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  • Experimental evolution reveals unifying systems-level adaptations but diversity in driving genotypes.
    Kavvas ES, Long CP, Sastry A, Poudel S, Antoniewicz MR, Ding Y, Mohamed ET, Szubin R, Monk JM, Feist AM, Palsson BO.
    mSystems, In Press, 2022 [Show Abstract]
    Genotype-fitness maps of evolution have been well characterized for biological components, such as RNA and proteins, but remain less clear for systems-level properties, such as those of metabolic and transcriptional regulatory networks. Here, we take multi-omics measurements of 6 different E. coli strains throughout adaptive laboratory evolution (ALE) to maximal growth fitness. The results show the following: (i) convergence in most overall phenotypic measures across all strains, with the notable exception of divergence in NADPH production mechanisms; (ii) conserved transcriptomic adaptations, describing increased expression of growth promoting genes but decreased expression of stress response and structural components; (iii) four groups of regulatory trade-offs underlying the adjustment of transcriptome composition; and (iv) correlates that link causal mutations to systems-level adaptations, including mutation-pathway flux correlates and mutation-transcriptome composition correlates. We thus show that fitness landscapes for ALE can be described with two layers of causation: one based on system-level properties (continuous variables) and the other based on mutations (discrete variables). IMPORTANCE: Understanding the mechanisms of microbial adaptation will help combat the evolution of drug-resistant microbes and enable predictive genome design. Although experimental evolution allows us to identify the causal mutations underlying microbial adaptation, it remains unclear how causal mutations enable increased fitness and is often explained in terms of individual components (i.e. enzyme rate) as opposed to biological systems (i.e. pathways). Here, we find that causal mutations in E. coli are linked to systems-level changes in NADPH balance and expression of stress response genes. These systems-level adaptation patterns are conserved across diverse E. coli strains and thus identify cofactor balance and proteome reallocation as dominant constraints governing microbial adaptation.
  • 13C-Metabolic flux analysis of Clostridium ljungdahlii illuminates its core metabolism under mixotrophic culture conditions.
    Dahle ML, Papoutsakis ET, Antoniewicz MR.
    Metab Eng, 72: 161-170, 2022 [Show Abstract]
    Carbon dioxide-fixing acetogenic bacteria (acetogens) utilizing the Wood-Ljungdahl Pathway (WLP) play an important role in CO2 fixation in the biosphere and in the development of biological processes – alone or in cocultures, under both autotrophic and mixotrophic conditions – for production of chemicals and fuels. To date, limited work has been reported in experimentally validating and quantifying reaction fluxes of their core metabolic pathways. Here, the core metabolic model of the acetogen Clostridium ljungdahlii was interrogated using 13C-metabolic flux analysis (13C-MFA), which required the development of a new defined culture medium. Autotrophic, heterotrophic, and mixotrophic growth in defined medium was possible by adding 1 mM methionine to replace yeast extract. Our 13C-MFA found an incomplete TCA cycle and inactive core pathways/reactions, notably those of the oxidative pentose phosphate pathway, Entner-Doudoroff pathway, and malate dehydrogenase. 13C-MFA during mixotrophic growth using the parallel tracers [1-13C]fructose, [1,2-13C]fructose, [1,2,3-13C]fructose, and [U-13C]asparagine found that externally supplied CO2 contributed the majority of carbon consumed. All internally-produced CO2 from the catabolism of asparagine and fructose was consumed by the WLP. While glycolysis of fructose was active, it was not a major contributor to overall production of ATP, NADH, and acetyl-CoA. Gluconeogenic reactions were active despite the availability of organic carbon. Asparagine was catabolized equally via conversion to threonine and subsequent cleavage to produce acetaldehyde and glycine, and via deamination to fumarate and then the anaplerotic conversion of malate to pyruvate. Both pathways for asparagine catabolism produced acetyl-CoA, either directly via pyruvate or indirectly via the WLP. Cofactor stoichiometry based on our data predicted an essentially zero flux through the ferredoxin-dependent transhydrogenase (Nfn) reaction. Instead, nearly all of NADPH generated from the hydrogenase reaction was consumed by the WLP. Reduced ferredoxin produced by the hydrogenase reaction and glycolysis was mostly used for ATP generation via the RNF/ATPase system, with the remainder consumed by the WLP. NADH produced by RNF/ATPase was entirely consumed via the WLP.
  • Coordinated reprogramming of metabolism and cell function in adipocytes from proliferation to differentiation.
    Oates EH, Antoniewicz MR.
    Metab Eng, 69: 221-230, 2022 [Show Abstract]
    Adipose tissue plays a major role in regulating lipid and energy homeostasis by storing excess nutrients, releasing energetic substrates through lipolysis, and regulating metabolism of other tissues and organs through endocrine and paracrine signaling. Adipocytes within fat tissues store excess nutrients through increased cell number (hyperplasia), increased cell size (hypertrophy), or both. The differentiation of pre-adipocytes into mature lipid-accumulating adipocytes requires a complex interaction of metabolic pathways that is still incompletely understood. Here, we applied parallel labeling experiments and 13C-metabolic flux analysis to quantify precise metabolic fluxes in proliferating and differentiated 3T3-L1 cells, a widely used model to study adipogenesis. We found that morphological and biomass composition changes in adipocytes were accompanied by significant shifts in metabolic fluxes, encompassing all major metabolic pathways. In contrast to proliferating cells, differentiated adipocytes 1) increased glucose uptake and redirected glucose utilization from lactate production to lipogenesis and energy generation; 2) increased pathway fluxes through glycolysis, oxidative pentose phosphate pathway and citric acid cycle; 3) reduced lactate secretion, resulting in increased ATP generation via oxidative phosphorylation; 4) rewired glutamine metabolism, from glutaminolysis to de novo glutamine synthesis; 5) increased cytosolic NADPH production, driven mostly by increased cytosolic malic enzyme flux; 6) increased production of monounsaturated C16:1; and 7) activated a mitochondrial pyruvate cycle through simultaneous activity of pyruvate carboxylase, malate dehydrogenase and malic enzyme. Taken together, these results quantitatively highlight the complex interplay between pathway fluxes and cell function in adipocytes, and suggest a functional role for metabolic reprogramming in adipose differentiation and lipogenesis.


  • Improving the methanol tolerance of an Escherichia coli methylotroph via adaptive laboratory evolution enhances synthetic methanol utilization.
    Bennett RK, Gregory GJ, Gonzalez JE, Har JRG, Antoniewicz MR, Papoutsakis ET.
    Front Microbiol, 12: 638426, 2021 [Show Abstract]
    There is great interest in developing synthetic methylotrophs that harbor methane and methanol utilization pathways in heterologous hosts such as Escherichia coli for industrial bioconversion of one-carbon compounds. While there are recent reports that describe the successful engineering of synthetic methylotrophs, additional efforts are required to achieve the robust methylotrophic phenotypes required for industrial realization. Here, we address an important issue of synthetic methylotrophy in E. coli: methanol toxicity. Both methanol, and its oxidation product, formaldehyde, are cytotoxic to cells. Methanol alters the fluidity and biological properties of cellular membranes while formaldehyde reacts readily with proteins and nucleic acids. Thus, efforts to enhance the methanol tolerance of synthetic methylotrophs are important. Here, adaptive laboratory evolution was performed to improve the methanol tolerance of several E. coli strains, both methylotrophic and non-methylotrophic. Serial batch passaging in rich medium containing toxic methanol concentrations yielded clones exhibiting improved methanol tolerance. In several cases, these evolved clones exhibited a >50% improvement in growth rate and biomass yield in the presence of high methanol concentrations compared to the respective parental strains. Importantly, one evolved clone exhibited a 2-3-fold improvement in the methanol utilization phenotype, as determined via 13C-labeling, at non-toxic, industrially relevant methanol concentrations compared to the respective parental strain. Whole genome sequencing was performed to identify causative mutations contributing to methanol tolerance. Common mutations were identified in 30S ribosomal subunit proteins, which increased translational accuracy and provided insight into a novel methanol tolerance mechanism. This study addresses an important issue of synthetic methylotrophy in E. coli and provides insight as to how methanol toxicity can be alleviated via enhancing methanol tolerance. Coupled improvement of methanol tolerance and synthetic methanol utilization is an important advancement for the field of synthetic methylotrophy.
  • Adaptive laboratory evolution of methylotrophic Escherichia coli enables synthesis of all amino acids from methanol-derived carbon.
    Har JRG, Agee A, Bennett RK, Papoutsakis ET, Antoniewicz MR.
    Appl Microbiol Biotechnol, 105(2): 869-876, 2021 [Show Abstract]
    Recent attempts to create synthetic Escherichia coli methylotrophs identified that de novo biosynthesis of amino acids, in the presence of methanol, presents significant challenges in achieving autonomous methylotrophic growth. Previously engineered methanol-dependent strains required co-utilization of stoichiometric amounts of co-substrates and methanol. As such, these strains could not be evolved to grow on methanol alone. In this work, we have explored an alternative approach to enable biosynthesis of all amino acids from methanol-derived carbon in minimal media without stoichiometric coupling. First, we identified that biosynthesis of threonine was limiting the growth of our methylotrophic E. coli. To address this, we performed adaptive laboratory evolution to generate a strain that grew efficiently in minimal medium with methanol and threonine. Methanol assimilation and growth of the evolved strain were analyzed and, interestingly, we found that the evolved strain synthesized all amino acids, including threonine, from methanol-derived carbon. The evolved strain was then further engineered through overexpression of an optimized threonine biosynthetic pathway. We show that the resulting methylotrophic E. coli strain has a methanol-dependent growth phenotype with homoserine as co-substrate. In contrast to previous methanol-dependent strains, co-utilization of homoserine is not stoichiometrically linked to methanol assimilation. As such, future engineering of this strain and successive adaptive evolution could enable autonomous growth on methanol as the sole carbon source.
  • A guide to metabolic flux analysis in metabolic engineering: Methods, tools and applications.
    Antoniewicz MR.
    Metab Eng, 63: 2-12, 2021 [Show Abstract]
    The field of metabolic engineering is primarily concerned with improving the biological production of value-added chemicals, fuels and pharmaceuticals through the design, construction and optimization of metabolic pathways, redirection of intracellular fluxes, and refinement of cellular properties relevant for industrial bioprocess implementation. Metabolic network models and metabolic fluxes are central concepts in metabolic engineering, as was emphasized in the first paper published in this journal, “Metabolic fluxes and metabolic engineering” (Metabolic Engineering, 1: 1-11, 1999). In the past two decades, a wide range of computational, analytical and experimental approaches have been developed to interrogate the capabilities of biological systems through analysis of metabolic network models using techniques such as flux balance analysis (FBA), and quantify metabolic fluxes using constrained-based modeling approaches such as metabolic flux analysis (MFA) and more advanced experimental techniques based on the use of stable-isotope tracers, i.e. 13C-metabolic flux analysis (13C-MFA). In this review, we describe the basic principles of metabolic flux analysis, discuss current best practices in flux quantification, highlight potential pitfalls and alternative approaches in the application of these tools, and give a broad overview of pragmatic applications of flux analysis in metabolic engineering practice.
  • Regulatory interventions improve the biosynthesis of limiting amino acids from methanol carbon to improve synthetic methylotrophy in Escherichia coli.
    Bennett RK, Agee A, Har JRG, von Hagel B, Antoniewicz MR, Papoutsakis ET.
    Biotechnol Bioeng, 118(1), 43-57, 2021 [Show Abstract]
    Synthetic methylotrophy aims to engineer methane and methanol utilization pathways in platform hosts like Escherichia coli for industrial bioprocessing of natural gas and biogas. While recent attempts to engineer synthetic methylotrophs have proved successful, autonomous methylotrophy, i.e. the ability to utilize methane or methanol as sole carbon and energy substrates, has not yet been realized. Here, we address an important limitation of autonomous methylotrophy in E. coli: the inability of the organism to synthesize several amino acids when grown on methanol. We targeted global and local amino acid regulatory networks. Those include removal of amino acid allosteric feedback inhibition (argAH15Y, ilvAL447F, hisGE271K, leuAG462D, proBD107N, thrAS345F, trpES40F), knockouts of transcriptional repressors (ihfA, metJ); and overexpression of amino acid biosynthetic operons (hisGDCBHAFI, leuABCD, thrABC, trpEDCBA) and transcriptional regulators (crp, purR). Compared to the parent methylotrophic E. coli strain that was unable to synthesize these amino acids from methanol carbon, these strategies resulted in improved biosynthesis of limiting proteinogenic amino acids (histidine, leucine, lysine, methionine, phenylalanine, threonine, tyrosine) from methanol carbon. In several cases, improved amino acid biosynthesis from methanol carbon led to improvements in methylotrophic growth in methanol minimal medium supplemented with a small amount of yeast extract. This study addresses a key limitation currently preventing autonomous methylotrophy in E. coli and possibly other synthetic methylotrophs and provides insight as to how this limitation can be alleviated via global and local regulatory modifications.


  • A guide to deciphering microbial interactions and metabolic fluxes in microbiome communities.
    Antoniewicz MR.
    Curr Opin Biotechnol, 64: 230-237, 2020 [Show Abstract]
    Microbiomes occupy nearly all environments on Earth. These communities of interacting microorganisms are highly complex, dynamic biological systems that impact and reshape the molecular composition of their habitats by performing complex biochemical transformations. The structure and function of microbiomes are influenced by local environmental stimuli and spatiotemporal changes. In order to control the dynamics and ultimately the function of microbiomes, we need to develop a mechanistic and quantitative understanding of the ecological, molecular, and evolutionary driving forces that govern these systems. Here, we describe recent advances in developing computational and experimental approaches that can promote a more fundamental understanding of microbial communities through comprehensive model-based analysis of heterogeneous data types across multiple scales, from intracellular metabolism, to metabolite cross-feeding interactions, to the emergent macroscopic behaviors. Ultimately, harnessing the full potential of microbiomes for practical applications will require developing new predictive modeling approaches and better tools to manipulate microbiome interactions.
  • Triggering the stringent response enhances synthetic methanol utilization in Escherichia coli.
    Bennett RK, Agee A, Har JRG, von Hagel B, Siu KH, Antoniewicz MR, Papoutsakis ET.
    Metab Eng, 61: 1-10, 2020 [Show Abstract]
    Synthetic methylotrophy aims to engineer methane and methanol utilization pathways in platform hosts like Escherichia coli for industrial bioprocessing of natural gas and biogas. While recent attempts to engineer synthetic methylotrophs have proved successful, autonomous methylotrophy, i.e. the ability to utilize methane or methanol as sole carbon and energy substrates, has not yet been realized. Here, we address an important limitation of autonomous methylotrophy in E. coli: the inability of the organism to synthesize several amino acids when grown on methanol. By activating the stringent/stress response via ppGpp overproduction, or DksA and RpoS overexpression, we demonstrate improved biosynthesis of proteinogenic amino acids via endogenous upregulation of amino acid synthesis pathway genes. Thus, we were able to achieve biosynthesis of several limiting amino acids from methanol-derived carbon, in contrast to the control methylotrophic E. coli strain. This study addresses a key limitation currently preventing autonomous methylotrophy in E. coli and possibly other synthetic methylotrophs and provides insight as to how this limitation can be alleviated via stringent/stress response activation.
  • Engineering Escherichia coli for methanol-dependent growth on glucose for metabolite production.
    Bennett RK, Dillon M, Har JRG, Agee A, von Hagel B, Rohlhill J, Antoniewicz MR, Papoutsakis ET.
    Metab Eng, 60: 45-55, 2020 [Show Abstract]
    Synthetic methylotrophy aims to engineer methane and methanol utilization pathways in platform hosts like Escherichia coli for industrial bioprocessing of natural gas and biogas. While recent attempts to engineer synthetic methanol auxotrophs have proved successful, these studies focused on scarce and expensive co-substrates. Here, we engineered E. coli for methanol-dependent growth on glucose, an abundant and inexpensive co-substrate, via deletion of glucose 6-phosphate isomerase (pgi), phosphogluconate dehydratase (edd), and ribose 5-phosphate isomerases (rpiAB). Since the parental strain did not exhibit methanol-dependent growth on glucose in minimal medium, we first achieved methanol-dependent growth via amino acid supplementation and used this medium to evolve the strain for methanol-dependent growth in glucose minimal medium. The evolved strain exhibited a maximum growth rate of 0.15 h-1 in glucose minimal medium with methanol, which is comparable to that of other synthetic methanol auxotrophs. Whole genome sequencing and 13C-metabolic flux analysis revealed the causative mutations in the evolved strain. A mutation in the phosphotransferase system enzyme I gene (ptsI) resulted in a reduced glucose uptake rate to maintain a one-to-one molar ratio of substrate utilization. Deletion of the e14 prophage DNA region resulted in two non-synonymous mutations in the isocitrate dehydrogenase (icd) gene, which reduced TCA cycle carbon flux to maintain the internal redox state. In high cell density glucose fed-batch fermentation, methanol-dependent acetone production resulted in 22% average carbon labeling of acetone from 13C-methanol, which far surpasses that of the previous best (2.4%) found with methylotrophic E. coli Δpgi. This study addresses the need to identify appropriate co-substrates for engineering synthetic methanol auxotrophs and provides a basis for the next steps toward industrial one-carbon bioprocessing.
  • Improving synthetic methylotrophy via dynamic formaldehyde regulation of pentose phosphate pathway genes and redox perturbation.
    Rohlhill J, Har JRG, Antoniewicz MR, Papoutsakis ET.
    Metab Eng, 57: 247-255, 2020 [Show Abstract]
    Escherichia coli is an ideal choice for constructing synthetic methylotrophs capable of utilizing the non-native substrate methanol as a carbon and energy source. All current E. coli-based synthetic methylotrophs require co-substrates. They display variable levels of methanol-carbon incorporation due to a lack of native regulatory control of biosynthetic pathways, as E. coli does not recognize methanol as a proper substrate despite its ability to catabolize it. Here, using the E. coli formaldehyde-inducible promoter Pfrm, we implement dynamic expression control of select pentose-phosphate genes in response to the formaldehyde produced upon methanol oxidation. Genes under Pfrm control exhibited 8- to 30-fold transcriptional upregulation during growth on methanol. Formaldehyde-induced episomal expression of the B. methanolicus rpe and tkt genes involved in the regeneration of ribulose 5-phosphate required for formaldehyde fixation led to significantly improved methanol assimilation into intracellular metabolites, including a 2-fold increase of 13C-methanol into glutamate. Using a simple strategy for redox perturbation by deleting the E. coli NAD-dependent malate dehydrogenase gene maldh, we demonstrate 5-fold improved biomass formation of cells growing on methanol in the presence of a small concentration of yeast extract. Further improvements in methanol utilization are achieved via adaptive laboratory evolution and heterologous rpe and tkt expression. A short-term in vivo 13C-methanol labeling assay was used to determine methanol assimilation activity for Δmaldh strains, and demonstrated dramatically higher labeling in intracellular metabolites, including a 6-fold and 1.8-fold increase in glycine labeling for the rpe/tkt and evolved strains, respectively. The combination of formaldehyde-controlled pentose phosphate pathway expression and redox perturbation with the maldh knock-out greatly improved both growth benefit with methanol and methanol carbon incorporation into intracellular metabolites.


  • Metabolic flux responses to deletion of 20 core enzymes reveal flexibility and limits of E. coli metabolism.
    Long CP, Antoniewicz MR.
    Metab Eng, 55: 249-257, 2019 [Show Abstract]
    Despite remarkable progress in mapping biochemistry and gene-protein-reaction relationships, quantitative systems-level understanding of microbial metabolism remains a persistent challenge. Here, 13C-flux metabolic analysis was applied to interrogate metabolic responses to 20 genetic perturbations in all viable Escherichia coli single gene knockouts in upper central metabolic pathways. Strains with severe growth defects displayed highly altered intracellular flux patterns and were the most difficult to predict using current constraint-based modeling approaches. In the pfkA-knockout strain, an unexpected glucose-secretion phenotype was identified. The broad range of flux rewiring responses that were quantified suggest that some compensating pathways are more flexible than others resulting in a more robust physiology. The fact that only 2 out of 20 strains displayed an increased net pathway-flux capacity points to a fundamental rate limitation of E. coli core metabolism. In cataloguing the various cellular responses our results provide a critical resource for kinetic model development and efforts focused on genotype-to-phenotype predictions.
  • From Escherichia coli mutant 13C labeling data to a core kinetic model: A kinetic model parameterization pipeline.
    Foster CJ, Gopalakrishnan S, Antoniewicz MR, Maranas CD.
    PLoS Comp Biol, 15(9): e1007319, 2019 [Show Abstract]
    Kinetic models of metabolic networks offer the promise of quantitative phenotype prediction. The mechanistic characterization of enzyme catalyzed reactions allows for tracing the effect of perturbations in metabolite concentrations and reaction fluxes in response to genetic and environmental perturbation that are beyond the scope of stoichiometric models. In this study, we develop a two-step computational pipeline for the rapid parameterization of kinetic models of metabolic networks using a curated metabolic model and available 13C-labeling distributions under multiple genetic and environmental perturbations. The first step involves the elucidation of all intracellular fluxes in a core model of E. coli containing 74 reactions and 61 metabolites using 13C Metabolic Flux Analysis (13C-MFA). Here, fluxes corresponding to the mid-exponential growth phase are elucidated for seven single gene deletion mutants from upper glycolysis, pentose phosphate pathway and the Entner-Doudoroff pathway. The computed flux ranges are then used to parameterize the same (i.e., k-ecoli74) core kinetic model for E. coli with 55 substrate-level regulations using the newly developed K-FIT parameterization algorithm. The K-FIT algorithm employs a combination of equation decomposition and iterative solution techniques to evaluate steady-state fluxes in response to genetic perturbations. k-ecoli74 predicted 86% of flux values for strains used during fitting within a single standard deviation of 13C-MFA estimated values. By performing both tasks using the same network, errors associated with lack of congruity between the two networks are avoided, allowing for seamless integration of data with model building. Product yield predictions and comparison with previously developed kinetic models indicate shifts in flux ranges and the presence or absence of mutant strains delivering flux towards pathways of interest from training data significantly impact predictive capabilities. Using this workflow, the impact of completeness of fluxomic datasets and the importance of specific genetic perturbations on uncertainties in kinetic parameter estimation are evaluated.
  • High-resolution 13C metabolic flux analysis.
    Long, CP, Antoniewicz MR.
    Nat Protoc, 14: 2856-2877, 2019 [Show Abstract]
    Accurate quantification of metabolic pathway fluxes in biological systems is of major importance in guiding efforts in metabolic engineering, biotechnology, microbiology, human health and cell culture. 13C metabolic flux analysis (13C-MFA) is the predominant technique used for determining intracellular fluxes. Here, we present a protocol for 13C-MFA that incorporates recent advances in parallel labeling experiments, isotopic labeling measurements, statistical analysis, as well as best practices developed through decades of experience. Experiment design is an integral part of the protocol to ensure that fluxes are estimated with the highest precision. The protocol is based on growing microbes in two (or more) parallel cultures with 13C-labeled glucose tracers, followed by gas chromatography mass spectrometry measurements of isotopic labeling of protein-bound amino acids, glycogen-bound glucose, and RNA-bound ribose. Fluxes are then estimated using a software for 13C-MFA such as Metran, followed by comprehensive statistical analysis to determine the goodness-of-fit and calculate confidence intervals of fluxes. The presented protocol can be completed in 4 days and quantifies metabolic fluxes with a standard deviation of 2% or better, a significant improvement over previous implementations. The presented protocol is exemplified using an E. coli ΔtpiA case study with full supporting data, providing a hands-on opportunity to step through a complex troubleshooting scenario. While applications to prokaryotic microbial systems are emphasized, this protocol is easily adjusted for eukaryotic organisms.
  • Synthetic methylotrophy: Strategies to assimilate methanol for growth and chemicals production.
    Antoniewicz MR.
    Curr Opin Biotechnol, 59: 165-174, 2019 [Show Abstract]
    Methanol is an attractive and broadly available substrate for large-scale bioproduction of fuels and chemicals. It contains more energy and electrons per carbon than carbohydrates and can be cheaply produced from natural gas. Synthetic methylotrophy refers to the development of non-native methylotrophs such as Escherichia coli and Corynebacterium glutamicum to utilize methanol as a carbon source. Here, we discuss recent advances in engineering these industrial hosts to assimilate methanol for growth and chemicals production through the introduction of the ribulose monophosphate (RuMP) cycle. In addition, we present novel strategies based on flux coupling and adaptive laboratory evolution to engineer new strains that can grow exclusively on methanol.
  • An unconventional uptake rate objective function approach enhances applicability of genome-scale models for mammalian cells.
    Chen Y, McConnell BO, Gayatri Dhara V, Mukesh Naik H, Li CT, Antoniewicz MR, Betenbaugh MJ.
    NPJ Syst Biol Appl, 5: 25, 2019 [Show Abstract]
    Constraint-based modeling has been applied to analyze metabolism of numerous organisms via flux balance analysis and genome-scale metabolic models, including mammalian cells such as the Chinese hamster ovary (CHO) cells—the principal cell factory platform for therapeutic protein production. Unfortunately, the application of genome-scale model methodologies using the conventional biomass objective function is challenged by the presence of overly-restrictive constraints, including essential amino acid exchange fluxes that can lead to improper predictions of growth rates and intracellular flux distributions. In this study, these constraints are found to be reliably predicted by an “essential nutrient minimization” approach. After modifying these constraints with the predicted minimal uptake values, a series of unconventional objective functions are applied to minimize each individual non-essential nutrient uptake rate, revealing useful insights about metabolic exchange rates and flows across different cell lines and culture conditions. This unconventional uptake-rate objective functions (UOFs) approach is able to distinguish metabolic differences between three distinct CHO cell lines (CHO-K1, -DG44, and -S) not directly observed using the conventional biomass growth maximization solutions. Further, a comparison of model predictions with experimental data from literature correctly correlates with the specific CHO-DG44-derived cell line used experimentally, and the corresponding dual prices provide fruitful information concerning coupling relationships between nutrients. The UOFs approach is likely to be particularly suited for mammalian cells and other complex organisms which contain multiple distinct essential nutrient inputs, and may offer enhanced applicability for characterizing cell metabolism and physiology as well as media optimization and biomanufacturing control.
  • Tandem mass spectrometry for 13C metabolic flux analysis: Methods and algorithms based on EMU framework.
    Choi J, Antoniewicz MR.
    Front Microbiol, 10: 31, 2019 [Show Abstract]
    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 mass spectrometry 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 methods paper, we present recent advances in algorithms for incorporating tandem MS measurements into existing 13C-MFA approaches that are based on the elementary metabolite units (EMU). Specifically, EMU-based algorithms are presented for simulating tandem MS data in biochemical network models and for correcting tandem MS data for natural isotope abundances.
  • Deletion of four genes in Escherichia coli enables preferential consumption of xylose and secretion of glucose.
    Diaz CAC, Bennett RK, Papoutsakis ET, Antoniewicz MR.
    Metab Eng, 52: 168-177, 2019 [Show Abstract]
    Overcoming carbon catabolite repression presents a significant challenge, largely due to the complex regulatory networks governing substrate catabolism, even in microbial cells. In this work, we have engineered an E. coli strain, which we have named X2G, that not only exhibits a reversed substrate preference for xylose over glucose, but also demonstrates an unusual ability to produce significant amounts of glucose. We obtained this non-intuitive phenotype by deleting four genes in upper central metabolism: ptsI, glk, pfkA, and zwf, which respectively encode Enzyme I of the phosphotransferase system, glucokinase, the dominant isozyme of phosphofructokinase, and glucose 6-phosphate dehydrogenase. The deletion of ptsI and glk blocks glucose uptake in E. coli, while the deletion of pfkA and zwf prevents the reassimilation of carbons through glycolysis and the oxidative pentose phosphate pathway, respectively. Our strain X2G is capable of converting 34% of the carbon it takes up as xylose into exported glucose. This corresponds to a glucose production rate of 1.4 ± 0.3 mmol/gDW/h at a specific growth rate of 0.25 ± 0.03 h-1, or about 1.8 ± 0.1 mM of glucose accumulated for every unit increase in OD600. Despite a 22% decrease in xylose uptake rate, a 33% decrease in biomass yield, and a 52% decrease in acetate production rate relative to the wild-type, the intracellular flux profile and cofactor allocation of X2G remain largely unperturbed, as elucidated through 13C-metabolic flux analysis. Further quantification of the pool sizes of key intracellular metabolites revealed that glucose secretion by X2G is likely driven by the substantial accumulation of intracellular glucose 6-phosphate, fructose 6-phosphate, glucose and fructose at levels greater than 20x of that in wild-type E. coli. Combined, our results shed light on the flexibility of central metabolism, and the opportunities this affords for producing value-added pentose- and hexose-derived products from lignocellulosic feedstocks.


  • How adaptive evolution reshapes metabolism to improve fitness: recent advances and future outlook.
    Long CP, Antoniewicz MR.
    Curr Opin Chem Eng, 22: 209-215, 2018 [Show Abstract]
    Adaptive laboratory evolution (ALE) has emerged as a powerful tool in basic microbial research and strain development. In the context of metabolic science and engineering, it has been applied to study gene knockout responses, expand substrate ranges, improve tolerance to process conditions, and to improve productivity via designed growth coupling. In recent years, advancements in ALE methods and systems biology measurement technologies, particularly genome sequencing and 13C metabolic flux analysis (13C-MFA), have enabled detailed study of the mechanisms and dynamics of evolving metabolism. In this review, we discuss a range of studies that have applied flux analysis to adaptively evolved strains, as well as modeling frameworks developed to predict and interpret evolved fluxes. These efforts link mutations to fitness-enhanced phenotypes, identify bottlenecks and approaches to resolve them, and address systems concepts such as optimality.
  • Metabolism in dense microbial colonies: 13C metabolic flux analysis of E. coli grown on agar identifies two distinct cell populations with acetate cross-feeding.
    Wolfsberg E, Long CP, Antoniewicz MR.
    Metab Eng, 49: 242-247, 2018 [Show Abstract]
    In this study, we have investigated for the first time the metabolism of E. coli grown on agar using 13C metabolic flux analysis (13C-MFA). To date, all 13C-MFA studies on microbes have been performed with cells grown in liquid culture. Here, we extend the scope of 13C-MFA to biological systems where cells are grown in dense microbial colonies. First, we identified new optimal 13C tracers to quantify fluxes in systems where the acetate yield cannot be easily measured. We determined that three parallel labeling experiments with the tracers [1,2-13C]glucose, [1,6-13C]glucose, and [4,5,6-13C]glucose permit precise estimation of not only intracellular fluxes, but also of the amount of acetate produced from glucose. Parallel labeling experiments were then performed with wild-type E. coli and E. coli ackA-KO grown in liquid culture and on agar plates. Initial attempts to fit the labeling data from wild-type E. coli grown on agar did not produce a statistically acceptable fit. To resolve this issue, we employed the recently developed co-culture 13C-MFA approach, where two E. coli subpopulations were defined in the model that engaged in metabolite cross-feeding. The flux results identified two distinct E. coli cell populations, a dominant cell population (92% of cells) that metabolized glucose via conventional metabolic pathways and secreted a large amount of acetate (~40% of maximum theoretical yield), and a second smaller cell population (8% of cells) that consumed the secreted acetate without any glucose influx. These experimental results are in good agreement with recent theoretical simulations. Importantly, this study provides a solid foundation for future investigations of a wide range of problems involving microbial biofilms that are of great interest in biotechnology, ecology and medicine, where metabolite cross-feeding between cell populations is a core feature of the communities.
  • Hexokinase-2 depletion inhibits glycolysis and induces oxidative phosphorylation in hepatocellular carcinoma and sensitizes to metformin.
    DeWaal D, Nogueira V, Terry AR, Patra CP, Jeon SM, Guzman G, Au J, Long CP, Antoniewicz MR, Hay N.
    Nat Commun, 9:446, 2018 [Show Abstract]
    Hepatocellular carcinoma (HCC) cells are metabolically distinct from normal hepatocytes by expressing the high-affinity hexokinase (HK2) and suppressing glucokinase (GCK). This is exploited to selectively target HCC. Hepatic HK2 deletion inhibits tumor incidence in a mouse model of hepatocarcinogenesis. Silencing HK2 in human HCC cells inhibits tumorigenesis and increases cell death, which cannot be restored by GCK or mitochondrial binding deficient HK2. Upon HK2 silencing, glucose flux to pyruvate and lactate is inhibited, but TCA fluxes are maintained. Serine uptake and glycine secretion are elevated suggesting increased requirement for one-carbon contribution. Consistently, vulnerability to serine depletion increases. The decrease in glycolysis is coupled to elevated oxidative phosphorylation, which is diminished by metformin, further increasing cell death and inhibiting tumor growth. Neither HK2 silencing nor metformin alone inhibits mTORC1, but their combination inhibits mTORC1 in an AMPK-independent and REDD1-dependent mechanism. Finally, HK2 silencing synergizes with sorafenib to inhibit tumor growth.
  • A guide to 13C metabolic flux analysis for the cancer biologist.
    Antoniewicz MR.
    Exp Mol Med, 50(4):19, 2018 [Show Abstract]
    Cancer metabolism is significantly altered from normal cellular metabolism allowing cancer cells to adapt to changing microenvironments and maintain high rates of proliferation. In the past decade, stable-isotope tracing and network analysis have become powerful tools for uncovering metabolic pathways that are differentially activated in cancer cells. In particular, 13C metabolic flux analysis (13C-MFA) has emerged as the primary technique for quantifying intracellular fluxes in cancer cells. In this review, we provide a practical guide for investigators interested in getting started with 13C-MFA. We describe best practices in 13C-MFA, highlight potential pitfalls and alternative approaches, and conclude with new developments that can further enhance our understanding of cancer metabolism.
  • Dissecting the genetic and metabolic mechanisms of adaptation to the knockout of a major metabolic enzyme in Escherichia coli.
    Long CP, Gonzalez JE, Feist AM, Palsson BO, Antoniewicz MR.
    Proc Natl Acad Sci U S A, 115(1): 222-227, 2018 [Show Abstract]
    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 high-resolution 13C-metabolic flux analysis were performed on ten 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 PTS 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.
  • Methanol assimilation in Escherichia coli is improved by co-utilization of threonine and deletion of leucine-responsive regulatory protein.
    Gonzalez JE, Bennett RK, Papoutsakis ET, Antoniewicz MR.
    Metab Eng, 45: 67-74, 2018 [Show Abstract]
    Methane, the main component of natural gas, can be used to produce methanol which can be further converted to other valuable products. There is increasing interest in using biological systems for the production of fuels and chemicals from methanol, termed methylotrophy. In this work, we have examined methanol assimilation metabolism in a synthetic methylotrophic E. coli strain. Specifically, we applied 13C-tracers and evaluated 25 different co-substrates for methanol assimilation, including amino acids, sugars and organic acids. In particular, co-utilization of threonine significantly enhanced methylotrophy. Through our investigations, we proposed specific metabolic pathways that, when activated, correlated with increased methanol assimilation. These pathways are normally repressed by the leucine-responsive regulatory protein (lrp), a global regulator of metabolism associated with the feast-and-famine response in E. coli. By deleting lrp, we were able to further enhance the methylotrophic ability of our synthetic strain, as demonstrated through increased incorporation of 13C carbon from 13C-methanol into biomass.
  • Expression of heterologous non-oxidative pentose phosphate pathway from Bacillus methanolicus and phosphoglucose isomerase deletion improves methanol assimilation and metabolite production by a synthetic Escherichia coli methylotroph.
    Bennett RK, Gonzalez JE, Whitaker WB, Antoniewicz MR, Papoutsakis ET.
    Metab Eng, 45: 75-85, 2018 [Show Abstract]
    Synthetic methylotrophy aims to develop non-native methylotrophic microorganisms to utilize methane or methanol to produce chemicals and biofuels. We report two complimentary strategies to further engineer a previously engineered methylotrophic E. coli strain for improved methanol utilization. First, we demonstrate improved methanol assimilation in the presence of small amounts of yeast extract by expressing the non-oxidative pentose phosphate pathway (PPP) from Bacillus methanolicus. Second, we demonstrate improved co-utilization of methanol and glucose by deleting the phosphoglucose isomerase gene (pgi), which rerouted glucose carbon flux through the oxidative PPP. Both strategies led to significant improvements in methanol assimilation as determined by 13C-labeling in intracellular metabolites. Introduction of an acetone-formation pathway in the pgi-deficient methylotrophic E. coli strain led to improved methanol utilization and acetone titers during glucose fed-batch fermentation.


  • Metabolism of the fast-growing bacterium Vibrio natriegens elucidated by 13C metabolic flux analysis
    Long CP, Gonzalez JE, Cipolla RM, Antoniewicz MR.
    Metab Eng, 44: 191-197, 2017 [Show Abstract]
    Vibrio natriegens is a fast-growing, non-pathogenic bacterium that is being considered as the next-generation workhorse for the biotechnology industry. However, little is known about the metabolism of this organism which is limiting our ability to apply rational metabolic engineering strategies. To address this critical gap in current knowledge, here we have performed a comprehensive analysis of V. natriegens metabolism. We constructed a detailed model of V. natriegens core metabolism, measured the biomass composition, and performed high-resolution 13C metabolic flux analysis (13C-MFA) to estimate intracellular fluxes using parallel labeling experiments with the optimal tracers [1,2-13C]glucose and [1,6-13C]glucose. During exponential growth in glucose minimal medium, V. natriegens had a growth rate of 1.70 1/h (doubling time of 24 min) and a glucose uptake rate of 3.90 g/g/h, which is more than two 2-fold faster than E. coli, although slower than the fast-growing thermophile Geobacillus LC300. 13C-MFA revealed that the core metabolism of V. natriegens is similar to that of E. coli, with the main difference being a 33% lower normalized flux through the oxidative pentose phosphate pathway. Quantitative analysis of co-factor balances provided additional insights into the energy and redox metabolism of V. natriegens. Taken together, the results presented in this study provide valuable new information about the physiology of V. natriegens and establish a solid foundation for future metabolic engineering efforts with this promising microorganism.

  • 13C metabolic flux analysis of three divergent extremely thermophilic bacteria: Geobacillus sp. LC300, Thermus thermophilus HB8, and Rhodothermus marinus DSM 4252
    Cordova LT, Cipolla RM, Swarup A, Long CP, Antoniewicz MR.
    Metab Eng, 44: 182-190, 2017 [Show Abstract]
    Thermophilic organisms are being increasingly investigated and applied in metabolic engineering and biotechnology. The distinct metabolic and physiological characteristics of thermophiles, including broad substrate range and high uptake rates, coupled with recent advances in genetic tool development, present unique opportunities for strain engineering. However, poor understanding of the cellular physiology and metabolism of thermophiles has limited the application of systems biology and metabolic engineering tools to these organisms. To address this concern, we applied high resolution 13C metabolic flux analysis to quantify fluxes for three divergent extremely thermophilic bacteria from separate phyla: Geobacillus sp. LC300, Thermus thermophilus HB8, and Rhodothermus marinus DSM 4252. We performed 18 parallel labeling experiments, using all singly labeled glucose tracers for each strain, reconstructed and validated metabolic network models, measured biomass composition, and quantified precise metabolic fluxes for each organism. In the process, we resolved many uncertainties regarding gaps in pathway reconstructions and elucidated how these organisms maintain redox balance and generate energy. Overall, we found that the metabolisms of the three thermophiles were highly distinct, suggesting that adaptation to growth at high temperatures did not favor any particular set of metabolic pathways. All three strains relied heavily on glycolysis and TCA cycle to generate key cellular precursors and cofactors. None of the investigated organisms utilized the Entner-Doudoroff pathway and only one strain had an active oxidative pentose phosphate pathway. Taken together, the results from this study provide a solid foundation for future model building and engineering efforts with these and related thermophiles.

  • Predicting dynamic metabolic demands in the photosynthetic eukaryote Chlorella vulgaris
    Zuniga C, Levering J, Antoniewicz MR, Guarnieri MT, Betenbaugh MJ, Zengler K.
    Plant Physiol, 176: 450-462, 2017 [Show Abstract]
    Phototrophic organisms exhibit a highly dynamic proteome, adapting their biomass composition in response to diurnal light/dark cycles and nutrient availability. Here, we used experimentally determined biomass compositions over the course of growth to determine and constrain the biomass objective function (BOF) in a genome-scale metabolic model of Chlorella vulgaris UTEX 395 over time. Changes in the BOF, which encompasses all metabolites necessary to produce biomass, influence the state of the metabolic network thus directly affecting predictions. Simulations using dynamic BOFs predicted distinct proteome demands during heterotrophic or photoautotrophic growth. Model-driven analysis of extracellular nitrogen concentrations and predicted nitrogen uptake rates revealed an intracellular nitrogen pool, which contains 38% of the total nitrogen provided in the medium for photoautotrophic and 13% for heterotrophic growth. Agreement between flux and gene expression trends was determined by statistical comparison. Accordance between predicted fluxes trends and gene expression trends was found for 65% of multi-subunit enzymes and 75% of allosteric reactions. Reactions with the highest agreement between simulations and experimental data were associated with energy metabolism, terpenoid biosynthesis, fatty acids, nucleotides, and amino acids metabolism. Furthermore, predicted flux distributions at each time point were compared with gene expression data to gain new insights into intracellular compartmentalization, specifically for transporters. A total of 103 genes related to internal transport reactions were identified and added to the updated model of C. vulgaris, iCZ946, thus increasing our knowledgebase by 10% for this model green alga.

  • Fast growth phenotype of E. coli K-12 from adaptive laboratory evolution does not require intracellular flux rewiring
    Long CP, Gonzalez JE, Feist AM, Palsson BO, Antoniewicz MR.
    Metab Eng, 44: 100-107, 2017 [Show Abstract]
    Adaptive laboratory evolution (ALE) is a widely-used method for improving the fitness of microorganisms in selected environmental conditions. It has been applied previously to Escherichia coli K-12 MG1655 during aerobic exponential growth on glucose minimal media, a frequently used model organism and growth condition, to probe the limits of E. coli growth rate and gain insights into fast growth phenotypes. Previous studies have described up to 1.6-fold increases in growth rate following ALE, and have identified key causal genetic mutations and changes in transcriptional patterns. Here, we report for the first time intracellular metabolic fluxes for six such adaptively evolved strains, as determined by high-resolution 13C-metabolic flux analysis. Interestingly, we found that intracellular metabolic pathway usage changed very little following adaptive evolution. Instead, at the level of central carbon metabolism the faster growth was facilitated by proportional increases in glucose uptake and all intracellular rates. Of the six evolved strains studied here, only one strain showed a small degree of flux rewiring, and this was also the strain with unique genetic mutations. A comparison of fluxes with two other wild-type (unevolved) E. coli strains, BW25113 and BL21, showed that inter-strain differences are greater than differences between the parental and evolved strains. Principal component analysis highlighted that nearly all flux differences (95%) between the nine strains were captured by only two principal components. The distance between measured and flux balance analysis predicted fluxes was also investigated. It suggested a relatively wide range of similar stoichiometric optima, which opens new questions about the path-dependency of adaptive evolution.

  • Enzyme I facilitates reverse flux from pyruvate to phosphoenolpyruvate in Escherichia coli.
    Long CP, Au J, Sandoval NR, Gebreselassie NA, Antoniewicz MR.
    Nat Commun, 8:14316, 2017 [Show Abstract]
    The bacterial phosphoenolpyruvate-carbohydrate phosphotransferase system (PTS) consists of cascading phosphotransferases that couple the simultaneous import and phosphorylation of a variety of sugars to the glycolytic conversion of phosphoenolpyruvate (PEP) to pyruvate. As the primary route of glucose uptake in E. coli, the PTS plays a key role in regulating central carbon metabolism and carbon catabolite repression, and is a frequent target of metabolic engineering interventions. Here we show that Enzyme I, the terminal phosphotransferase responsible for the conversion of PEP to pyruvate, is responsible for a significant in vivo flux in the reverse direction (pyruvate to PEP) during both gluconeogenic and glycolytic growth. We use 13C alanine tracers to quantify this back-flux in single and double knockouts of genes relating to PEP synthetase and PTS components. Our findings are relevant to metabolic engineering design and add to our understanding of gene-reaction connectivity in E. coli.

  • Tracing metabolism from lignocellulosic biomass and gaseous substrates to products with stable-isotopes.
    Gonzalez JE, Antoniewicz MR.
    Curr Opin Biotechnol, 43: 86-95, 2017 [Show Abstract]
    Engineered microbes offer a practical and sustainable alternative to traditional industrial approaches. To increase the economic feasibility of biological processes, microbial isolates are engineered to take up inexpensive feedstocks (including lignocellulosic biomass, syngas, methane, and carbon dioxide), and convert them into substrates of central metabolism and further into value-added products. To trace the metabolism of these feedstocks into products, isotopic tracers are applied together with isotopomer analysis techniques such as 13C-metabolic flux analysis to provide a detailed picture of pathway utilization. Flux data is then integrated with kinetic models and constraint-based approaches to identify metabolic bottlenecks, propose novel metabolic engineering strategies, and improve process performance.

  • Comprehensive analysis of glucose and xylose metabolism in Escherichia coli under aerobic and anaerobic conditions by 13C metabolic flux analysis.
    Gonzalez JE, Long CP, Antoniewicz MR.
    Metab Eng, 39: 9-18, 2017 [Show Abstract]
    Glucose and xylose are the two most abundant sugars derived from the breakdown of lignocellulosic biomass. While aerobic glucose metabolism is relatively well understood in E. coli, until now there have been only a handful of studies focused on anaerobic glucose metabolism and no 13C-flux studies on xylose metabolism. In the absence of experimentally validated flux maps, constraint-based approaches such as MOMA and RELATCH cannot be used to guide new metabolic engineering designs. In this work, we have addressed this critical gap in current understanding by performing comprehensive characterizations of glucose and xylose metabolism under aerobic and anaerobic conditions, using recent state-of-the-art techniques in 13C metabolic flux analysis (13C-MFA). Specifically, we quantified precise metabolic fluxes for each condition by performing parallel labeling experiments and analyzing the data through integrated 13C-MFA using the optimal tracers [1,2-13C]glucose, [1,6-13C]glucose, [1,2-13C]xylose and [5-13C]xylose. We also quantified changes in biomass composition and confirmed turnover of macromolecules by applying [U-13C]glucose and [U-13C]xylose tracers. We demonstrate that under anaerobic growth conditions there is significant turnover of lipids and that a significant portion of CO2 originates from biomass turnover. Using knockout strains, we also demonstrate that β-oxidation is critical for anaerobic growth on xylose. Quantitative analysis of co-factor balances (NADH/FADH2, NADPH, and ATP) for different growth conditions provided new insights regarding the interplay of energy and redox metabolism and the impact on E. coli cell physiology.

  • Engineering the biological conversion of methanol to specialty chemicals in Escherichia coli.
    Whitaker WB, Jones JA, Bennett K, Gonzalez JE, Vernacchio VR, Collins SM, Palmer MA, Schmidt S, Antoniewicz MR, Koffas MA, Papoutsakis ET.
    Metab Eng, 39:49-59, 2017 [Show Abstract]
    Methanol is an attractive substrate for biological production of chemicals and fuels. Engineering methylotrophic Escherichia coli as a platform organism for converting methanol to metabolites is desirable. Prior efforts to engineer methylotrophic E. coli were limited by methanol dehydrogenases (Mdhs) with unfavorable enzyme kinetics. We engineered E. coli to utilize methanol using a superior NAD-dependent Mdh from Bacillus stearothermophilus and ribulose monophosphate (RuMP) pathway enzymes from B. methanolicus. Using 13C-labeling, we demonstrate this E. coli strain converts methanol into biomass components. For example, the key TCA cycle intermediates, succinate and malate, exhibit labeling up to 39%, while the lower glycolytic intermediate, 3-phosphoglycerate, up to 53%. Multiple carbons are labeled for each compound, demonstrating a cycling RuMP pathway for methanol assimilation to support growth. By incorporating the pathway to synthesize the flavanone naringenin, we demonstrate the first example of in vivo conversion of methanol into a specialty chemical in E. coli.


  • Comprehensive metabolic modeling of multiple 13C-isotopomer data sets to study metabolism in perfused working hearts.
    Crown SB, Kelleher JK, Rouf R, Muoio DM, Antoniewicz MR.
    Am J Physiol Heart Circ Physiol, 311(4): H881-H891, 2016 [Show Abstract]
    In many forms of cardiomyopathy, alterations in energy substrate metabolism play a key role in disease pathogenesis. Stable isotope tracing in rodent heart perfusion systems can be used to determine cardiac metabolic fluxes, namely those fluxes which contribute to pyruvate, the acetyl-CoA pool, and pyruvate anaplerosis which are critical to cardiac homeostasis. Methods have previously been developed to interrogate these fluxes using isotopomer enrichments of measured metabolites and algebraic equations to determine a pre-defined metabolic flux model. However, this approach is exquisitely sensitive to measurement error, thus precluding accurate flux parameter determination. In this study, we applied a novel mathematical approach to determine cardiac metabolic fluxes using 13C-metabolic flux analysis (13C-MFA) aided by multiple tracer experiments and integrated data analysis. Using 13C-MFA, we validated a metabolic network model to explain myocardial energy substrate metabolism. Four different 13C-labeled substrates were queried (i.e. glucose, lactate, pyruvate, and oleate) based on a previously published study. We integrated the analysis of the complete set of isotopomer data gathered from these mouse heart perfusion experiments into a single comprehensive network model which delineates substrate contributions to both pyruvate and acetyl-CoA pools at a greater resolution than that offered by traditional methods using algebraic equations. To our knowledge, this is the first rigorous application of 13C-MFA to interrogate data from multiple tracer experiments in the perfused heart. We anticipate that this approach can be used widely to study energy substrate metabolism in this and other similar biological systems.

  • CO2 fixation by anaerobic non-photosynthetic mixotrophy for improved carbon conversion.
    Jones SW, Fast AG, Carlson ED, Wiedel CA, Au J, Antoniewicz MR, Papoutsaks ET, Tracy BP.
    Nat Commun, 7:12800, 2016 [Show Abstract]
    Maximizing the conversion of biogenic carbon feedstocks into chemicals and fuels is essential for fermentation processes as feedstock costs and processing is commonly the greatest operating expense. Unfortunately, for most fermentations, over one-third of sugar carbon is lost to CO2 due to the decarboxylation of pyruvate to acetyl-CoA and limitations in the reducing power of the bio-feedstock. Here, we show that Anaerobic, Non-Photosynthetic mixotrophy, defined as the concurrent utilization of organic (e.g., sugars) and inorganic (e.g., CO2) substrates in a single organism, can overcome these constraints to increase product yields and reduce overall CO2 emissions. As a proof-of-concept, Clostridium ljungdahlii was engineered to produce acetone and achieved a mass yield 138% of the previous theoretical maximum using a high cell density continuous fermentation process. In addition, when enough reductant (i.e., H2) is provided, the fermentation emits no CO2. Finally, we show that mixotrophy is a general trait among acetogens.

  • 13C metabolic flux analysis of microbial and mammalian systems is enhanced with GC-MS measurements of glycogen and RNA labeling.
    Long CP, Au J, Gonzalez JE, Antoniewicz MR.
    Metab Eng, 38: 65–72, 2016 [Show Abstract]
    13C Metabolic flux analysis (13C-MFA) is a widely used tool for quantitative analysis of microbial and mammalian metabolism. Until now, 13C-MFA was based mainly on measurements of isotopic labeling of amino acids derived from hydrolyzed biomass proteins and isotopic labeling of extracted intracellular metabolites. Here, we demonstrate that isotopic labeling of glycogen and RNA, measured with gas chromatography-mass spectrometry (GC-MS), provides valuable additional information for 13C-MFA. Specifically, we demonstrate that isotopic labeling of glucose moiety of glycogen and ribose moiety of RNA greatly enhances resolution of metabolic fluxes in the upper part of metabolism; importantly, these measurements allow precise quantification of net and exchange fluxes in the pentose phosphate pathway. To demonstrate the practical importance of these measurements for 13C-MFA, we have used E. coli as a model microbial system and CHO cells as a model mammalian system. Additionally, we have applied this approach to determine metabolic fluxes of glucose and xylose co-utilization in the E. coli ΔptsG mutant. The convenience of measuring glycogen and RNA, which are stable and abundant in microbial and mammalian cells, offers the following key advantages: reduced sample size, no quenching required, no extractions required, and GC-MS can be used instead of more costly LC-MS/MS techniques. Overall, the presented approach for 13C-MFA will have widespread applicability in metabolic engineering and biomedical research.

  • Genome-scale metabolic model for the green alga Chlorella vulgaris UTEX 395 accurately predicts phenotypes under autotrophic, heterotrophic, and mixotrophic growth conditions.
    Zuniga C, Li CT, Huelsman T, Levering J, Zielinski DC, McConnell BO, Long CP, Knoshaug EP, Guarnieri MT, Antoniewicz MR, Betenbaugh MJ, Zengler K.
    Plant Physiol, 172(1): 589-602, 2016 [Show Abstract]
    The green microalgae 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.

  • A key role for transketolase-like 1 in tumor metabolic reprogramming.
    Diaz-Moralli S, Aguilar E, Marin S, Coy JF, Dewerchin M, Antoniewicz MR, Meca-Cortes O, Notebaert L, Ghesquiere B, Eelen G, Thomson TM, Carmeliet P, Cascante M.
    Oncotarget, 10429, 2016 [Show Abstract]
    Metabolic reprogramming, a crucial cancer hallmark, shifts metabolic pathways such as glycolysis, tricarboxylic acid cycle or lipogenesis, to enable the growth characteristics of cancer cells. Here, we provide evidence that transketolase-like 1 (TKTL1) orchestrates aerobic glycolysis, fatty acid and nucleic acid synthesis, glutamine metabolism, protection against oxidative stress and cell proliferation. Furthermore, silencing of TKTL1 reduced the levels of sphingolipids such as lactosylceramide (a sphingolipid regulating cell survival, proliferation and angiogenesis) and phosphatidylinositol (which activates PI3K/Akt/mTOR signaling). Thus, in addition to its well-known roles in glucose and amino acid metabolism, TKTL1 also regulates lipid metabolism. In conclusion, our study provides unprecedented evidence that TKTL1 plays central roles in major metabolic processes subject to reprogramming in cancer cells and thus identifies TKTL1 as a promising target for new anti-cancer therapies.

  • Optimal tracers for parallel labeling experiments and 13C metabolic flux analysis: A new precision and synergy scoring system.
    Crown SB, Long CP, Antoniewicz MR.
    Metab Eng, 38: 10-18, 2016 [Show Abstract]
    13C-Metabolic flux analysis (13C-MFA) is a widely used approach in metabolic engineering for quantifying intracellular metabolic fluxes. The precision of fluxes determined by 13C-MFA depends largely on the choice of isotopic tracers and the specific set of labeling measurements. A recent advance in the field is the use of parallel labeling experiments for improved flux precision and accuracy. However, as of today, no systemic methods exist for identifying optimal tracers for parallel labeling experiments. In this contribution, we have addressed this problem by introducing a new scoring system and evaluating thousands of different isotopic tracer schemes. Based on this extensive analysis we have identified optimal tracers for 13C-MFA. The best single tracers were doubly 13C-labeled glucose tracers, including [1,6-13C]glucose, [5,6-13C]glucose and [1,2-13C]glucose, which consistently produced the highest flux precision independent of the metabolic flux map (here, 100 random flux maps were evaluated). Moreover, we demonstrate that pure glucose tracers perform better overall than mixtures of glucose tracers. For parallel labeling experiments the optimal isotopic tracers were [1,6-13C]glucose and [1,2-13C]glucose. Combined analysis of [1,6-13C]glucose and [1,2-13C]glucose labeling data improved the flux precision score by nearly 20-fold compared to widely use tracer mixture 80% [1-13C]glucose + 20% [U-13C]glucose.

  • Characterization of physiological responses to 22 gene knockouts in Escherichia coli central carbon metabolism.
    Long CP, Gonzalez JE, Sandoval NR, Antoniewicz MR.
    Metab Eng, 37: 102–113, 2016 [Show Abstract]
    Understanding the impact of gene knockouts on cellular physiology, and metabolism in particular, is centrally important to quantitative systems biology and metabolic engineering. Here, we present a comprehensive physiological characterization of wild-type Escherichia coli and 22 knockouts of enzymes in the upper part of central carbon metabolism, including the PTS system, glycolysis, pentose phosphate pathway and Entner–Doudoroff pathway. Our results reveal significant metabolic changes that are affected by specific gene knockouts. Analysis of collective trends and correlations in the data using principal component analysis (PCA) provide new, and sometimes surprising, insights into E. coli physiology. Additionally, by comparing the data-to-model predictions from constraint-based approaches such as FBA, MOMA and RELATCH we demonstrate the important role of less well-understood kinetic and regulatory effects in central carbon metabolism.

  • Evidence for transketolase-like TKTL1 flux in CHO cells based on parallel labeling experiments and 13C-metabolic flux analysis.
    Ahn WS, Crown SB, Antoniewicz MR.
    Metab Eng, 37: 72–78, 2016 [Show Abstract]
    The pentose phosphate pathway (PPP) is a fundamental component of cellular metabolism. It provides precursors for the biosynthesis of nucleotides and contributes to the production of reducing power in the form of NADPH. It has been hypothesized that mammalian cells may contain a hidden reaction in PPP catalyzed by transketolase-like protein 1 (TKTL1) that is closely related to the classical transketolase enzyme; however, until now there has been no direct experimental evidence for this reaction. In this work, we have applied state-of-the-art techniques in 13C metabolic flux analysis (13C-MFA) based on parallel labeling experiments and integrated flux fitting to estimate the TKTL1 flux in CHO cells. We identified a set of three parallel labeling experiments with [1-13C]glucose+[4,5,6-13C]glucose, [2-13C]glucose+[4,5,6-13C]glucose, and [3-13C]glucose+[4,5,6-13C]glucose and developed a new method to measure 13C-labeling of fructose 6-phosphate by GC-MS that allows intuitive interpretation of mass isotopomer distributions to determine key fluxes in the model, including glycolysis, oxidative PPP, non-oxidative PPP, and the TKTL1 flux. Using these tracers we detected a significant TKTL1 flux in CHO cells at the stationary phase. The flux results suggest that the main function of oxidative PPP in CHO cells at the stationary phase is to fuel the TKTL1 reaction. Overall, this study demonstrates for the first time that carbon atoms can be lost in the PPP, by means other than the oxidative PPP, and that this loss of carbon atoms is consistent with the hypothesized TKTL1 reaction in mammalian cells.

  • Co-utilization of glucose and xylose by evolved Thermus thermophilus LC113 strain elucidated by 13C metabolic flux analysis and whole genome sequencing.
    Cordova LT, Lu J, Cipolla RM, Sandoval NR, Long CP, Antoniewicz MR.
    Metab Eng, 37: 63–71, 2016 [Show Abstract]
    We evolved Thermus thermophilus to efficiently co-utilize glucose and xylose, the two most abundant sugars in lignocellulosic biomass, at high temperatures without carbon catabolite repression. To generate the strain, T. thermophilus HB8 was first evolved on glucose to improve its growth characteristics, followed by evolution on xylose. The resulting strain, T. thermophilus LC113, was characterized in growth studies, by whole genome sequencing, and 13C-metabolic flux analysis (13C-MFA) with [1,6-13C]glucose, [5-13C]xylose, and [1,6-13C]glucose + [5-13C]xylose as isotopic tracers. Compared to the starting strain, the evolved strain had an increased growth rate (~ 2-fold), increased biomass yield, increased tolerance to high temperatures up to 90 oC, and gained the ability to grow on xylose in minimal medium. At the optimal growth temperature of 81 oC, the maximum growth rate on glucose and xylose was 0.44 and 0.46 h-1, respectively. In medium containing glucose and xylose the strain efficiently co-utilized the two sugars. 13C-MFA results provided insights into the metabolism of T. thermophilus LC113 that allows efficient co-utilization of glucose and xylose. Specifically, 13C-MFA revealed that metabolic fluxes in the upper part of metabolism adjust flexibly to sugar availability, while fluxes in the lower part of metabolism remain relatively constant. Whole genome sequence analysis revealed two large structural changes that can help explain the physiology of the evolved strain: a duplication of a chromosome region that contains many sugar transporters, and a 5x multiplication of a region on the pVV8 plasmid that contains xylose isomerase and xylulokinase genes, the first two enzymes of xylose catabolism. Taken together, 13C-MFA and genome sequence analysis provided complementary insights into the physiology of the evolved strain.

  • Measuring the composition and stable-isotope labeling of algal biomass carbohydrates by gas chromatography/mass spectrometry.
    McConnell BO, Antoniewicz MR.
    Anal Chem, 88(9): 4624–4628, 2016 [Show Abstract]
    We have developed a method to measure carbohydrate composition and stable-isotope labeling in algal biomass using gas chromatography/mass spectrometry (GC/MS). The method consists of two-stage hydrochloric acid hydrolysis, followed by chemical derivatization of the released monomer sugars and quantification by GC/MS. Fully 13C-labeled sugars are used as internal standards for composition analysis. This convenient, reliable, and accurate single-platform workflow offers advantages over existing methods and opens new opportunities to study carbohydrate metabolism of algae under autotrophic, mixotrophic and heterotrophic conditions using metabolic flux analysis and isotopic tracers such as 2H2O and 13C-glucose.

  • Evolution of E. coli on [U-13C]glucose reveals a negligible isotopic influence on metabolism and physiology.
    Sandberg TE, Long CP, Gonzalez JE, Feist AM, Antoniewicz MR, Palsson BO.
    PLoS One, 11(3):e0151130, 2016 [Show Abstract]
    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.

  • Heterotrophic bacteria from an extremely phosphate-poor lake have conditionally reduced phosphorus demand and utilize diverse sources of phosphorus.
    Yao M, Elling FJ, Jones CA, Nomosatryo S, Long CP, Crowe SA, Antoniewicz MR, Hinrichs KU, Maresca JA.
    Environ Microbiol, 18(2): 656–667, 2016 [Show Abstract]
    Heterotrophic Proteo- and Actinobacteria were isolated from Lake Matano, Indonesia, a stratified, ferruginous, ultra-oligotrophic lake with phosphate concentrations below 50 nM. Here, we describe the growth of eight strains of heterotrophic bacteria on a variety of soluble and insoluble sources of phosphorus. When transferred to medium without added phosphorus (P), the isolates grow more slowly, their RNA content falls to as low as 1% of cellular dry weight, and 86-100% of the membrane lipids are replaced with amino- or glycolipids. Similar changes in lipid composition have been observed in marine photoautotrophs and soil heterotrophs, and similar flexibility in phosphorus sources has been demonstrated in marine and soil-dwelling heterotrophs. Our results demonstrate that heterotrophs isolated from this unusual environment alter their macromolecular composition, which allows the organisms to grow efficiently even in their extremely phosphorus-limited environment.

  • 13C Metabolic flux analysis of the extremely thermophilic, fast growing, xylose-utilizing Geobacillus strain LC300.
    Cordova LT, Antoniewicz MR.
    Metab Eng, 33: 148-157, 2016 [Show Abstract]
    Thermophiles are increasingly used as versatile hosts in the biotechnology industry. One of the key advantages of thermophiles is the potential to achieve high rates of feedstock conversion at elevated temperatures. The recently isolated Geobacillus strain LC300 grows extremely fast on xylose, with a doubling time of less than 30 minutes. In the accompanying paper, the genome of Geobacillus LC300 was sequenced and annotated. In this work, we have experimentally validated the metabolic network model using parallel 13C-labeling experiments and applied 13C-metabolic flux analysis to quantify precise metabolic fluxes. Specifically, the complete set of singly labeled xylose tracers, [1-13C], [2-13C], [3-13C], [4-13C], and [5-13C]xylose, was used for the first time. Isotopic labeling of biomass amino acids was measured by gas chromatography mass spectrometry (GC-MS). Isotopic labeling of carbon dioxide in the off-gas was also measured by an on-line mass spectrometer. The 13C-labeling data was then rigorously integrated for flux elucidation using the COMPLETE-MFA approach. The results provided important new insights into the metabolism of Geobacillus LC300, its efficient xylose utilization pathways, and the balance between carbon, redox and energy fluxes. The pentose phosphate pathway, glycolysis and TCA cycle were found to be highly active in Geobacillus LC300. The oxidative pentose phosphate pathway was also active and contributed significantly to NADPH production. No transhydrogenase activity was detected. Results from this work provide a solid foundation for future studies of this strain and its metabolic engineering and biotechnological applications.


  • Catabolism of branched chain amino acids contributes significantly to synthesis of odd-chain and even-chain fatty acids in 3T3-L1 adipocytes.
    Crown SB, Marze N, Antoniewicz MR.
    PLoS One, 10(12): e0145850, 2015 [Show Abstract]
    The branched chain amino acids (BCAA) valine, leucine and isoleucine have been implicated in a number of diseases including obesity, insulin resistance, and type 2 diabetes mellitus, although the mechanisms are still poorly understood. Adipose tissue plays an important role in BCAA homeostasis by actively metabolizing circulating BCAA. In this work, we have investigated the link between BCAA catabolism and fatty acid synthesis in 3T3-L1 adipocytes using parallel 13C-labeling experiments, mass spectrometry and model-based isotopomer data analysis. Specifically, we performed parallel labeling experiments with four fully 13C-labeled tracers, [U-13C]valine, [U-13C]leucine, [U-13C]isoleucine and [U-13C]glutamine. We measured mass isotopomer distributions of fatty acids and intracellular metabolites by GC-MS and analyzed the data using the isotopomer spectral analysis (ISA) framework. We demonstrate that 3T3-L1 adipocytes accumulate significant amounts of even chain length (C14:0, C16:0 and C18:0) and odd chain length (C15:0 and C17:0) fatty acids under standard cell culture conditions. Using a novel GC-MS method, we demonstrate that propionyl-CoA acts as the primer on fatty acid synthase for the production of odd chain fatty acids. BCAA contributed significantly to the production of all fatty acids. Leucine and isoleucine contributed at least 25% to lipogenic acetyl-CoA pool, and valine and isoleucine contributed 100% to lipogenic propionyl-CoA pool. Our results further suggest that low activity of methylmalonyl-CoA mutase and mass action kinetics of propionyl-CoA on fatty acid synthase result in high rates of odd chain fatty acid synthesis in 3T3-L1 cells. Overall, this work provides important new insights into the connection between BCAA catabolism and fatty acid synthesis in adipocytes and underscores the high capacity of adipocytes for metabolizing BCAA.

  • Complete genome sequence, metabolic model construction and phenotypic characterization of Geobacillus LC300, an extremely thermophilic, fast growing, xylose-utilizing bacterium.
    Cordova LT, Long CP, Venkataramanan KP, Antoniewicz MR.
    Metab Eng, 32: 74-81, 2015 [Show Abstract]
    We have isolated a new extremely thermophilic fast-growing Geobacillus strain that can efficiently utilize xylose, glucose, mannose and galactose for cell growth. When grown aerobically at 72 oC, Geobacillus LC300 has a growth rate of 2.15 h-1 on glucose and 1.52 h-1 on xylose (doubling time less than 30 minutes). The corresponding specific glucose and xylose utilization rates are 5.55 g/g/h and 5.24 g/g/h, respectively. As such, Geobacillus LC300 grows 3-times faster than E. coli on glucose and xylose, and has a specific xylose utilization rate that is 3-times higher than the best metabolically engineered organism to date. To gain more insight into the metabolism of Geobacillus LC300 its genome was sequenced using PacBio’s RS II single-molecule real-time (SMRT) sequencing platform and annotated using the RAST server. Based on the genome annotation and the measured biomass composition a core metabolic network model was constructed. To further demonstrate the biotechnological potential of this organism, Geobacillus LC300 was grown to high cell-densities in a fed-batch culture, where cells maintained a high xylose utilization rate under low dissolved oxygen concentrations. All of these characteristics make Geobacillus LC300 an attractive host for future metabolic engineering and biotechnology applications.

  • Parallel labeling experiments for pathway elucidation and 13C metabolic flux analysis.
    Antoniewicz MR.
    Curr Opin Biotechnol, 36: 91-97, 2015 [Show Abstract]
    Metabolic pathway models provide the foundation for quantitative studies of cellular physiology through the measurement of intracellular metabolic fluxes. For model organisms metabolic models are well established, with many manually curated genome-scale model reconstructions, gene knockout studies and stable-isotope tracing studies. However, for non-model organisms a similar level of knowledge is often lacking. Compartmentation of cellular metabolism in eukaryotic systems also presents significant challenges for quantitative 13C-metabolic flux analysis (13C-MFA). Recently, innovative 13C-MFA approaches have been developed based on parallel labeling experiments, the use of multiple isotopic tracers, and integrated data analysis that allow more rigorous validation of pathway models and improved quantification of metabolic fluxes. Applications of these approaches open new research directions in metabolic engineering, biotechnology and medicine.

  • 13C-Metabolic flux analysis of co-cultures: A novel approach.
    Gebreselassie NA, Antoniewicz MR.
    Metab Eng, 31: 132-139, 2015 [Show Abstract]
    In this work, we present a novel approach for performing 13C metabolic flux analysis (13C-MFA) of co-culture systems. We demonstrate for the first time that it is possible to determine metabolic flux distributions in multiple species simultaneously without the need for physical separation of cells or proteins, or overexpression of species-specific products. Instead, metabolic fluxes for each species in a co-culture are estimated directly from isotopic labeling of total biomass obtained using conventional mass spectrometry approaches such as GC-MS. In addition to determining metabolic fluxes, this approach estimates the relative population size of each species in a mixed culture and inter-species metabolite exchange. As such, it enables detailed studies of microbial communities including species dynamics and interactions between community members. The methodology is experimentally validated here using a co-culture of two E. coli knockout strains. Taken together, this work greatly extends the scope of 13C-MFA to a large number of multi-cellular systems that are of significant importance in biotechnology and medicine.

  • A roadmap for interpreting 13C metabolite labeling patterns from cells.
    Buescher JM, Antoniewicz MR, …, Fendt SM.
    Curr Opin Biotechnol, 34: 189-201, 2015 [Show Abstract]
    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.

  • Methods and advances in metabolic flux analysis: A mini-review.
    Antoniewicz MR.
    J Ind Microbiol Biotechnol, 42(3): 317-325, 2015 [Show Abstract]
    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) as well as 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 (13C-MFA), isotopic non-stationary 13C metabolic flux analysis (13C-NMFA), dynamic metabolic flux analysis (DMFA), and 13C dynamic metabolic flux analysis (13C-DMFA). For each method, we discuss key advantages and limitations and we conclude by highlighting important recent advances in flux analysis approaches.

  • Integrated 13C-metabolic flux analysis of 14 parallel labeling experiments in Escherichia coli.
    Crown SB, Long CP, Antoniewicz MR.
    Metab Eng, 28: 151-158, 2015 [Show Abstract]
    The use of parallel labeling experiments for 13C metabolic flux analysis (13C-MFA) has emerged in recent years as the new gold standard in fluxomics. The methodology has been termed COMPLETE-MFA, short for complementary parallel labeling experiments technique for metabolic flux analysis. In this contribution, we have tested the limits of COMPLETE-MFA by demonstrating integrated analysis of 14 parallel labeling experiments with Escherichia coli. An effort on such a massive scale has never been attempted before. In addition to several widely used isotopic tracers such as [1,2-13C]glucose and mixtures of [1-13C]glucose and [U-13C]glucose, four novel tracers were applied in this study: [2,3-13C]glucose, [4,5,6-13C]glucose, [2,3,4,5,6-13C]glucose and a mixture of [1-13C]glucose and [4,5,6-13C]glucose. This allowed us for the first time to compare the performance of a large number of isotopic tracers. Overall, there was no single best tracer for the entire E. coli metabolic network model. Tracers that produced well-resolved fluxes in the upper part of metabolism (glycolysis and pentose phosphate pathways) showed poor performance for fluxes in the lower part of metabolism (TCA cycle and anaplerotic reactions), and vice versa. The best tracer for upper metabolism was 75% [1-13C]glucose + 25% [U-13C]glucose, while [4,5,6-13C]glucose and [5-13C]glucose both produced optimal flux resolution in the lower part of metabolism. COMPLETE-MFA improved both flux precision and flux observability, i.e. more independent fluxes were resolved with smaller confidence intervals, especially exchange fluxes. Overall, this study demonstrates that COMPLETE-MFA is a powerful approach for improving flux measurements and that this methodology should be considered in future studies that require very high flux resolution.


  • Quantifying biomass composition by gas chromatography/mass spectrometry.
    Long CP, Antoniewicz MR.
    Anal Chem, 86(19): 9423-7, 2014 [Show Abstract]
    We developed a set of methods for the quantification of four major components of microbial biomass using gas chromatography-mass spectrometry (GC/MS). Specifically, methods are described to quantify amino acids, RNA, fatty acids, and glycogen, which comprise an estimated 88% of the dry weight of Escherichia coli. Quantification is performed by isotope ratio analysis with fully 13C-labeled biomass as internal standard, which is generated by growing E. coli on [U-13C]glucose. This convenient, reliable, and accurate single-platform (GC/MS) workflow for measuring biomass composition offers significant advantages over existing methods. We demonstrate the consistency, accuracy, precision, and utility of this procedure by applying it to three metabolically unique E. coli strains. The presented methods will have widespread applicability in systems microbiology and bioengineering.

  • Parallel labeling experiments validate Clostridium acetobutylicum metabolic network model for 13C metabolic flux analysis.
    Au J, Choi J, Jones SW, Venkataramanan KP, Antoniewicz MR.
    Metab Eng, 26: 23-33, 2014 [Show Abstract]
    In this work, we provide new insights into the metabolism of Clostridium acetobutylicum ATCC 824 obtained using a systematic approach for quantifying fluxes based on parallel labeling experiments and 13C-metabolic flux analysis (13C-MFA). Here, cells were grown in parallel cultures with [1-13C]glucose and [U-13C]glucose as tracers and 13C-MFA was used to quantify intracellular metabolic fluxes. Several metabolic network models were compared: an initial model based on current knowledge, and extended network models that included additional reactions that improved the fits of experimental data. While the initial network model did not produce a statistically acceptable fit of 13C-labeling data, an extended network model with five additional reactions was able to fit all data with 292 redundant measurements. The model was subsequently trimmed to produce a minimal network model of C. acetobutylicum for 13C-MFA, which could still reproduce all of the experimental data. The flux results provided valuable new insights into the metabolism of C. acetobutylicum. First, we found that TCA cycle is effectively incomplete, as there was no measurable flux between α-ketoglutarate and succinyl-CoA, succinate and fumarate, and malate and oxaloacetate. Second, an active pathway was identified from pyruvate to fumarate via aspartate. Third, we found that isoleucine was produced exclusively through the citramalate synthase pathway in C. acetobutylicum and that CAC3174 was likely responsible for citramalate synthase activity. These model predictions were confirmed in several follow-up tracer experiments. The validated metabolic network model established in this study can be used in future investigations for unbiased 13C-flux measurements in C. acetobutylicum.

  • Metabolic network reconstruction, growth characterization and 13C-metabolic flux analysis of the extremophile Thermus thermophilus HB8.
    Swarup A, Lu J, DeWoody KC, Antoniewicz MR.
    Metab Eng, 24: 173-180, 2014 [Show Abstract]
    Thermus thermophilus is an extremely thermophilic bacterium with significant biotechnological potential. In this work, we characterized aerobic growth characteristics of T. thermophilus HB8 at temperatures between 50 and 85 °C, constructed a metabolic network model of its central carbon metabolism and validated the model using 13C-metabolic flux analysis (13C-MFA). First, cells were grown in batch cultures in custom constructed mini-bioreactors at different temperatures to determine optimal growth conditions. The optimal temperature for T. thermophilus grown on defined medium with glucose was 81 °C. The maximum growth rate was 0.25 h-1. Between 50 and 81 °C the growth rate increased by 7-fold and the temperature dependence was described well by an Arrhenius model with an activation energy of 47 kJ/mol. Next, we performed a 13C-labeling experiment with [1,2-13C]glucose as the tracer and calculated intracellular metabolic fluxes using 13C-MFA. The results provided support for the constructed network model and highlighted several interesting characteristics of T. thermophilus metabolism. We found that T. thermophilus largely uses glycolysis and TCA cycle to produce biosynthetic precursors, ATP and reducing equivalents needed for cells growth. Consistent with its proposed metabolic network model, we did not detect any oxidative pentose phosphate pathway flux or Entner-Doudoroff pathway activity. The biomass precursors erythrose-4-phosphate and ribose-5-phosphate were produced via the non-oxidative pentose phosphate pathway, and largely via transketolase, with little contribution from transaldolase. The high biomass yield on glucose that was measured experimentally was also confirmed independently by 13C-MFA. The results presented here provide a solid foundation for future studies of T. thermophilus and its metabolic engineering applications.

  • Metabolic flux analysis of Escherichia coli knockouts: lessons from the Keio collection and future outlook.
    Long CP, Antoniewicz MR.
    Curr Opin Biotechnol, 28: 127-133, 2014 [Show Abstract]
    Cellular metabolic and regulatory systems are of fundamental interest to biologists and engineers. Incomplete understanding of these complex systems remains an obstacle to progress in biotechnology and metabolic engineering. An established method for obtaining new information on network structure, regulation and dynamics is to study the cellular system following a perturbation such as a genetic knockout. The Keio collection of all viable E. coli single-gene knockouts is facilitating a systematic investigation of the regulation and metabolism of E. coli. Of all omics measurements available, the metabolic flux profile (the fluxome) provides the most direct and relevant representation of the cellular phenotype. Recent advances in 13C-metabolic flux analysis are now permitting highly precise and accurate flux measurements for investigating cellular systems and guiding metabolic engineering efforts.

  • Central metabolic responses to the overproduction of fatty acids in Escherichia coli based on 13C-metabolic flux analysis.
    He L, Xiao Y, Gebreselassie N, Zhang F, Antoniewicz MR, Tang YJ, Peng L.
    Biotechnol Bioeng, 111(3): 575-585, 2014 [Show Abstract]
    We engineered a fatty acid overproducing E. coli strain through overexpressing tesA (“pull”) and fadR (“push”) and knocking out fadE (“block”). This “pull-push-block” strategy yielded 0.17 gram of fatty acids (C12-C18) per gram of glucose (equivalent to 48% of the maximum theoretical yield) in batch cultures during the exponential growth phase under aerobic conditions. Metabolic fluxes were determined for the engineered E. coli and its control strain using tracer ([1,2-13C]glucose) experiments and 13C-metabolic flux analysis. Cofactor (NADPH) and energy (ATP) balances were also investigated for both strains based on estimated fluxes. Compared to the control strain, fatty acid overproduction led to significant metabolic responses in the central metabolism: 1) Acetic acid secretion flux decreased 10-fold; 2) Pentose phosphate pathway and Entner–Doudoroff pathway fluxes increased 1.5-fold and 2.0-fold, respectively; 3) Biomass synthesis flux was reduced 1.9-fold; 4) Anaplerotic phosphoenolpyruvate carboxylation flux decreased 1.7-fold; 5) Transhydrogenation flux converting NADH to NADPH increased by 1.7-fold. Real-time quantitative RT-PCR analysis revealed the engineered strain increased the transcription levels of pntA (encoding the membrane-bound transhydrogenase) by 2.1-fold and udhA (encoding the soluble transhydrogenase) by 1.4-fold, which is in agreement with the increased transhydrogenation flux. Cofactor and energy balances analyses showed that the fatty acid overproducing E. coli consumed significantly higher maintenance energy than the control strain. We discussed the guidelines to future strain development and process improvements for fatty acid production in E. coli.


  • COMPLETE-MFA: Complementary parallel labeling experiments technique for metabolic flux analysis.
    Leighty RW, Antoniewicz MR.
    Metab Eng, 20: 49-55, 2013 [Show Abstract]
    We have developed a novel approach for measuring highly accurate and precise metabolic fluxes in living cells, termed COMPLETE-MFA, short for complementary parallel labeling experiments technique for metabolic flux analysis. The COMPLETE-MFA method is based on combined analysis of multiple isotopic labeling experiments, where the synergy of using complementary tracers greatly improves the precision of estimated fluxes. In this work, we demonstrate the COMPLETE-MFA approach using all singly labeled glucose tracers, [1-13C], [2-13C], [3-13C], [4-13C], [5-13C], and [6-13C]glucose to determine precise metabolic fluxes for wild-type E. coli. Cells were grown in six parallel cultures on defined medium with glucose as the only carbon source. Mass isotopomers of biomass amino acids were measured by gas chromatography-mass spectrometry (GC-MS). The data from all six experiments were then fitted simultaneously to a single flux model to determine accurate intracellular fluxes. We obtained a statistically acceptable fit with more than 300 redundant measurements. The estimated flux map is the most precise flux result obtained thus far for E. coli cells. To our knowledge, this is the first time that six isotopic labeling experiments have been successfully integrated for high-resolution 13C-flux analysis.

  • Publishing 13C metabolic flux analysis studies: A review and future perspectives.
    Crown SB, Antoniewicz MR.
    Metab Eng, 20: 42-48, 2013 [Show Abstract]
    13C-Metabolic flux analysis (13C-MFA) is a powerful model-based analysis technique for determining intracellular metabolic fluxes in living cells. It has become a standard tool in many labs for quantifying cell physiology, e.g. in metabolic engineering, systems biology, biotechnology, and biomedical research. With the increasing number of 13C-MFA studies published each year, it is now ever more important to provide practical guidelines for performing and publishing 13C-MFA studies so that quality is not sacrificed as the number of publications increases. The main purpose of this paper is to provide an overview of good practices in 13C-MFA, which can eventually be used as minimum data standards for publishing 13C-MFA studies. The motivation for this work is two-fold: (1) currently, there is no general consensus among researchers and journal editors as to what minimum data standards should be required for publishing 13C-MFA studies; as a result, there are great discrepancies in terms of quality and consistency; and (2) there is a growing number of studies that cannot be reproduced or verified independently due to incomplete information provided in these publications. This creates confusion, e.g. when trying to reconcile conflicting results, and hinders progress in the field. Here, we review current status in the 13C-MFA field and highlight some of the shortcomings with regards to 13C-MFA publications. We then propose a checklist that encompasses good practices in 13C-MFA. We hope that these guidelines will be a valuable resource for the community and allow 13C-flux studies to be more easily reproduced and accessed by others in the future.

  • Dynamic metabolic flux analysis – tools for probing transient states of metabolic networks.
    Antoniewicz MR.
    Curr Opin Biotechnol, 24(6): 976-978, 2013 [Show Abstract]
    Computational approaches for analyzing dynamic states of metabolic networks provide a practical framework for design, control and optimization of biotechnological processes. In recent years, two promising modeling approaches have emerged for characterizing transients in cellular metabolism, dynamic metabolic flux analysis (DMFA) and dynamic flux balance analysis (DFBA). Both approaches combine metabolic network analysis based on pseudo steady-state assumption for intracellular metabolism with dynamic models for extracellular environment. One strategy to capture dynamics is by combining network analysis with a kinetic model. Predictive models are thus established that can be used to optimize bioprocessing conditions and identify useful genetic manipulations. Alternatively, by combining network analysis with methods for analyzing extracellular time-series data, transients in intracellular metabolic fluxes can be determined and applied for process monitoring and control.

  • 13C Metabolic flux analysis: optimal design of isotopic labeling experiments.
    Antoniewicz MR.
    Curr Opin Biotechnol, 24(6): 1116-1121, 2013 [Show Abstract]
    Measuring fluxes by 13C metabolic flux analysis (13C-MFA) has become a key activity in chemical and pharmaceutical biotechnology. Optimal design of isotopic labeling experiments is of central importance to 13C-MFA as it determines the precision with which fluxes can be estimated. Traditional methods for selecting isotopic tracers and labeling measurements did not fully utilize the power of 13C-MFA. Recently, new approaches were developed for optimal design of isotopic labeling experiments based on parallel labeling experiments and algorithms for rational selection of tracers. In addition, advanced isotopic labeling measurements were developed based on tandem mass spectrometry. Combined, these approaches can dramatically improve the quality of 13C-MFA results with important applications in metabolic engineering and biotechnology.

  • Parallel labeling experiments and metabolic flux analysis: past, present and future methodologies.
    Crown, SB, Antoniewicz MR.
    Metab Eng, 16: 21-32, 2013 [Show Abstract]
    Radioactive and stable isotopes have been applied for decades to elucidate metabolic pathways and quantify carbon flow in cellular systems using mass and isotope balancing approaches. Isotope-labeling experiments can be conducted as a single tracer experiment, or as parallel labeling experiments. In the latter case, several experiments are performed under identical conditions except for the choice of substrate labeling. In this review, we highlight robust approaches for probing metabolism and addressing metabolically related questions though parallel labeling experiments. In the first part, we provide a brief historical perspective on parallel labeling experiments, from the early metabolic studies when radioisotopes were predominant to present-day applications based on stable-isotopes. We also elaborate on important technical and theoretical advances that have facilitated the transition from radioisotopes to stable-isotopes. In the second part of the review, we focus on parallel labeling experiments for 13C-metabolic flux analysis (13C-MFA). Parallel experiments offer several advantages that include: tailoring experiments to resolve specific fluxes with high precision; reducing the length of labeling experiments by introducing multiple entry-points of isotopes; validating biochemical network models; and improving the performance of 13C-MFA in systems where the number of measurements is limited. We conclude by discussing some challenges facing the use of parallel labeling experiments for 13C-MFA and highlight the need to address issues related to biological variability, data integration, and rational tracer selection.

  • Tandem mass spectrometry for measuring stable-isotope labeling.
    Antoniewicz MR.
    Curr Opin Biotechnol, 24(1): 48-53, 2013 [Show Abstract]
    Measuring metabolic rates by 13C-metabolic flux analysis (13C-MFA) is of central importance for metabolic engineers and biomedical investigators. Enhanced knowledge of in vivo fluxes can be applied to reengineer the metabolic, regulatory, and phenotypic characteristics of organisms and help uncover the mechanisms of human ailments such as cancer and diabetes. To determine accurate and precise fluxes by 13C-MFA advanced methods for measuring stable-isotope labeling are needed. The application of tandem mass spectrometry is emerging as a new promising technique that has significant advantages over traditional MS and NMR based methods. With further refinement, tandem MS has the potential to become the new gold standard for measuring isotopic labeling for 13C-flux studies.

  • Parallel labeling experiments with [1,2-13C]glucose and [U-13C]glutamine provide new insights into CHO cell metabolism.
    Ahn WS, Antoniewicz MR.
    Metab Eng, 15: 34-47, 2013 [Show Abstract]
    We applied a parallel labeling strategy using two isotopic tracers, [1,2-13C]glucose and [U-13C]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 13C-labeling of intracellular metabolites was measured by gas chromatography-mass spectrometry (GC-MS). Metabolites in glycolysis pathway reached isotopic steady state for [1,2-13C]glucose within 1.5 h, and metabolites in the TCA cycle reached isotopic steady state for [U-13C]glutamine within 3 h. 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 13C-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-13C]glucose confirmed that additional metabolites were accumulating in the medium that became M+3 and M+6 labeled.

  • Using multiple tracers for 13C metabolic flux analysis.
    Antoniewicz MR.
    Methods Mol Biol, 985: 353-365, 2013 [Show Abstract]
    13C-Metabolic flux analysis (13C-MFA) is a powerful technique for quantifying intracellular metabolic fluxes in living cells. These in vivo fluxes provide important information on the physiology of cells in culture that can be used for metabolic engineering purposes and serve as inputs for systems biology modeling. The 13C-MFA technique consists of several steps: 1) selecting appropriate tracers for a given system of interest; 2) performing isotopic labeling experiments; 3) measuring isotopic labeling distributions in metabolic products; 4) estimating metabolic fluxes using least-squares regression; and 5) evaluating the goodness-of-fit and computing confidence intervals for estimated fluxes. In this chapter, we provide guidelines for performing 13C-MFA studies using multiple isotopic tracers, a technique that is especially useful for elucidating fluxes in complex biological systems where multiple carbon sources are present. Here, as an example, we describe key steps and decision points for designing 13C-MFA studies for microbes grown on mixtures of glucose and xylose. The general concepts described in this chapter are applicable to many other biological systems. For example, the same procedures can be applied to design 13C-MFA studies in mammalian cells, which are generally grown in complex media containing multiple substrates such as glucose and amino acids.

  • 2012

    • Parallel labeling experiments with [U-13C]glucose validate E. coli metabolic network model for 13C metabolic flux analysis.
      Leighty RW, Antoniewicz MR.
      Metab Eng, 14(5): 533-541, 2012 [Show Abstract]
      13C-Metabolic flux analysis (MFA) is a widely used method for measuring intracellular metabolic fluxes in living cells. 13C-MFA relies on several key assumptions: 1) the assumed metabolic network model is complete, in that it accounts for all significant enzymatic and transport reactions; 2) 13C-labeling measurements are accurate and precise; and 3) enzymes and transporters do not discriminate between 12C- and 13C-labeled metabolites. In this study, we tested these inherent assumptions of 13C-MFA for wild-type E. coli by parallel labeling experiments with [U-13C]glucose as tracer. Cells were grown in six parallel cultures in custom-constructed mini-bioreactors, starting from the same inoculum, on medium containing different mixtures of natural glucose and fully labeled [U-13C]glucose, ranging from 0% to 100% [U-13C]glucose. Macroscopic growth characteristics of E. coli showed no observable kinetic isotope effect. The cells grew equally well on natural glucose, 100% [U-13C]glucose, and mixtures thereof. 13C-MFA was then used to determine intracellular metabolic fluxes for several metabolic network models: an initial network model from literature; and extended network models that accounted for potential dilution effects of isotopic labeling. The initial network model did not give statistically acceptable fits and produced inconsistent flux results for the parallel labeling experiments. In contrast, an extended network model that accounted for dilution of intracellular CO2 by exchange with extracellular CO2 produced statistically acceptable fits, and the estimated metabolic fluxes were consistent for the parallel cultures. This study illustrates the importance of model validation for 13C-MFA. We show that an incomplete network model can produce statistically unacceptable fits, as determined by a chi-square test for goodness-of-fit, and return biased metabolic fluxes. The validated metabolic network model for E. coli from this study can be used in future investigations for unbiased metabolic flux measurements.

    • Measuring complete isotopomer distribution of aspartate using gas chromatography tandem mass spectrometry.
      Choi J, Grossbach MT, Antoniewicz MR.
      Anal Chem, 84(10): 4628-4632, 2012 [Show Abstract]
      We have developed a simple and accurate method for determining the complete positional isotopomer distribution of aspartate carbon atoms by gas chromatography tandem mass spectrometry for 13C-metabolic flux analysis. First, we screened tandem MS spectra of tert-butyldimethylsilyl (TBDMS) derivative of aspartate for daughter fragments with the necessary carbon atom fragmentations to fully resolve all sixteen isotopomers of aspartate. Tandem MS scanning parameters were optimized for each daughter fragment and the accuracy of tandem MS measurements were evaluated. We selected five accurate fragments that provided a redundant set of 47 labeling measurements to quantify the complete isotopomer distribution of aspartate by least-squares regression. The validity of the approach was demonstrated using six 13C-labeled aspartate standards and natural aspartate.

    • Rational design of 13C-labeling experiments for metabolic flux analysis in mammalian cells.
      Crown SB, Ahn WS, Antoniewicz MR.
      BMC Syst Biol, 6:43, 2012, [Show Abstract]
      Background. 13C-Metabolic flux analysis (13C-MFA) is a standard technique to probe cellular metabolism and elucidate in vivo metabolic fluxes. 13C-Tracer selection is an important step in conducting 13C-MFA, however, current methods are restricted to trial-and-error approaches, which commonly focus on an arbitrary subset of the tracer design space. To systematically probe the complete tracer design space, especially for complex systems such as mammalian cells, there is a pressing need for new rational approaches to identify optimal tracers.
      Results. Recently, we introduced a new framework for optimal 13C-tracer design based on elementary metabolite units (EMU) decomposition, in which a measured metabolite is decomposed into a linear combination of so-called EMU basis vectors. In this contribution, we applied the EMU method to a realistic network model of mammalian metabolism with lactate as the measured metabolite. The method was used to select optimal tracers for two free fluxes in the system, the oxidative pentose phosphate pathway (oxPPP) flux and anaplerosis by pyruvate carboxylase (PC). Our approach was based on sensitivity analysis of EMU basis vector coefficients with respect to free fluxes. Through efficient grouping of coefficient sensitivities, simple tracer selection rules were derived for high-resolution quantification of the fluxes in the mammalian network model. The approach resulted in a significant reduction of the number of possible tracers and the feasible tracers were evaluated using numerical simulations. Two optimal, novel tracers were identified that have not been previously considered for 13C-MFA of mammalian cells, specifically [2,3,4,5,6-13C]glucose for elucidating oxPPP flux and [3,4-13C]glucose for elucidating PC flux. We demonstrate that 13C-glutamine tracers perform poorly in this system in comparison to the optimal glucose tracers.
      Conclusions. In this work, we have demonstrated that optimal tracer design does not need to be a pure simulation-based trial-and-error process; rather, rational insights into tracer design can be gained through the application of the EMU basis vector methodology. Using this approach, rational labeling rules can be established a priori to guide the selection of optimal 13C-tracers for high-resolution flux elucidation in complex metabolic network models.

    • Selection of tracers for 13C-metabolic flux analysis using elementary metabolite units (EMU) basis vector methodology.
      Crown SB, Antoniewicz MR.
      Metab Eng, 14(2): 150-161, 2012 [Show Abstract]
      Metabolic flux analysis (MFA) is a powerful technique for elucidating in vivo fluxes in microbial and mammalian systems. A key step in 13C-MFA is the selection of an appropriate isotopic tracer to observe fluxes in a proposed network model. Despite the importance of MFA in metabolic engineering and beyond, current approaches for tracer experiment design are still largely based on trial-and-error. The lack of a rational methodology for selecting isotopic tracers prevents MFA from achieving its full potential. Here, we introduce a new technique for tracer experiment design based on the concept of elementary metabolite unit (EMU) basis vectors. We demonstrate that any metabolite in a network model can be expressed as a linear combination of so-called EMU basis vectors, where the corresponding coefficients indicate the fractional contribution of the EMU basis vector to the product metabolite. The strength of this approach is the decoupling of substrate labeling, i.e. the EMU basis vectors, from the dependence on free fluxes, i.e. the coefficients. In this work, we demonstrate that flux observability inherently depends on the number of independent EMU basis vectors and the sensitivities of coefficients with respect to free fluxes. Specifically, the number of independent EMU basis vectors places hard limits on how many free fluxes can be determined in a model. This constraint is used as a guide for selecting feasible substrate labeling. In three example models, we demonstrate that by maximizing the number of independent EMU basis vectors the observability of a system is improved. Inspection of sensitivities of coefficients with respect to free fluxes provides additional constraints for proper selection of tracers. The present contribution provides a fresh perspective on an important topic in metabolic engineering, and gives practical guidelines and design principles for a priori selection of isotopic tracers for 13C-MFA studies.

    • Towards dynamic metabolic flux analysis in CHO cell cultures.
      Ahn WS, Antoniewicz MR.
      Biotechnol J, 7(1): 61-74, 2012 [Show Abstract]
      Chinese hamster ovary (CHO) cells are the most widely used mammalian cell line for biopharmaceutical production, with a total global market approaching $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 (MFA) 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 (DMFA). 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.


    • Dynamic metabolic flux analysis (DMFA): A framework for determining fluxes at metabolic non-steady state.
      Leighty RW, Antoniewicz MR.
      Metab Eng, 13(6): 745-755, 2011 [Show Abstract]
      Metabolic flux analysis (MFA) is a key tool for measuring in vivo metabolic fluxes in systems at metabolic steady state. Here, we present a new method for dynamic metabolic flux analysis (DMFA) of systems that are not at metabolic steady state. The advantages of our DMFA method are: 1) time-series of metabolite concentration data can be applied directly for estimating dynamic fluxes, making data smoothing and estimation of average extracellular rates unnecessary; 2) flux estimation is achieved without integration of ODEs, or iterations; (3) characteristic metabolic phases in the fermentation data are identified automatically by the algorithm, rather than selected manually/arbitrarily. We demonstrate the application of the new DMFA framework in three example systems. First, we evaluated the performance of DMFA in a simple three-reaction model in terms of accuracy, precision and flux observability. Next, we analyzed a commercial glucose-limited fed-batch process for 1,3-propanediol production. The DMFA method accurately captured the dynamic behavior of the fed-batch fermentation and identified characteristic metabolic phases. Lastly, we demonstrate that DMFA can be used without any assumed metabolic network model for data reconciliation and detection of gross measurement errors using carbon and electron balances as constraints.

    • Metabolic flux analysis of CHO cells at growth and non-growth phases using isotopic tracers and mass spectrometry.
      Ahn WS, Antoniewicz MR.
      Metab Eng, 13(5): 598-609, 2011 [Show Abstract]
      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, [1,2-13C]glucose was introduced and the labeling of intracellular metabolites was measured by gas chromatography-mass spectrometry (GC-MS) at 6, 12 and 24 hr following the introduction of tracer. Intracellular metabolic fluxes were quantified from measured extracellular rates and 13C-labeling dynamics of intracellular metabolites using non-stationary 13C-metabolic flux analysis (13C-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.

    • Measuring deuterium enrichment of glucose hydrogen atoms by gas chromatography mass spectrometry.
      Antoniewicz MR, Kelleher JK, Stephanopoulos G.
      Anal Chem, 83(8): 3211-6, 2011 [Show Abstract]
      We developed a simple and accurate method for determining deuterium enrichment of glucose hydrogen atoms by electron impact gas chromatography mass spectrometry (GC-MS). First, we prepared 18 derivatives of glucose and screened over 200 glucose fragments to evaluate the accuracy and precision of mass isotopomer data for each fragment. We identified three glucose derivatives that gave six analytically useful ions: (1) glucose aldonitrile pentapropionate (m/z 173 derived from C4-C5 bond cleavage; m/z 259 from C3-C4 cleavage; m/z 284 from C4-C5 cleavage; and m/z 370 from C5-C6 cleavage); (2) glucose 1,2,5,6-di-isopropylidene propionate (m/z 301, no cleavage of glucose carbon atoms); and (3) glucose methyloxime pentapropionate (m/z 145 from C2-C3 cleavage). Deuterium enrichment at each carbon position of glucose was determined by least squares regression of mass isotopomer distributions. The validity of the approach was tested using labeled glucose standards and carefully prepared mixtures of standards. Our method determines deuterium enrichment of glucose hydrogen atoms with an accuracy of 0.3 mol%, or better, without the use of any calibration curves or correction factors. The analysis requires only 20 μL of plasma, which makes the method applicable for studying gluconeogenesis using deuterated water in cell culture and animal experiments.

    • Tandem mass spectrometry: A novel approach for metabolic flux analysis.
      Choi J, Antoniewicz MR.
      Metab Eng, 13(2): 225-233, 2011 [Show Abstract]
      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 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.

    • Resolving the TCA cycle and pentose-phosphate pathway of Clostridium acetobutylicum ATCC 824: Isotopomer analysis, in vitro activities and expression analysis.
      Crown SB, Indurthi DC, Ahn WS, Choi J, Papoutsakis ET, Antoniewicz MR.
      Biotechnol J, 6(3): 300-305, 2011 [Show Abstract]
      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 13C-based isotopomer analysis to re-examine central metabolic pathways of Clostridium acetobutylicum ATCC 824. We demonstrate using [1,2-13C]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.


    • Computational approaches in metabolic engineering.
      Reed JL, Senger RS, Antoniewicz MR, Young JD.
      J Biomed Biotechnol, 2010:207414, 2010 [Show Abstract]
      Metabolic engineering involves the adjustment of metabolic and regulatory processes to improve desired cellular behaviors, such as the production of proteins and chemicals. Since cellular metabolic and regulatory networks are often large and complex, the construction and analysis of computational models of these networks can be useful for identifying current network states and evaluating the effects of network perturbations on desired phenotypes. This special issue includes papers that illustrate how computational approaches can be used in metabolic engineering. Here, we provide a brief overview of several established computational approaches that can be used to aid in the engineering of metabolic networks, while describing some of the exciting recent advances in these fields.


    • Linking high resolution metabolic flux phenotypes and transcriptional regulation in yeast modulated by the global regulator Gcn4p.
      Moxley JF*, Jewett MC*, Antoniewicz MR*, Villas-Boas SG*, Alper H, Wheeler RT, Tong L, Hinnebusch AG, Ideker T, Nielsen J, Stephanopoulos G (* = equal contribution).
      Proc Natl Acad Sci U S A, 106(16): 6477-82, 2009 [Show Abstract]
      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 5764 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. While 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 as well as constructing strains for industrial applications.


    • Quantifying reductive carboxylation flux of glutamine to lipid in a brown adipocyte cell line.
      Yoo H*, Antoniewicz MR*, Stephanopoulos G, Kelleher JK (* = equal contribution).
      J Biol Chem, 283(30): 20621-7, 2008 [Show Abstract]
      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 while the reductive carboxylation pathway travels in reverse of the citric acid cycle from alpha-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 alpha-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 proposes 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.

    • An Elementary Metabolite Unit (EMU) based method of isotopically nonstationary flux analysis.
      Young JD, Walther JL, Antoniewicz MR, Yoo H, Stephanopoulos G.
      Biotechnol Bioeng, 99(3): 686-699, 2008 [Show Abstract]
      Nonstationary metabolic flux analysis (NMFA) is at present a very computationally intensive exercise, especially for large reaction networks. We applied elementary metabolite unit (EMU) theory to NMFA, dramatically reducing computational difficulty. We also introduced block decoupling, a new method that systematically and comprehensively divides EMU systems of equations into smaller subproblems to further reduce computational difficulty. These improvements led to a 5000-fold reduction in simulation times, enabling an entirely new and more complicated set of problems to be analyzed with NMFA. We simulated a series of nonstationary and stationary GC/MS measurements for a large E. coli network that was then used to estimate parameters and their associated confidence intervals. We found that fluxes could be successfully estimated using only nonstationary labeling data and external flux measurements. Addition of near-stationary and stationary time points increased the precision of most parameters. Contrary to prior reports, the precision of nonstationary estimates proved to be comparable to the precision of estimates based solely on stationary data. Finally, we applied EMU-based NMFA to experimental nonstationary measurements taken from brown adipocytes and successfully estimated fluxes and some metabolite concentrations. By using NFMA instead of traditional MFA, the experiment required only 6 h instead of 50 (the time necessary for most metabolite labeling to reach 99% of isotopic steady state).


    • Accurate assessment of amino acid mass isotopomer distributions for metabolic flux analysis.
      Antoniewicz MR, Kelleher JK, Stephanopoulos G.
      Anal Chem, 79(19):7554-9, 2007 [Show Abstract]
      Metabolic flux analysis based on stable-isotope labeling experiments and analysis of mass isotopomer distributions (MID) of cellular metabolites is a tool of great significance for metabolic engineering and study of human disease. This method relies on accurate and precise measurements of mass isotopomers by gas chromatography/mass spectrometry. To improve flux estimates, we assessed potential errors in determining MID of tert-butyldimethylsilyl-derivatized amino acids, which were attributed to (i) the choice of integration algorithm, (ii) concentration effects, and (iii) overlapping fragments. We report 29 amino acid fragments that are useful for flux analysis and another 18 fragments that should be rejected, most importantly Val-302, Leu-200, Leu-302, Ile-302, Ser-302, and Asp-316. In addition, we provide a protocol to minimize errors for determining MID to less than 0.4 mol % for accepted fragments.

    • Metabolic flux analysis in a nonstationary system: fed-batch fermentation of a high yielding strain of E. coli producing 1,3-propanediol.
      Antoniewicz MR, Kraynie DF, Laffend LA, González-Lergier J, Kelleher JK, Stephanopoulos G.
      Metab Eng, 9(3): 277-292, 2007 [Show Abstract]
      Metabolic fluxes estimated from stable-isotope studies provide a key to understanding cell physiology and regulation of metabolism. A limitation of the classical method for metabolic flux analysis (MFA) is the requirement for isotopic steady state. To extend the scope of flux determination from stationary to nonstationary systems, we present a novel modeling strategy that combines key ideas from isotopomer spectral analysis (ISA) and stationary MFA. Isotopic transients of the precursor pool and the sampled products are described by two parameters, D and G parameters, respectively, which are incorporated into the flux model. The G value is the fraction of labeled product in the sample, and the D value is the fractional contribution of the feed for the production of labeled products. We illustrate the novel modeling strategy with a nonstationary system that closely resembles industrial production conditions, i.e. fed-batch fermentation of Escherichia coli that produces 1,3-propanediol (PDO). Metabolic fluxes and the D and G parameters were estimated by fitting labeling distributions of biomass amino acids measured by GC/MS to a model of E. coli metabolism. We obtained highly consistent fits from the data with 82 redundant measurements. Metabolic fluxes were estimated for 20 time points during course of the fermentation. As such we established, for the first time, detailed time profiles of in vivo fluxes. We found that intracellular fluxes changed significantly during the fed-batch. The intracellular flux associated with PDO pathway increased by 10%. Concurrently, we observed a decrease in the split ratio between glycolysis and pentose phosphate pathway from 70/30 to 50/50 as a function of time. The TCA cycle flux, on the other hand, remained constant throughout the fermentation. Furthermore, our flux results provided additional insight in support of the assumed genotype of the organism.

    • Elementary Metabolite Units (EMU): A novel framework for modeling isotopic distributions.
      Antoniewicz MR, Kelleher JK, Stephanopoulos G.
      Metab Eng, 9(1): 68-86, 2007 [Show Abstract]
      Metabolic flux analysis (MFA) has emerged as a tool of great significance for metabolic engineering and mammalian physiology. An important limitation of MFA, as carried out via stable isotope labeling and GC/MS and nuclear magnetic resonance (NMR) measurements, is the large number of isotopomer or cumomer equations that need to be solved, especially when multiple isotopic tracers are used for the labeling of the system. This restriction reduces the ability of MFA to fully utilize the power of multiple isotopic tracers in elucidating the physiology of realistic situations comprising complex bioreaction networks. Here, we present a novel framework for the modeling of isotopic labeling systems that significantly reduces the number of system variables without any loss of information. The elementary metabolite unit (EMU) framework is based on a highly efficient decomposition method that identifies the minimum amount of information needed to simulate isotopic labeling within a reaction network using the knowledge of atomic transitions occurring in the network reactions. The functional units generated by the decomposition algorithm, called EMUs, form the new basis for generating system equations that describe the relationship between fluxes and stable isotope measurements. Isotopomer abundances simulated using the EMU framework are identical to those obtained using the isotopomer and cumomer methods, however, require significantly less computation time. For a typical 13C-labeling system the total number of equations that needs to be solved is reduced by one order-of-magnitude (100s EMUs vs. 1000s isotopomers). As such, the EMU framework is most efficient for the analysis of labeling by multiple isotopic tracers. For example, analysis of the gluconeogenesis pathway with 2H, 13C, and 18O tracers requires only 354 EMUs, compared to more than two million isotopomers.


    • Determination of confidence intervals of metabolic fluxes estimated from stable isotope measurements.
      Antoniewicz MR, Kelleher JK, Stephanopoulos G.
      Metab Eng, 8(4): 324-337, 2006 [Show Abstract]
      Metabolic fluxes, estimated from stable isotope studies, provide a key to quantifying physiology in fields ranging from metabolic engineering to the analysis of human metabolic diseases. A serious drawback of the flux estimation method in current use is that it does not produce confidence limits for the estimated fluxes. Without this information it is difficult to interpret flux results and expand the physiological significance of flux studies. To address this shortcoming we derived analytical expressions of flux sensitivities with respect to isotope measurements and measurement errors. These tools allow the determination of local statistical properties of fluxes and relative importance of measurements. Furthermore, we developed an efficient algorithm to determine accurate flux confidence intervals and demonstrated that confidence intervals obtained with this method closely approximate true flux uncertainty. In contrast, confidence intervals approximated from local estimates of standard deviations are inappropriate due to inherent system nonlinearities. We applied these methods to analyze the statistical significance and confidence of estimated gluconeogenesis fluxes from human studies with [U-13C]glucose as tracer and found true limits for flux estimation in specific human isotopic protocols.

    • Evaluation of regression models in metabolic physiology: Predicting fluxes from isotopic data without knowledge of the pathway.
      Antoniewicz MR, Stephanopoulos G, Kelleher JK.
      Metabolomics, 2(1): 41-52, 2006 [Show Abstract]
      This study explores the ability of regression models, with no knowledge of the underlying physiology, to estimate physiological parameters relevant for metabolism and endocrinology. Four regression models were compared: multiple linear regression (MLR), principal component regression (PCR), partial least-squares regression (PLS) and regression using artificial neural networks (ANN). The pathway of mammalian gluconeogenesis was analyzed using [U-13C]glucose as tracer. A set of data was simulated by randomly selecting physiologically appropriate metabolic fluxes for the 9 steps of this pathway as independent variables. The isotope labeling patterns of key intermediates in the pathway were then calculated for each set of fluxes, yielding 29 dependent variables. Two thousand sets were created, allowing independent training and test data. Regression models were asked to predict the nine fluxes, given only the 29 isotopomers. For large training sets (>50) the artificial neural network model was superior, capturing 95% of the variability in the gluconeogenic flux, whereas the three linear models captured only 75%. This reflects the ability of neural networks to capture the inherent non-linearities of the metabolic system. The effect of error in the variables and the addition of random variables to the data set was considered. Model sensitivities were used to find the isotopomers that most influenced the predicted flux values. These studies provide the first test of multivariate regression models for the analysis of isotopomer flux data. They provide insight for metabolomics and the future of isotopic tracers in metabolic research where the underlying physiology is complex or unknown.


    • Energetics of growth and penicillin production in a high-producing strain of Penicillium chrysogenum.
      van Gulik WM, Antoniewicz MR, deLaat WT, Vinke JL, Heijnen JJ.
      Biotechnol Bioeng, 72(2): 185-193, 2001 [Show Abstract]
      The results of a large number of carbon-limited chemostat cultures of Penicillium chrysogenum carried out on glucose, ethanol, and acetate as the growth limiting substrate have been used to obtain an estimation of the adenosine triphosphate (ATP) costs for mycelium growth, penicillin production, and maintenance and the overall stoichiometry of oxidative phosphorylation of the fungus. It was found that penicillin production was accompanied by a significant additional energy drain (73 mol of ATP per mole of penicillin-G) from primary metabolism. This finding has been confirmed in independent experiments and has been shown to result in a significantly lower estimate for the maximum theoretical yield of penicillin-G on the carbon source.

    Invited Presentations

    1. Metabolic Engineering 13 Conference (Invited). Honolulu, HI. July 2020
    2. University of Texas Southwestern Medical Center, Dallas, TX. June 2020
    3. University of Michigan, Department of Chemical Engineering, Ann Arbor, MI. Feb 2019
    4. University of Houston, Dept. of Chemical & Biomolecular Eng, Houston, TX. Sept 2018
    5. University of Michigan, Department of Chemical Engineering, Ann Arbor, MI. Sept 2018
    6. DSM Nutritional Products, Columbia, MD. July 2018
    7. Manus Bio, Cambridge, MA. May 2018
    8. Massachusetts Institute of Technology, Dept. of Chemical Eng., Cambridge, MA. Nov 2017
    9. AIChE 2017 Meeting (Invited Keynote). Minneapolis, MN. Oct 2017
    10. UC San Diego, Bioengineering Department, San Diego, CA. Oct 2017
    11. Genomatica, San Diego, CA. Oct 2017
    12. University of Minnesota, Biotechnology Institute, St Paul, MN. Sept 2017
    13. BD Life Sciences. Cockeysville, MD. May 2017
    14. University of Pittsburgh, Department of Chemical and Petroleum Eng. Pittsburgh, PA. April 2017
    15. University of Connecticut, Dept. of Chemical & Biomolecular Engineering. Storrs, CT. March 2017
    16. Penn State University, Department of Chemical Engineering. University Park, PA. Oct 2016
    17. Merck, Inc. Kenilworth, NJ. Sept 2016
    18. Osaka University, Department of Bioinformatic Engineering. Osaka, Japan. July 2016
    19. Metabolic Engineering 11 Conference (Invited). Awaji Island, Japan. June 2016
    20. Bristol-Myers Squibb. Bloomsbury, NJ. June 2016
    21. University of Illinois at Chicago, Dept. of Biochemistry & Molecular Genetics, Chicago, IL. Sept 2015
    22. Society for Industrial Microbiology & Biotechnology (SIMB) (Invited), Philadelphia, PA. Aug 2015
    23. ACS BIOT 2015 Meeting (Invited Award Talk). Denver, CO. March 2015
    24. University of Maryland, Baltimore County. Baltimore, MD. March 2015
    25. Duke University, Sarah W. Stedman Nutrition & Metabolism Center. Durham, NC. June 2014
    26. University of Pennsylvania, Cancer Cell Metabolism Meeting (Invited), Philadelphia, PA. March 2014
    27. Metabolic Origins of Disease Symposium (Invited). Orlando, FL. March 2014
    28. Florida State University, Dept. of Chemistry & Biochemistry. Tallahassee, FL. Feb 2014
    29. Delft University of Technology, Department of Biotechnology. The Netherlands. Novermber 2013
    30. UD Chemical & Biomolecular Engineering Winter Research Review. Newark, DE. Jan 2013
    31. DSM Biotechnology Center. Delft, The Netherlands. Novermber 2012
    32. Delft University of Technology, Department of Biotechnology. The Netherlands. Novermber 2012
    33. University of Delaware’s Francis Alison Society Meeting (Invited). Newark, DE. Oct 2013
    34. North Carolina Biotechnology Center, Cell Culture Symposium (Invited). Durham, MC. October 2012
    35. Donald Danforth Center's 14th Annual Symposium (Invited). St. Louis, MO. Sept 2012
    36. The Bioprocessing Summit, Optimizing Cell Culture Technology (Invited). Boston, MA. Aug 2012
    37. Society for Industrial Microbiol & Biotechnol (SIMB) Meeting (Invited), Washington, DC. Aug 2012
    38. KU Leuven, Vesalius Research Center (VRC), Leuven, Belgium. July 2012
    39. University of Delaware, Department of Math Sciences. Newark, DE. April 2012
    40. University of Wisconsin-Madison, Chemical and Biological Engineering. Madison, WI. Feb 2012
    41. University of Delaware, Department of Animal and Food Science. Newark, DE. November 2011
    42. Delft University of Technology, Department of Biotechnology. The Netherlands. October 2011
    43. Sanford Burnham Medical Research Institute. La Jolla, CA. May 2011
    44. MedImmune, LLC. Gaithersburg, MD. May 2011
    45. University of Oklahoma, Dept. of Chemical, Biological and Materials Eng. Norman, OK. Sept 2010
    46. Metabolic Engineering VIII Conference (Invited). Jeju Island, Korea. June 2010
    47. Bioinformatics and Bioengineering Conference (Invited). Philadelphia, PA. May 2010
    48. E. I. DuPont de Nemours, Horizons in Biotechnology Seminar. Wilmington, DE. April 2010
    49. Princeton University, Department of Chemical Engineering. Princeton, NJ. April 2010
    50. Amyris Biotechnologies. Emeryville, CA. December 2008
    51. University of Delaware, Chemistry-Biology Interface (CBI) Seminar. Newark, DE. April 2008
    52. Delaware Biotechnology Institute, DBI Seminar Series. Newark, DE. December 2007
    53. E. I. DuPont de Nemours, Horizons in Biotechnology Seminar. Wilmington, DE. December 2007
    54. 2nd International Metabolomics Symposium (Invited). Louisville, KY. March 2007
    55. University of Maryland, Dept. of Chemical and Biomolecular Engineering. College Park, MD. March 2007
    56. University of Delaware, Department of Chemical Engineering. Newark, DE. February 2007
    57. National Institutes of Health, Brain Physiology and Metabolism Section. Bethesda, MD. January 2007
    58. E. I. DuPont de Nemours, Central Research & Development. Wilmington, DE. June 2006
    59. Duke University, Sarah W. Stedman Nutrition & Metabolism Center. Durham, NC. October 2005
    60. Broad Institute of MIT and Harvard, Broad Metabolism Initiative. Cambridge, MA. January 2005
    61. 7th International Meeting of the Microarray Gene Expression Data Society. Toronto, Canada. Sept 2004
    62. E. I. DuPont de Nemours, Central Research & Development. Wilmington, DE. November 2003

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