dynamics of 13C-enrichment in citrate, glutamate and succinate and variations of citrate total amounts under light and dark conditions. Interestingly, there was a light-dependent 13C-incorporation into glycine and serine showing that decarboxylations from pyruvate dehydrogenase complex and TCA cycle enzymes were actively reassimilated and could represent up to 5% to net photosynthesis.

Entner-Doudoroff pathway in Synechocystis PCC 6803: Proposed regulatory roles and enzyme multifunctionalities.

The Entner-Doudoroff pathway (ED-P) was established in 2016 as the fourth glycolytic pathway in Synechocystis sp. PCC 6803. ED-P consists of two reactions, the first catalyzed by 6-phosphogluconate dehydratase (EDD), the second by keto3-deoxygluconate-6-phosphate aldolase/4-hydroxy-2-oxoglutarate aldolase (EDA). ED-P was previously concluded to be a widespread (∼92%) pathway among cyanobacteria, but current bioinformatic analysis estimated the occurrence of ED-P to be either scarce (∼1%) or uncommon (∼47%), depending if dihydroxy-acid dehydratase (ilvD) also functions as EDD (currently assumed). Thus, the biochemical characterization of ilvD is a task pending to resolve this uncertainty. Next, we have provided new insights into several single and double glycolytic mutants based on kinetic model of central carbon metabolism of Synechocystis. The model predicted that silencing 6-phosphogluconate dehydrogenase (gnd) could be coupled with ∼90% down-regulation of G6P-dehydrogenase, also limiting the metabolic flux via ED-P. Furthermore, our metabolic flux estimation implied that growth impairment linked to silenced EDA under mixotrophic conditions is not caused by diminished carbon flux via ED-P but rather by a missing mechanism related to the role of EDA in metabolism. We proposed two possible, mutually non-exclusive explanations: (i) Δeda leads to disrupted carbon catabolite repression, regulated by 2-keto3-deoxygluconate-6-phosphate (ED-P intermediate), and (ii) EDA catalyzes the interconversion between glyoxylate and 4-hydroxy-2-oxoglutarate + pyruvate in the proximity of TCA cycle, possibly effecting the levels of 2-oxoglutarate under Δeda. We have also proposed a new pathway from EDA toward proline, which could explain the proline accumulation under Δeda. In addition, the presented in silico method provides an alternative to 13C metabolic flux analysis for marginal metabolic pathways around/below the threshold of ultrasensitive LC-MS. Finally, our in silico analysis provided alternative explanations for the role of ED-P in Synechocystis while identifying some severe uncertainties.

Multi-omics-based label-free metabolic flux inference reveals obesity-associated dysregulatory mechanisms in liver glucose metabolism

SummaryGlucose homeostasis is maintained by modulation of metabolic flux. Enzymes and metabolites regulate the involved metabolic pathways. Dysregulation of glucose homeostasis is a pathological event in obesity. Analyzing metabolic pathways and the mechanisms contributing to obesity-associated dysregulation in vivo is challenging. Here, we introduce OMELET: Omics-Based Metabolic Flux Estimation without Labeling for Extended Trans-omic Analysis. OMELET uses metabolomic, proteomic, and transcriptomic data to identify relative changes in metabolic flux, and to calculate contributions of metabolites, enzymes, and transcripts to the changes in metabolic flux. By evaluating the livers of fasting ob/ob mice, we found that increased metabolic flux through gluconeogenesis resulted primarily from increased transcripts, whereas that through the pyruvate cycle resulted from both increased transcripts and changes in substrates of metabolic enzymes. With OMELET, we identified mechanisms underlying the obesity-associated dysregulation of metabolic flux in the liver.

A new strategy for dynamic metabolic flux estimation by integrating transient metabolome data into genome-scale metabolic models.

Metabolic flux analysis (MFA) is a powerful tool for studying microbial cell physiology. Isotope-based MFA is the accepted standard for studying metabolic fluxes under steady-state conditions. However, its application under dynamic extracellular conditions is limited due to lack of proper techniques, such as rapid sampling and quenching, high cost and laborious execution. Here, we propose a new strategy to tackle this through incorporating dynamic metabolite abundance data into genome-scale metabolic models (GEM). First, a dummy extracellular pool concept is proposed for each dynamically changing metabolite, which represents a "sink" or "source", with corresponding dummy reactions coded into the GEM model. The dynamic model (expressed as differential equations) is then transformed into a quasi-steady-state model (expressed as linear equations), which can be easily solved by constraining the GEM model with the dynamic metabolite quantification data. For this, common linear-programming optimization algorithms were utilized to estimate the dynamic fluxes. Dynamic high-accuracy metabolite abundance data were obtained through the Isotope Dilution Mass Spectrometry (IDMS) method and high-speed sampling-quenching, and it was demonstrated that the newly proposed strategy could be successfully applied to obtain intracellular dynamic fluxes of Aspergillus niger under regimes of single and periodic extracellular glucose pulses. The applicability of the new method was also tested on dynamic fluxes estimation in a glucose pulse-response study of Saccharomyces cerevisiae. The proposed method provides a powerful tool to investigate cell physiology under dynamic conditions, especially relevant for bioprocess scale-up to industrial-scale bioreactors.

Squeezing Flux Out of Fat

Merging transcriptomics or metabolomics data remains insufficient for metabolic flux estimation. Ramirez et al. integrate a genome-scale metabolic model with extracellular flux data to predict and validate metabolic differences between white and brown adipose tissue. This method allows both metabolic phenotyping and the identification of potential therapeutic targets for obesity.

Intracellular product recycling in high succinic acid producing yeast at low pH

BackgroundThe metabolic engineering of Saccharomyces cerevisiae for the production of succinic acid has progressed dramatically, and a series of high-producing hosts are available. At low cultivation pH and high titers, the product transport can become bidirectional, i.e. the acid is reentering the cell and is again exported or even catabolized. Here, a quantitative approach for the identification of product recycling fluxes is developed.ResultsThe metabolic flux distributions at two time-points of the fermentation process were analyzed. 13C labeled succinic acid was added to the extracellular space and intracellular enrichments were measured and subsequently used for the estimation of metabolic fluxes. The labeling was introduced by a labeling switch experiment, leading to an immediate labeling of about 85% of the acid while keeping the total acid concentration constant. Within 100 s significant labeling enrichment of the TCA cycle intermediates fumarate, iso-citrate and α-ketoglutarate was observed, while no labeling was detected for malate and citrate. These findings suggest that succinic acid is rapidly exchanged over the cellular membrane and enters the oxidative TCA cycle. Remarkably, in the oxidative direction malate 13C enrichment was not detected, indicating that there is no flux going through this metabolite pool. Using flux modeling and thermodynamic assumptions on compartmentation it was concluded that malate must be predominantly cytosolic while fumarate and iso-citrate were more dominant in the mitochondria.ConclusionsAdding labeled product without changing the extracellular environment allowed to quantify intracellular metabolic fluxes under high producing conditions and identify product degradation cycles. In the specific case of succinic acid production, compartmentation was found to play a major role, i.e. the presence of metabolic activity in two different cellular compartments lead to intracellular product degradation reducing the yield. We also observed that the flux from glucose to succinic acid branches at two points in metabolism: (1) At the level of pyruvate, and (2) at cytosolic malate which was not expected.

3D TOCSY-HSQC NMR for Metabolic Flux Analysis Using Non-Uniform Sampling.

(13)C-Metabolic Flux Analysis ((13)C-MFA) is rapidly being recognized as the authoritative method for determining fluxes through metabolic networks. Site-specific (13)C enrichment information obtained using NMR spectroscopy is a valuable input for (13)C-MFA experiments. Chemical shift overlaps in the 1D or 2D NMR experiments typically used for (13)C-MFA frequently hinder assignment and quantitation of site-specific (13)C enrichment. Here we propose the use of a 3D TOCSY-HSQC experiment for (13)C-MFA. We employ Non-Uniform Sampling (NUS) to reduce the acquisition time of the experiment to a few hours, making it practical for use in (13)C-MFA experiments. Our data show that the NUS experiment is linear and quantitative. Identification of metabolites in complex mixtures, such as a biomass hydrolysate, is simplified by virtue of the (13)C chemical shift obtained in the experiment. In addition, the experiment reports (13)C-labeling information that reveals the position specific labeling of subsets of isotopomers. The information provided by this technique will enable more accurate estimation of metabolic fluxes in large metabolic networks.

Effective Estimation of Dynamic Metabolic Fluxes Using (13)C Labeling and Piecewise Affine Approximation: From Theory to Practical Applicability.

The design of microbial production processes relies on rational choices for metabolic engineering of the production host and the process conditions. These require a systematic and quantitative understanding of cellular regulation. Therefore, a novel method for dynamic flux identification using quantitative metabolomics and 13C labeling to identify piecewise-affine (PWA) flux functions has been described recently. Obtaining flux estimates nevertheless still required frequent manual reinitalization to obtain a good reproduction of the experimental data and, moreover, did not optimize on all observables simultaneously (metabolites and isotopomer concentrations). In our contribution we focus on measures to achieve faster and robust dynamic flux estimation which leads to a high dimensional parameter estimation problem. Specifically, we address the following challenges within the PWA problem formulation: (1) Fast selection of sufficient domains for the PWA flux functions, (2) Control of over-fitting in the concentration space using shape-prescriptive modeling and (3) robust and efficient implementation of the parameter estimation using the hybrid implicit filtering algorithm. With the improvements we significantly speed up the convergence by efficiently exploiting that the optimization problem is partly linear. This allows application to larger-scale metabolic networks and demonstrates that the proposed approach is not purely theoretical, but also applicable in practice.

Natural isotope correction of MS/MS measurements for metabolomics and (13)C fluxomics.

Fluxomics and metabolomics are crucial tools for metabolic engineering and biomedical analysis to determine the in vivo cellular state. Especially, the application of (13)C isotopes allows comprehensive insights into the functional operation of cellular metabolism. Compared to single MS, tandem mass spectrometry (MS/MS) provides more detailed and accurate measurements of the metabolite enrichment patterns (tandem mass isotopomers), increasing the accuracy of metabolite concentration measurements and metabolic flux estimation. MS-type data from isotope labeling experiments is biased by naturally occurring stable isotopes (C, H, N, O, etc.). In particular, GC-MS(/MS) requires derivatization for the usually non-volatile intracellular metabolites introducing additional natural isotopes leading to measurements that do not directly represent the carbon labeling distribution. To make full use of LC- and GC-MS/MS mass isotopomer measurements, the influence of natural isotopes has to be eliminated (corrected). Our correction approach is analyzed for the two most common applications; (13)C fluxomics and isotope dilution mass spectrometry (IDMS) based metabolomics. Natural isotopes can have an impact on the calculated flux distribution which strongly depends on the substrate labeling and the actual flux distribution. Second, we show that in IDMS based metabolomics natural isotopes lead to underestimated concentrations that can and should be corrected with a nonlinear calibration. Our simulations indicate that the correction for natural abundance in isotope based fluxomics and quantitative metabolomics is essential for correct data interpretation.

Efficient Modeling of MS/MS Data for Metabolic Flux Analysis.

Metabolic flux analysis (MFA) is a widely used method for quantifying intracellular metabolic fluxes. It works by feeding cells with isotopic labeled nutrients, measuring metabolite isotopic labeling, and computationally interpreting the measured labeling data to estimate flux. Tandem mass-spectrometry (MS/MS) has been shown to be useful for MFA, providing positional isotopic labeling data. Specifically, MS/MS enables the measurement of a metabolite tandem mass-isotopomer distribution, representing the abundance in which certain parent and product fragments of a metabolite have different number of labeled atoms. However, a major limitation in using MFA with MS/MS data is the lack of a computationally efficient method for simulating such isotopic labeling data. Here, we describe the tandemer approach for efficiently computing metabolite tandem mass-isotopomer distributions in a metabolic network, given an estimation of metabolic fluxes. This approach can be used by MFA to find optimal metabolic fluxes, whose induced metabolite labeling patterns match tandem mass-isotopomer distributions measured by MS/MS. The tandemer approach is applied to simulate MS/MS data in a small-scale metabolic network model of mammalian methionine metabolism and in a large-scale metabolic network model of E. coli. It is shown to significantly improve the running time by between two to three orders of magnitude compared to the state-of-the-art, cumomers approach. We expect the tandemer approach to promote broader usage of MS/MS technology in metabolic flux analysis. Implementation is freely available at www.cs.technion.ac.il/~tomersh/methods.html

Steady-state ¹³C fluxomics using OpenFLUX.

Metabolic flux estimation using (13)C isotopic tracers ((13)C-MFA) provides a greater resolution of intracellular fluxes than using only cell growth and consumption/production rates. However, (13)C-MFA is computationally more demanding. A nonlinear least-square optimization process is employed to constrain metabolic fluxes using atom balance models and experimentally measured (13)C labelling pattern of intracellular or proteinogenic metabolites. OpenFLUX was therefore developed for the purpose of streamlining the computational workflow. Here, we describe in detail the computational procedure for performing (13)C-MFA using OpenFLUX. We also provide some helpful information on model reconstruction and GC-MS data treatment.

Assessment of energetic costs of AhR activation by β-naphthoflavone in rainbow trout (Oncorhynchus mykiss) hepatocytes using metabolic flux analysis

Exposure to environmental contaminants such as activators of the aryl hydrocarbon receptor (AhR) leads to the induction of defense and detoxification mechanisms. While these mechanisms allow organisms to metabolize and excrete at least some of these environmental contaminants, it has been proposed that these mechanisms lead to significant energetic challenges. This study tests the hypothesis that activation of the AhR by the model agonist β-naphthoflavone (βNF) results in increased energetic costs in rainbow trout (Oncorhynchus mykiss) hepatocytes. To address this hypothesis, we employed traditional biochemical approaches to examine energy allocation and metabolism including the adenylate energy charge (AEC), protein synthesis rates, Na+/K+-ATPase activity, and enzyme activities. Moreover, we have used for the first time in a fish cell preparation, metabolic flux analysis (MFA) an in silico approach for the estimation of intracellular metabolic fluxes. Exposure of trout hepatocytes to 1μM βNF for 48h did not alter hepatocyte AEC, protein synthesis, or Na+/K+-ATPase activity but did lead to sparing of glycogen reserves and changes in activities of alanine aminotransferase and citrate synthase suggesting altered metabolism. Conversely, MFA did not identify altered metabolic fluxes, although we do show that the dynamic metabolism of isolated trout hepatocytes poses a significant challenge for this type of approach which should be considered in future studies.

Effect of pH on the metabolic flux of Klebsiella oxytoca producing 2,3-butanediol in continuous cultures at different dilution rates

The efficiency of the bioconversion process and the achievable end-product concentration decides the economic feasibility of microbial 2,3-butanediol (2,3-BDO) production. In 2,3-BDO production, optimization of culture condition is required for cell growth and metabolism. Also, the pH is an important factor that influences microbial performance. For different microorganisms and substrates, it has been shown that the distribution of the metabolites in 2,3-BDO fermentation is greatly affected by pH, and the optimum pH for 2,3-BDO production seems dependently linked to the particular strain and the substrate employed. Quantification analysis of intracellular metabolites and metabolic flux analysis (MFA) were used to investigate the effect of pH on the Klebsiella oxytoca producing 2,3-BDO and other organic acids. The main objectives of MFA are the estimation of intracellular metabolic fluxes and the identification of rate-limiting step and the key enzymes. This study was conducted under continuous aerobic conditions at different dilution rates (0.1, 0.2, and 0.3 h(-1)) and different pH values (pH 5.5 and 7.0) for the steady-state experimental data. In order to obtain the flux distribution, the extracellular specific rates were calculated from the experimental data using the metabolic network model of K. oxytoca. Intracellular metabolite concentration profiles were generated using ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry.

Computational estimation of tricarboxylic acid cycle fluxes using noisy NMR data from cardiac biopsies

BackgroundThe aerobic energy metabolism of cardiac muscle cells is of major importance for the contractile function of the heart. Because energy metabolism is very heterogeneously distributed in heart tissue, especially during coronary disease, a method to quantify metabolic fluxes in small tissue samples is desirable. Taking tissue biopsies after infusion of substrates labeled with stable carbon isotopes makes this possible in animal experiments. However, the appreciable noise level in NMR spectra of extracted tissue samples makes computational estimation of metabolic fluxes challenging and a good method to define confidence regions was not yet available.ResultsHere we present a computational analysis method for nuclear magnetic resonance (NMR) measurements of tricarboxylic acid (TCA) cycle metabolites. The method was validated using measurements on extracts of single tissue biopsies taken from porcine heart in vivo. Isotopic enrichment of glutamate was measured by NMR spectroscopy in tissue samples taken at a single time point after the timed infusion of 13C labeled substrates for the TCA cycle. The NMR intensities for glutamate were analyzed with a computational model describing carbon transitions in the TCA cycle and carbon exchange with amino acids. The model dynamics depended on five flux parameters, which were optimized to fit the NMR measurements. To determine confidence regions for the estimated fluxes, we used the Metropolis-Hastings algorithm for Markov chain Monte Carlo (MCMC) sampling to generate extensive ensembles of feasible flux combinations that describe the data within measurement precision limits. To validate our method, we compared myocardial oxygen consumption calculated from the TCA cycle flux with in vivo blood gas measurements for 38 hearts under several experimental conditions, e.g. during coronary artery narrowing.ConclusionsDespite the appreciable NMR noise level, the oxygen consumption in the tissue samples, estimated from the NMR spectra, correlates with blood-gas oxygen uptake measurements for the whole heart. The MCMC method provides confidence regions for the estimated metabolic fluxes in single cardiac biopsies, taking the quantified measurement noise level and the nonlinear dependencies between parameters fully into account.

Central energy metabolism remains robust in acute steatotic hepatocytes challenged by a high free fatty acid load

Overnutrition is one of the major causes of non-alcoholic fatty liver disease (NAFLD). NAFLD is characterized by an accumulation of lipids (triglycerides) in hepatocytes and is often accompanied by high plasma levels of free fatty acids (FFA). In this study, we compared the energy metabolism in acute steatotic and non-steatotic primary mouse hepatocytes. Acute steatosis was induced by pre-incubation with high concentrations of oleate and palmitate. Labeling experiments were conducted using [U-(13)C(5),U-(15)N(2)] glutamine. Metabolite concentrations and mass isotopomer distributions of intracellular metabolites were measured and applied for metabolic flux estimation using transient 13C metabolic flux analysis. FFAs were efficiently taken up and almost completely incorporated into triglycerides (TAGs). In spite of high FFA uptake rates and the high synthesis rate of TAGs, central energy metabolism was not significantly changed in acute steatotic cells. Fatty acid β-oxidation does not significantly contribute to the detoxification of FFAs under the applied conditions.

Computer programs for modeling mammalian cell batch and fed-batch cultures using logistic equations

A MATLAB(®) toolbox was developed for applying the logistic modeling approach to mammalian cell batch and fed-batch cultures. The programs in the toolbox encompass sensitivity analyses and simulations of the logistic equations in addition to cell specific rate estimation. The toolbox was first used to generate time courses of the sensitivity equations for characterizing the relationship between the logistic variable and the model parameters. Subsequently, the toolbox was used to describe CHO cell data from batch and fed-batch mammalian cell cultures. Cell density, product, glucose, lactate, glutamine, and ammonia data were analyzed for the batch culture while fed-batch analysis included cell density and product concentration. In all instances, experimental data were well described by the logistic equations and the resulting specific rate profiles were representative of the underlying cell physiology. The 6-variable batch culture data set was also used to compare the logistic specific rates with those from polynomial fitting and discrete derivative methods. The polynomial specific rates grossly misrepresented cell behavior in the initial and final stages of culture while those based on discrete derivatives had high variability due to computational artifacts. The utility of logistic specific rates to guide process development activities was demonstrated using specific protein productivity versus growth rate trajectories for the 3 cultures examined in this study. Overall, the computer programs developed in this study enable rapid and robust analysis of data from mammalian cell batch and fed-batch cultures which can help process development and metabolic flux estimation.

Evaluating 13C enrichment data of free amino acids for precise metabolic flux analysis

Metabolic flux analysis using (13)C enrichment data of intracellular free amino acids (FAAs) can improve the time resolution of flux estimation compared to analysis of proteinogenic amino acid data owing to the faster turnover times of FAAs. The nature of the (13)C enrichment dynamics of FAAs remains obscure, however, especially with regard to its dependence on culture conditions, even though an understanding of dynamic behavior is important for precise metabolic flux estimation. In this study, we analyzed the (13)C enrichment dynamics of free and proteinogenic amino acids in a series of continuous culture experiments with Escherichia coli. The results indicated that the effect of protein degradation on the (13)C enrichment of FAAs was negligible under cellular growth conditions. Furthermore, they showed that the time scale necessary for (13)C enrichment dynamics of FAAs to reach a steady state depends on culture conditions such as oxygen uptake rate, which was likely due to different pool sizes of intracellular metabolites. The results demonstrate the importance of analyzing (13)C enrichment dynamics for the precise estimation of metabolic fluxes using FAA data.

Re-examination of metabolic fluxes in Escherichia coli during anaerobic fermentation of glucose using 13C labeling experiments and 2-dimensional nuclear magnetic resonance (NMR) spectroscopy

Improved design of metabolic flux estimation using mixed label 13C labeling experiments and identifiability analysis motivated re-examination of metabolic fluxes during anaerobic fermentation in the Escherichia coli. Comprehensive metabolic flux maps were determined by using a mixture of differently labeled glucose and compared to conventional flux maps obtained using extracellular measurements and comprehensive metabolic flux maps obtained using only U-13C glucose as the substrate. As expected, conventional flux analysis performs poorly in comparison to 13C-MFA, especially in the Embden-Meyerhof-Parnas (EMP) and pentose phosphate (PP) pathways. Identifiability analysis indicated and experiments confirmed that a mixture of 10% U-l3C glucose, 25% 1-13C glucose, and 65% naturally labeled glucose significantly improved the statistical quality of all calculated fluxes in the PP pathway, the EMP pathway, the anaplerotic reactions, and the tricarboxylic acid cycle. Modifying the network topology for the presence and absence of the Entner-Doudoroff pathway and the glyoxylate shunt did not affect the value or quality of estimated fluxes significantly. Extracellular measurement of formate production was necessary for the accurate estimation of the fluxes around the formate node.