13C Metabolic Flux Analysis Research Articles

Elevated temperatures do not trigger a conserved metabolic network response among thermotolerant yeasts

BackgroundThermotolerance is a highly desirable trait of microbial cell factories and has been the focus of extensive research. Yeast usually tolerate only a narrow temperature range and just two species, Kluyveromyces marxianus and Ogataea polymorpha have been described to grow at reasonable rates above 40 °C. However, the complex mechanisms of thermotolerance in yeast impede its full comprehension and the rare physiological data at elevated temperatures has so far not been matched with corresponding metabolic analyses.ResultsTo elaborate on the metabolic network response to increased fermentation temperatures of up to 49 °C, comprehensive physiological datasets of several Kluyveromyces and Ogataea strains were generated and used for 13C-metabolic flux analyses. While the maximum growth temperature was very similar in all investigated strains, the metabolic network response to elevated temperatures was not conserved among the different species. In fact, metabolic flux distributions were remarkably irresponsive to increasing temperatures in O. polymorpha, while the K. marxianus strains exhibited extensive flux rerouting at elevated temperatures.ConclusionsWhile a clear mechanism of thermotolerance is not deducible from the fluxome level alone, the generated data can be valued as a knowledge repository for using temperature to modulate the metabolic activity towards engineering goals.

2H and 13C metabolic flux analysis elucidates in vivo thermodynamics of the ED pathway in Zymomonas mobilis

Zymomonas mobilis is an industrially relevant bacterium notable for its ability to rapidly ferment simple sugars to ethanol using the Entner-Doudoroff (ED) glycolytic pathway, an alternative to the well-known Embden-Meyerhof-Parnas (EMP) pathway used by most organisms. Recent computational studies have predicted that the ED pathway is substantially more thermodynamically favorable than the EMP pathway, a potential factor explaining the high glycolytic rate in Z. mobilis. Here, to investigate the in vivo thermodynamics of the ED pathway and central carbon metabolism in Z. mobilis, we implemented a network-level approach that integrates quantitative metabolomics with 2H and 13C metabolic flux analysis to estimate reversibility and Gibbs free energy (ΔG) of metabolic reactions. This analysis revealed a strongly thermodynamically favorable ED pathway in Z. mobilis that is nearly twice as favorable as the EMP pathway in E. coli or S. cerevisiae. The in vivo step-by-step thermodynamic profile of the ED pathway was highly similar to previous in silico predictions, indicating that maximizing ΔG for each pathway step likely constitutes a cellular objective in Z. mobilis. Our analysis also revealed novel features of Z. mobilis metabolism, including phosphofructokinase-like enzyme activity, tricarboxylic acid cycle anaplerosis via PEP carboxylase, and a metabolic imbalance in the pentose phosphate pathway resulting in excretion of shikimate pathway intermediates. The integrated approach we present here for in vivo ΔG quantitation may be applied to the thermodynamic profiling of pathways and metabolic networks in other microorganisms and will contribute to the development of quantitative models of metabolism.

13C-Metabolic Flux Analysis Reveals Effect of Phenol on Central Carbon Metabolism in Escherichia coli

Phenol is an important chemical product that can be used in a wide variety of applications, and it is currently produced from fossil resources. Fermentation production of phenol from renewable biomass resources by microorganisms is highly desirable for sustainable development. However, phenol toxicity hampers phenol production in industrial microorganisms such as Escherichia coli. In the present study, it was revealed that culturing E. coli in the presence of phenol not only decreased growth rate, but also biomass yield. This suggests that phenol affects the carbon flow of the metabolism, but the mechanism is unknown. To investigate the effect of phenol on the flux distribution of central carbon metabolism, 13C-metabolic flux analysis (13C-MFA) was performed on cells grown under different phenol concentrations (0, 0.1, and 0.15%). 13C-MFA revealed that the TCA cycle flux reduced by 25% increased acetate production from acetyl-CoA by 30% in the presence of 0.1% phenol. This trend of flux changes was emphasized at a phenol concentration of 0.15%. Although the expression level of citrate synthase, which catalyzes the first reaction of the TCA cycle, does not change regardless of phenol concentrations, the in vitro enzyme activity assay shows that the reaction was inhibited by phenol. These results suggest that the TCA cycle flux decreased due to phenol inhibition of citrate synthase; therefore, ATP could not be sufficiently produced by respiration, and growth rate decreased. Furthermore, since carbon was lost as acetate due to overflow metabolism, the biomass yield became low in the presence of phenol.

Proposing a validation scheme for 13C metabolite tracer studies in high-resolution mass spectrometry

13C metabolite tracer and metabolic flux analyses require upfront experimental planning and validation tools. Here, we present a validation scheme including a comparison of different LC methods that allow for customization of analytical strategies for tracer studies with regard to the targeted metabolites. As the measurement of significant changes in labeling patterns depends on the spectral accuracy, we investigate this aspect comprehensively for high-resolution orbitrap mass spectrometry combined with reversed-phase chromatography, hydrophilic interaction liquid chromatography, or anion-exchange chromatography. Moreover, we propose a quality control protocol based on (1) a metabolite containing selenium to assess the instrument performance and on (2) in vivo synthesized isotopically enriched Pichia pastoris to validate the accuracy of carbon isotopologue distributions (CIDs), in this case considering each isotopologue of a targeted metabolite panel. Finally, validation involved a thorough assessment of procedural blanks and matrix interferences. We compared the analytical figures of merit regarding CID determination for over 40 metabolites between the three methods. Excellent precisions of less than 1% and trueness bias as small as 0.01–1% were found for the majority of compounds, whereas the CID determination of a small fraction was affected by contaminants. For most compounds, changes of labeling pattern as low as 1% could be measured.Graphical abstract

GC-MS-based 13C metabolic flux analysis resolves the parallel and cyclic glucose metabolism of Pseudomonas putida KT2440 and Pseudomonas aeruginosa PAO1

The genus Pseudomonas comprises approximately 200 species with numerous isolates that are common inhabitants of soil, water, and vegetation and has been of particular interest for more than one hundred years. Here, we present a novel approach for accurate, precise and convenient 13C metabolic flux analysis of these and other microbes possessing periplasmic glucose oxidation and a cyclic hexose metabolism, which forms the recently discovered EDEMP cycle. This complex cyclic architecture cannot be resolved by common metabolic flux workflows, which rely on GC-MS-based labelling analysis of proteinogenic amino acids. Computational analyses revealed that this limitation can be overcome by three parallel labelling experiments on specific tracers, i.e., [1–13C], [6–13C] and 50% [13C6] glucose, with additional consideration of labelling information from glucose and glucosamine. Glucose and glucosamine display building blocks from cellular glycogen, peptidoglycan and lipopolysaccharides, reflect the pools of glucose6-phosphate and fructose6-phosphate in the heart of the EDEMP cycle and as we show, can be precisely assessed in biomass hydrolysates by GC-MS. The developed setup created 534 mass isotopomers and enabled high-resolution flux analysis of the cell factory Pseudomonas putida KT2440 and the human pathogen P. aeruginosa PAO1. The latter strain oxidized approximately 90% of its glucose into gluconate via the periplasmic route, whereas only a small fraction of substrate was phosphorylated and consumed via the cytoplasmic route. The oxidative pentose phosphate pathway was completely inactive, indicating the essentiality of the Entner-Doudoroff pathway and recycling of triose units into anabolic precursors.In addition to pseudomonads, many microbes operate a cyclic hexose metabolism, which becomes more accessible to flux analysis with this approach. In this regard, the presented approach displays a valuable extension of the available set of flux methods for these types of bacteria.

13C metabolic flux analysis-guided metabolic engineering of Escherichia coli for improved acetol production from glycerol

BackgroundBioprocessing offers a sustainable and green approach to manufacture various chemicals and materials. Development of bioprocesses requires transforming common producer strains to cell factories. 13C metabolic flux analysis (13C-MFA) can be applied to identify relevant targets to accomplish the desired phenotype, which has become one of the major tools to support systems metabolic engineering. In this research, we applied 13C-MFA to identify bottlenecks in the bioconversion of glycerol into acetol by Escherichia coli. Valorization of glycerol, the main by-product of biodiesel, has contributed to the viability of biofuel economy.ResultsWe performed 13C-MFA and measured intracellular pyridine nucleotide pools in a first-generation acetol producer strain (HJ06) and a non-producer strain (HJ06C), and identified that engineering the NADPH regeneration is a promising target. Based on this finding, we overexpressed nadK encoding NAD kinase or pntAB encoding membrane-bound transhydrogenase either individually or in combination with HJ06, obtaining HJ06N, HJ06P and HJ06PN. The step-wise approach resulted in increasing the acetol titer from 0.91 g/L (HJ06) to 2.81 g/L (HJ06PN). To systematically characterize and the effect of mutation(s) on the metabolism, we also examined the metabolomics and transcriptional levels of key genes in four strains. The pool sizes of NADPH, NADP+ and the NADPH/NADP+ ratio were progressively increased from HJ06 to HJ06PN, demonstrating that the sufficient NADPH supply is critical for acetol production. Flux distribution was optimized towards acetol formation from HJ06 to HJ06PN: (1) The carbon partitioning at the DHAP node directed gradually more carbon from the lower glycolytic pathway through the acetol biosynthetic pathway; (2) The transhydrogenation flux was constantly increased. In addition, 13C-MFA showed the rigidity of upper glycolytic pathway, PP pathway and the TCA cycle to support growth. The flux patterns were supported by most metabolomics data and gene expression profiles.ConclusionsThis research demonstrated how 13C-MFA can be applied to drive the cycles of design, build, test and learn implementation for strain development. This succeeding engineering strategy can also be applicable for rational design of other microbial cell factories.

13C-Metabolic Flux Analysis Reveals the Metabolic Flux Redistribution for Enhanced Production of Poly-γ-Glutamic Acid in dlt Over-Expressed Bacillus licheniformis.

Poly-γ-glutamic acid (γ-PGA) is an anionic polymer with various applications. Teichoic acid (TA) is a special component of cell wall in gram-positive bacteria, and its D-alanylation modification can change the net negative charge of cell surface, autolysin activity and cationic binding efficiency, and might further affect metabolic production. In this research, four genes (dltA, dltB, dltC, and dltD) of dlt operon were, respectively, deleted and overexpressed in the γ-PGA producing strain Bacillus licheniformis WX-02. Our results implied that overexpression of these genes could all significantly increase γ-PGA synthetic capabilities, among these strains, the dltB overexpression strain WX-02/pHY-dltB owned the highest γ-PGA yield (2.54 g/L), which was 93.42% higher than that of the control strain WX-02/pHY300 (1.31 g/L). While, the gene deletion strains produced lower γ-PGA titers. Furthermore, 13C-Metabolic flux analysis was conducted to investigate the influence of dltB overexpression on metabolic flux redistribution during γ-PGA synthesis. The simulation data demonstrated that fluxes of pentose phosphate pathway and tricarboxylic acid cycle in WX-02/pHY-dltB were 36.41 and 19.18 mmol/g DCW/h, increased by 7.82 and 38.38% compared to WX-02/pHY300 (33.77 and 13.86 mmol/g DCW/h), respectively. The synthetic capabilities of ATP and NADPH were also increased slightly. Meanwhile, the fluxes of glycolytic and by-product synthetic pathways were all reduced in WX-02/pHY-dltB. All these above phenomenons were beneficial for γ-PGA synthesis. Collectively, this study clarified that overexpression of dltB strengthened the fluxes of PPP pathway, TCA cycle and energy metabolism for γ-PGA synthesis, and provided an effective strategy for enhanced production of γ-PGA.

The Role of Pool Size Measurements in Improving Flux Estimations in Non-Stationary Metabolic Flux Analysis

Over the past several decades, Metabolic Flux Analysis (MFA) has gained importance in the biomedical and pharmaceutical sciences. MFA is based on a computational procedure used to determine metabolic reaction rates (fluxes) and in some cases metabolite concentrations (pool sizes) within metabolic pathways where direct observation and measurement of intracellular function is not feasible. Applying 13C MFA to biological data, such as that in liver metabolism, helps to determine, for example, the fate of glucose in regulatory metabolic pathways under physiological and pathophysiological steady state conditions of a cell. However, designing models with reliable flux estimations remains a challenge. A debate that still persists is whether metabolite pool size measurements added as constraints in optimization algorithms improve metabolic flux predictions. We studied the role of pool sizes using simple model networks with total of 8 and 9 metabolites. To investigate the effect of pool size constraints on flux estimations, we set up a series of scenarios varying inputs into the model for several metabolites (such as information on pool size measurements and noise levels in the mass isotopomer distribution (MID) labeling data). Our findings indicate that in models when a full set of MID labeling data (as oppose to just M0 labeling data) is available, pool sizes become dispensable in flux estimations. Moreover, in biological networks where a full set of labeling data for certain metabolites is accessible but the exact model network is unknown, flux estimations are more reliable if pool sizes are not included as model constraints.

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

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

Visual FTBL Generator: Visual Generation Tool for 13C-FLUX File

Abstract 13C metabolic flux analysis (13C-MFA) has achieved increasing significance in quantitative metabolic system analysis in recent years. In 13C metabolic flux analysis, 13C-FLUX software is a major analytical tool. The software’s input script is primarily expressed in textual form without visual presentation of the structure of the entire metabolic network, thus error-prone in manual input. To solve this problem, we have developed a visual FTBL generator (VFG, available at http://47.100.98.220/vfg/index.jsp in a Google or Firefox browser)for MFA that eliminates the tedious, error-prone text entry mode and provides a user-friendly graphical interface and simple visual reaction generation functions.

Genome-Scale Fluxome of Synechococcus elongatus UTEX 2973 Using Transient 13C-Labeling Data.

Synechococcus elongatus UTEX 2973 (Synechococcus 2973) has the shortest reported doubling time (2.1 h) among cyanobacteria, making it a promising platform for the solar-based production of biochemicals. In this meta-analysis, its intracellular flux distribution was recomputed using genome-scale isotopic nonstationary 13C-metabolic flux analysis given the labeling dynamics of 13 metabolites reported in an earlier study. To achieve this, a genome-scale mapping model, namely imSyu593, was constructed using the imSyn617 mapping model for Synechocystis sp. PCC 6803 (Synechocystis 6803) as the starting point encompassing 593 reactions. The flux elucidation revealed nearly complete conversion (greater than 96%) of the assimilated carbon into biomass in Synechococcus 2973. In contrast, Synechocystis 6803 achieves complete conversion of only 86% of the assimilated carbon. This high biomass yield was enabled by the reincorporation of the fixed carbons lost in anabolic and photorespiratory pathways in conjunction with flux rerouting through a nondecarboxylating reaction such as phosphoketolase. This reincorporation of lost CO2 sustains a higher flux through the photorespiratory C2 cycle that fully meets the glycine and serine demands for growth. In accordance with the high carbon efficiency drive, acetyl-coenzyme A was entirely produced using the carbon-efficient phosphoketolase pathway. Comparison of the Synechococcus 2973 flux map with that of Synechocystis 6803 revealed differences in the use of Calvin cycle and photorespiratory pathway reactions. The two species used different reactions for the synthesis of metabolites such as fructose-6-phosphate, glycine, sedoheptulose-7-phosphate, and Ser. These findings allude to a highly carbon-efficient metabolism alongside the fast carbon uptake rate in Synechococcus 2973, which explains its faster growth rate.

Deletion of four genes in Escherichia coli enables preferential consumption of xylose and secretion of glucose

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.

Bacterial Metabolism During Biofilm Growth Investigated by 13C Tracing.

This study investigated the metabolism of Pseudomonas aeruginosa PAO1 during its biofilm development via microscopy imaging, gene expression analysis, and 13C-labeling. First, dynamic labeling was employed to investigate glucose utilization rate in fresh biofilms (thickness 40∼60 micrometer). The labeling turnover time of glucose-6-P indicated biofilm metabolism was substantially slower than planktonic cells. Second, PAO1 was cultured in continuous tubular biofilm reactors or shake flasks. Then 13C-metabolic flux analysis of PAO1 was performed based on the isotopomer patterns of proteinogenic amino acids. The results showed that PAO1 biofilm cells during growth conserved the flux features as their planktonic mode. (1) Glucose could be degraded by two cyclic routes (the TCA cycle and the Entner-Doudoroff-Embden-Meyerhof-Parnas loop) that facilitated NAD(P)H supplies. (2) Anaplerotic pathways (including pyruvate shunt) increased flux plasticity. (3) Biofilm growth phenotype did not require significant intracellular flux rewiring (variations between biofilm and planktonic flux network, normalized by glucose uptake rate as 100%, were less than 20%). (4) Transcription analysis indicated that key catabolic genes in fresh biofilm cells had expression levels comparable to planktonic cells. Finally, PAO1, Shewanella oneidensis (as the comparing group), and their c-di-GMP transconjugants (with different biofilm formation capabilities) were 13C-labeled under biofilm reactors or planktonic conditions. Analysis of amino acid labeling variances from different cultures indicated Shewanella flux network was more flexibly changed than PAO1 during its biofilm formation.

Genome-Scale 13C Fluxomics Modeling for Metabolic Engineering of Saccharomyces cerevisiae.

Synthetic biology is a rapidly developing field that pursues the application of engineering principles and development approaches to biological engineering. Synthetic biology is poised to change the way biology is practiced, and has important practical applications: for example, building genetically engineered organisms to produce biofuels, medicines, and other chemicals. Traditionally, synthetic biology has focused on manipulating a few genes (e.g., in a single pathway or genetic circuit), but its combination with systems biology holds the promise of creating new cellular architectures and constructing complex biological systems from the ground up. Enabling this merge of synthetic and systems biology will require greater predictive capability for modeling the behavior of cellular systems, and more comprehensive data sets for building and calibrating these models. The so-called "-omics" data sets can now be generated via high throughput techniques in the form of genomic, proteomic, transcriptomic, and metabolomic information on the engineered biological system. Of particular interest with respect to the engineering of microbes capable of producing biofuels and other chemicals economically and at scale are metabolomic datasets, and their insights into intracellular metabolic fluxes. Metabolic fluxes provide a rapid and easy to understand picture of how carbon and energy flow throughout the cell. Here, we present a detailed guide to performing metabolic flux analysis and modeling using the open source JBEI Quantitative Metabolic Modeling (jQMM) library. This library allows the user to transform metabolomics data in the form of isotope labeling data from a 13C labeling experiment into a determination of cellular fluxes that can be used to develop genetic engineering strategies for metabolic engineering.The jQMM library presents a complete toolbox for performing a range of different tasks of interest in metabolic engineering. Various different types of flux analysis and modeling can be performed such as flux balance analysis, 13C metabolic flux analysis, and two-scale 13C metabolic flux analysis (2S-13C MFA). 2S-13C MFA is a novel method that determines genome-scale fluxes without the need of every single carbon transition in the metabolic network. In addition to several other capabilities, the jQMMlibrary can make model based predictions for how various genetic engineering strategies can be incorporated toward bioengineering goals: it can predict the effects of reaction knockouts on metabolism using both the MoMA and ROOM methodologies. In this chapter, we will illustrate the use of the jQMM library through a step-by-step demonstration of flux determination and knockout prediction in a complex eukaryotic model organism: Saccharomyces cerevisiae (S. cerevisiae). Included with this chapter is a digital Jupyter Notebook file that provides a computable appendix showing a self-contained example of jQMM usage, which can be changed to fit the user's specific needs. As an open source software project, users can modify and extend the code base to make improvements at will, allowing them to share their development work and contribute back to the jQMM modeling community.

A Pareto approach to resolve the conflict between information gain and experimental costs: Multiple-criteria design of carbon labeling experiments.

Science revolves around the best way of conducting an experiment to obtain insightful results. Experiments with maximal information content can be found by computational experimental design (ED) strategies that identify optimal conditions under which to perform the experiment. Several criteria have been proposed to measure the information content, each emphasizing different aspects of the design goal, i.e., reduction of uncertainty. Where experiments are complex or expensive, second sight is at the budget governing the achievable amount of information. In this context, the design objectives cost and information gain are often incommensurable, though dependent. By casting the ED task into a multiple-criteria optimization problem, a set of trade-off designs is derived that approximates the Pareto-frontier which is instrumental for exploring preferable designs. In this work, we present a computational methodology for multiple-criteria ED of information-rich experiments that accounts for virtually any set of design criteria. The methodology is implemented for the case of 13C metabolic flux analysis (MFA), which is arguably the most expensive type among the ‘omics’ technologies, featuring dozens of design parameters (tracer composition, analytical platform, measurement selection etc.). Supported by an innovative visualization scheme, we demonstrate with two realistic showcases that the use of multiple criteria reveals deep insights into the conflicting interplay between information carriers and cost factors that are not amendable to single-objective ED. For instance, tandem mass spectrometry turns out as best-in-class with respect to information gain, while it delivers this information quality cheaper than the other, routinely applied analytical technologies. Therewith, our Pareto approach to ED offers the investigator great flexibilities in the conception phase of a study to balance costs and benefits.

An improved method for extraction of polar and charged metabolites from cyanobacteria.

A key requirement for 13C Metabolic flux analysis (13C-MFA), a widely used technique to estimate intracellular metabolic fluxes, is an efficient method for the extraction of intermediate metabolites for analysis via liquid chromatography mass spectrometry (LC/MS). The 13C isotopic labeling results in further distribution of an already sparse pool of intermediate metabolites into isotopologues, each appearing as a separate chromatographic feature. We examined some of the reported solvent systems for the extraction of polar intracellular metabolites from three strains of cyanobacteria of the genus Synechococcus, viz., Synechococcus sp. PCC 7002, Synechococcus elongatus PCC 7942, and a newly isolated Synechococcus elongatus PCC 11801 (manuscript under review). High resolution-LC/MS was used to assess the relative abundance of the extracted metabolites. The different solvent systems used for extraction led to statistically significant changes in the extraction efficiency for a large number of metabolites. While a few hundred m/z features or potential metabolites were detected with different solvent systems, the abundance of over a quarter of all metabolites varied significantly from one solvent system to another. Further, the extraction methods were evaluated for a targeted set of metabolites that are important in 13C-MFA studies of photosynthetic organisms. While for the strain PCC 7002, the reported method using methanol-chloroform-water system gave satisfactory results, a mild base in the form of NH4OH had to be used in place of water to achieve adequate levels of extraction for PCC 7942 and PCC 11801. While minor changes in extraction solvent resulted in dramatic changes in the extraction efficiency of a number of compounds, certain metabolites such as amino acids and organic acids were adequately extracted in all the solvent systems tested. Overall, we present a new improved method for extraction using a methanol-chloroform-NH4OH system. Our method improves the extraction of polar compounds such as sugar phosphates, bisphosphates, that are central to 13C-MFA studies.

Metabolism in dense microbial colonies: 13C metabolic flux analysis of E. coli grown on agar identifies two distinct cell populations with acetate cross-feeding

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 ΔackA 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.

Sugar phosphate analysis with baseline separation and soft ionization by gas chromatography-negative chemical ionization-mass spectrometry improves flux estimation of bidirectional reactions in cancer cells

Precise measurement of sugar phosphates in glycolysis and the pentose phosphate (PP) pathway for 13C-metabolic flux analysis (13C-MFA) is needed to understand cancer-specific metabolism. Although various analytical methods have been proposed, analysis of sugar phosphates is challenging because of the structural similarity of various isomers and low intracellular abundance. In this study, gas chromatography-negative chemical ionization-mass spectrometry (GC-NCI-MS) is applied to sugar phosphate analysis with o-(2,3,4,5,6-pentafluorobenzyl) oxime (PFBO) and trimethylsilyl (TMS) derivatization. Optimization of the GC temperature gradient achieved baseline separation of sugar phosphates in 31 min. Mass spectra showed the predominant generation of fragment ions containing all carbon atoms in the sugar phosphate backbone. The limit of detection of pentose 5-phosphates and hexose 6-phosphates was 10 nM. The method was applied to 13C-labeling measurement of sugar phosphates for 13C-MFA of the MCF-7 human breast cancer cell line. 13C-labeling of sugar phosphates for 13C-MFA improved the estimation of the net flux and reversible flux of bidirectional reactions in glycolysis and the PP pathway.

Engineering an in vivo EP-bifido pathway in Escherichia coli for high-yield acetyl-CoA generation with low CO2 emission

The low carbon yield from native metabolic machinery produces unfavorable process economics during the biological conversion of substrates to desirable bioproducts. To obtain higher carbon yields, we constructed a carbon conservation pathway named EP-bifido pathway in Escherichia coli by combining Embden-Meyerhof-Parnas Pathway, Pentose Phosphate Pathway and “bifid shunt”, to generate high yield acetyl-CoA from glucose. 13C-Metabolic flux analysis confirmed the successful and appropriate employment of the EP-bifido pathway. The CO2 release during fermentation significantly reduced compared with the control strains. Then we demonstrated the in vivo effectiveness of the EP-bifido pathway using poly-β-hydroxybutyrate (PHB), mevalonate and fatty acids as example products. The engineered EP-bifido strains showed greatly improved PHB yield (from 26.0 mol% to 63.7 mol%), fatty acid yield (from 9.17% to 14.36%), and the highest mevalonate yield yet reported (64.3 mol% without considering the substrates used for cell mass formation). The synthetic pathway can be employed in the production of chemicals that use acetyl-CoA as a precursor and can be extended to other microorganisms.

Abstract 4104: Evaluation of a novel LDH inhibitor efficacy in vivo in a glycolytic cancer model using hyperpolarized 13C magnetic resonance imaging

Abstract Increased conversion of pyruvate to lactate by Lactate Dehydrogenase A (LDHA) is a feature of many neoplasms. Therefore, LDHA inhibition is considered a promising approach toward developing a new therapeutic strategy against cancers expressing the Warburg phenotype. To develop this strategy for clinical use, a feasible and sensitive noninvasive imaging approach that can dynamically evaluate LDHA activity in vivo would be highly beneficial since in vitro metabolic profiling dose not always predict in vivo cancer metabolism. Hyperpolarized (HP) 13C Magnetic Resonance Imaging (MRI) can be used to perform dynamic 13C metabolic flux analysis in vivo. In particular, use of [1-13C]pyruvate (13C-pyr) with 13C MRI permits real-time monitoring of intratumoral LDHA activity through dynamic observation of conversion of 13C-pyr to [1-13C]lactate (13C-lac). This study aimed to apply 13C MRI technology in support of a therapeutic strategy to explore efficacy of a newly developed and highly potent LDH inhibitor (LDHi) in a glycolytic tumor model using MiaPaCa2 xenografts in mice. In vitro analysis showed that the LDHi dose-dependently inhibited human LDH activity and suppressed in vitro cell growth in MiaPaCa2 cells. By ex vivo assay, LDH activity in MiaPaCa2 xenografts was significantly suppressed (82.2 ± 5.6 % decrease compared to vehicle controls) following a single intravenous(IV) injection of 50 mg/kg LDHi. Next, 13C MRI imaging of HP 13C-pyr metabolism was performed before and after a single LDHi IV injection to assess inhibitor impact on intratumoral metabolic flux in vivo. 13C MR spectroscopy confirmed that LDHi suppressed intratumor LDHA activity dose- and time-dependently. The maximum effective dose was 50 mg/kg and inhibitor impact in the tumor persisted for 10-12 hrs after a single injection. In addition, lactate production in the tumor was suppressed 30 minutes after IV administration of LDHi, as was the 13C-lac to 13C-pyr ratio decreased by 83.3 ± 4.4 % compared to vehicle controls. Importantly, the close correlation of these data with the results of the ex vivo LDH activity assay, suggests that 13C MRI can reliably monitor in vivo on target effects of LDHi without need for tissue sampling. Based on the data using 13C MRI, we developed a therapeutic strategy for using LDHi in an efficacy study of MiaPaCa2 xenografts. Intermittent IV administration of LDHi significantly suppressed tumor growth in this glycolytic model. In conclusion, intratumoral inhibition of LDHA in vivo upon IV administration of a novel LDHi was readily visualized by HP 13C MRI, confirming the utility of this noninvasive method. This methodology can be of great value in developing new therapeutic strategies using LDHi and perhaps other metabolic inhibitors to treat cancers characterized by the Warburg phenotype. Further, HP 13C MRI should allow for selection of those patients likely to respond to such treatments. Citation Format: Nobu Oshima, Shun Kishimoto, Kristin Beebe, Dan Crooks, Michael Moses, Kazutoshi Yamamoto, Jeffry R. Brender, Anastasia Sowers, Ganesha Rai, Daniel Urban, Goria Benavides, Giuseppe Squadrito, Victor Darley-Usmar, Matthew Hall, James B. Mitchell, Murali C. Krishna, Leonard M. Neckers. Evaluation of a novel LDH inhibitor efficacy in vivo in a glycolytic cancer model using hyperpolarized 13C magnetic resonance imaging [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4104.