Interpreting metabolic complexity via isotope-assisted metabolic flux analysis

Isotope-assisted metabolic flux analysis (iMFA) is a powerful method to mathematically determine the metabolic fluxome from experimental isotope labeling data and a metabolic network model. While iMFA was originally developed for industrial biotechnological applications, it is increasingly used to analyze eukaryotic cell metabolism in physiological and pathological states. In this review, we explain how iMFA estimates the intracellular fluxome, including data and network model (inputs), the optimization-based data fitting (process), and the flux map (output). We then describe how iMFA enables analysis of metabolic complexities and discovery of metabolic pathways. Our goal is to expand the use of iMFA in metabolism research, which is essential to maximizing the impact of metabolic experiments and continuing to advance iMFA and biocomputational techniques.

Isotope-Assisted Metabolic Flux Analysis: A Powerful Technique to Gain New Insights into the Human Metabolome in Health and Disease

Cell metabolism represents the coordinated changes in genes, proteins, and metabolites that occur in health and disease. The metabolic fluxome, which includes both intracellular and extracellular metabolic reaction rates (fluxes), therefore provides a powerful, integrated description of cellular phenotype. However, intracellular fluxes cannot be directly measured. Instead, flux quantification requires sophisticated mathematical and computational analysis of data from isotope labeling experiments. In this review, we describe isotope-assisted metabolic flux analysis (iMFA), a rigorous computational approach to fluxome quantification that integrates metabolic network models and experimental data to generate quantitative metabolic flux maps. We highlight practical considerations for implementing iMFA in mammalian models, as well as iMFA applications in in vitro and in vivo studies of physiology and disease. Finally, we identify promising new frontiers in iMFA which may enable us to fully unlock the potential of iMFA in biomedical research.

Metabolic Flux Analysis-Linking Isotope Labeling and Metabolic Fluxes

Metabolic flux analysis (MFA) is an increasingly important tool to study metabolism quantitatively. Unlike the concentrations of metabolites, the fluxes, which are the rates at which intracellular metabolites interconvert, are not directly measurable. MFA uses stable isotope labeled tracers to reveal information related to the fluxes. The conceptual idea of MFA is that in tracer experiments the isotope labeling patterns of intracellular metabolites are determined by the fluxes, therefore by measuring the labeling patterns we can infer the fluxes in the network. In this review, we will discuss the basic concept of MFA using a simplified upper glycolysis network as an example. We will show how the fluxes are reflected in the isotope labeling patterns. The central idea we wish to deliver is that under metabolic and isotopic steady-state the labeling pattern of a metabolite is the flux-weighted average of the substrates’ labeling patterns. As a result, MFA can tell the relative contributions of converging metabolic pathways only when these pathways make substrates in different labeling patterns for the shared product. This is the fundamental principle guiding the design of isotope labeling experiment for MFA including tracer selection. In addition, we will also discuss the basic biochemical assumptions of MFA, and we will show the flux-solving procedure and result evaluation. Finally, we will highlight the link between isotopically stationary and nonstationary flux analysis.

Modeling Plant Metabolism: From Network Reconstruction to Mechanistic Models

Mathematical modeling of plant metabolism enables the plant science community to understand the organization of plant metabolism, obtain quantitative insights into metabolic functions, and derive engineering strategies for manipulation of metabolism. Among the various modeling approaches, metabolic pathway analysis can dissect the basic functional modes of subsections of core metabolism, such as photorespiration, and reveal how classical definitions of metabolic pathways have overlapping functionality. In the many studies using constraint-based modeling in plants, numerous computational tools are currently available to analyze large-scale and genome-scale metabolic networks. For ¹³C-metabolic flux analysis, principles of isotopic steady state have been used to study heterotrophic plant tissues, while nonstationary isotope labeling approaches are amenable to the study of photoautotrophic and secondary metabolism. Enzyme kinetic models explore pathways in mechanistic detail, and we discuss different approaches to determine or estimate kinetic parameters. In this review, we describe recent advances and challenges in modeling plant metabolism.

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.

Designing experiments is obligatory in the biosciences to valorize their scientific outcome. When the experiments are expensive, unfortunately, in practice often the costs emerge to be showstoppers. In this situation the question arises: How to get the most out of the experiment for your invest in terms of time and money? We approach this question by formulating the design task as a multiple-criteria optimization problem. Its solution produces a set of Pareto-optimal design proposals that feature the trade-off between information gain, as measured by different metrics, and the costs. Then, exploration of the design proposals allows us to make the best decision on information-economic experiments under given circumstances. Implemented in the field of isotope-based metabolic flux analysis, practical application of the Pareto approach provides detailed insight into the tight interplay of plenty of information carriers and cost factors. Supported by an innovative tailored visual representation scheme, the investigator is enabled to explore the options before conducting the experiment. With a practical showcase at hand, our computational study highlights the benefits of incorporating multiple information criteria apart from the costs, balancing the shortcomings of conventional single-objective experimental design strategies.

Concurrent isotope-assisted metabolic flux analysis and transcriptome profiling reveal responses of poplar cells to altered nitrogen and carbon supply

Reduced nitrogen is indispensable to plants. However, its limited availability in soil combined with the energetic and environmental impacts of nitrogen fertilizers motivates research into molecular mechanisms toward improving plant nitrogen use efficiency (NUE). We performed a systems-level investigation of this problem by employing multiple 'omics methodologies on cell suspensions of hybrid poplar (Populus tremula × Populus alba). Acclimation and growth of the cell suspensions in four nutrient regimes ranging from abundant to deficient supplies of carbon and nitrogen revealed that cell growth under low-nitrogen levels was associated with substantially higher NUE. To investigate the underlying metabolic and molecular mechanisms, we concurrently performed steady-state ¹³ C metabolic flux analysis with multiple isotope labels and transcriptomic profiling with cDNA microarrays. The ¹³ C flux analysis revealed that the absolute flux through the oxidative pentose phosphate pathway (oxPPP) was substantially lower (~threefold) under low-nitrogen conditions. Additionally, the flux partitioning ratio between the tricarboxylic acid cycle and anaplerotic pathways varied from 84%:16% under abundant carbon and nitrogen to 55%:45% under deficient carbon and nitrogen. Gene expression data, together with the flux results, suggested a plastidic localization of the oxPPP as well as transcriptional regulation of certain metabolic branchpoints, including those between glycolysis and the oxPPP. The transcriptome data also indicated that NUE-improving mechanisms may involve a redirection of excess carbon to aromatic metabolic pathways and extensive downregulation of potentially redundant genes (in these heterotrophic cells) that encode photosynthetic and light-harvesting proteins, suggesting the recruitment of these proteins as nitrogen sinks in nitrogen-abundant conditions.

Rapid in situ 13C tracing of sucrose utilization in Arabidopsis sink and source leaves

BACKGROUND

Conventional metabolomics approaches face the problem of hidden metabolic phenotypes where only fluxes are altered but pool sizes stay constant. Metabolic flux experiments are used to detect such hidden flux phenotypes. These experiments are, however, time consuming, may be cost intensive, and involve specialists for modeling. We fill the gap between conventional metabolomics and flux modeling. We present rapid stable isotope tracing assays and analysis strategies of ¹³C labeling data. For this purpose, we combine the conventional metabolomics approach that detects significant relative changes of metabolite pool sizes with analyses of differential utilization of ¹³C labeled carbon. As a test case, we use uniformly labeled ¹³C-sucrose.

RESULTS

We present petiole and hypocotyl feeding assays for the rapid in situ feeding (≤ 4 h) of isotopically labeled metabolic precursor to whole Arabidopsis thaliana rosettes. The assays are assessed by conventional gas chromatography-mass spectrometry based metabolite profiling that was extended by joined differential analysis of ¹³C-labeled sub-pools and of ¹³C enrichment of metabolites relative to the enrichment of ¹³C-sucrose within each sample. We apply these analyses to the sink to source transition continuum of leaves from single A. thaliana rosettes and characterize the associated relative changes of metabolite pools, as well as previously hidden changes of sucrose-derived carbon partitioning. We compared the contribution of sucrose as a carbon source in predominantly sink to predominantly source leaves and identified a set of primary metabolites with differential carbon utilization during sink to source transition.

CONCLUSION

The presented feeding assays and data evaluation strategies represent a rapid and easy-to-use tool box for enhanced metabolomics studies that combine differential pool size analysis with screening for differential carbon utilization from defined stable isotope labeled metabolic precursors.

Investigation of useful carbon tracers for 13C-metabolic flux analysis of Escherichia coli by considering five experimentally determined flux distributions

The ¹³C-MFA experiments require an optimal design since the precision or confidence intervals of the estimated flux levels depends on factors such as the composition of ¹³C-labeled carbon sources, as well as the metabolic flux distribution of interest. In this study, useful compositions of ¹³C-labeled glucose for ¹³C-metabolic flux analysis (¹³C-MFA) of Escherichia coli are investigated using a computer simulation of the stable isotope labeling experiment. Following the generation of artificial mass spectra datasets of amino acid fragments using five literature-reported flux distributions of E. coli, the best fitted flux distribution and the 95% confidence interval were estimated by the ¹³C-MFA procedure. A comparison of the precision scores showed that [1, 2-¹³C]glucose and a mixture of [1-¹³C] and [U-¹³C]glucose at 8:2 are one of the best carbon sources for a precise estimation of flux levels of the pentose phosphate pathway, glycolysis and the TCA cycle. Although the precision scores of the anaplerotic and glyoxylate pathway reactions were affected by both the carbon source and flux distribution, it was also shown that the mixture of non-labeled, [1-¹³C], and [U-¹³C]glucose at 4:1:5 was specifically effective for the flux estimation of the glyoxylate pathway reaction. These findings were confirmed by wet ¹³C-MFA experiments.

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Useful compositions of ¹³C-labeled glucose are investigated for ¹³C-MFA of E. coli.

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Computer simulations revealed that [1,2-¹³C] was one of the best first choices.

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Mixture of non-labeled, [1-¹³C] and [U-¹³C] at 0:8:2 was also suitable for ¹³C-MFA.

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Mixture at 4:1:5 was specifically effective for estimation of glyoxylate pathway.

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The wet ¹³C-MFA experiments of E. coli confirmed the findings.

Assessing compartmentalized flux in lipid metabolism with isotopes

Metabolism in plants takes place across multiple cell types and within distinct organelles. The distributions equate to spatial heterogeneity; though the limited means to experimentally assess metabolism frequently involve homogenizing tissues and mixing metabolites from different locations. Most current isotope investigations of metabolism therefore lack the ability to resolve spatially distinct events. Recognition of this limitation has resulted in inspired efforts to advance metabolic flux analysis and isotopic labeling techniques. Though a number of these efforts have been applied to studies in central metabolism; recent advances in instrumentation and techniques present an untapped opportunity to make similar progress in lipid metabolism where the use of stable isotopes has been more limited. These efforts will benefit from sophisticated radiolabeling reports that continue to enrich our knowledge on lipid biosynthetic pathways and provide some direction for stable isotope experimental design and extension of MFA. Evidence for this assertion is presented through the review of several elegant stable isotope studies and by taking stock of what has been learned from radioisotope investigations when spatial aspects of metabolism were considered. The studies emphasize that glycerolipid production occurs across several locations with assembly of lipids in the ER or plastid, fatty acid biosynthesis occurring in the plastid, and the generation of acetyl-CoA and glycerol-3-phosphate taking place at multiple sites. Considering metabolism in this context underscores the cellular and subcellular organization that is important to enhanced production of glycerolipids in plants. An attempt is made to unify salient features from a number of reports into a diagrammatic model of lipid metabolism and propose where stable isotope labeling experiments and further flux analysis may help address questions in the field. This article is part of a Special Issue entitled: Plant Lipid Biology edited by Kent D. Chapman and Ivo Feussner.

Tracking the metabolic pulse of plant lipid production with isotopic labeling and flux analyses: Past, present and future

Metabolism is comprised of networks of chemical transformations, organized into integrated biochemical pathways that are the basis of cellular operation, and function to sustain life. Metabolism, and thus life, is not static. The rate of metabolites transitioning through biochemical pathways (i.e., flux) determines cellular phenotypes, and is constantly changing in response to genetic or environmental perturbations. Each change evokes a response in metabolic pathway flow, and the quantification of fluxes under varied conditions helps to elucidate major and minor routes, and regulatory aspects of metabolism. To measure fluxes requires experimental methods that assess the movements and transformations of metabolites without creating artifacts. Isotopic labeling fills this role and is a long-standing experimental approach to identify pathways and quantify their metabolic relevance in different tissues or under different conditions. The application of labeling techniques to plant science is however far from reaching it potential. In light of advances in genetics and molecular biology that provide a means to alter metabolism, and given recent improvements in instrumentation, computational tools and available isotopes, the use of isotopic labeling to probe metabolism is becoming more and more powerful. We review the principal analytical methods for isotopic labeling with a focus on seminal studies of pathways and fluxes in lipid metabolism and carbon partitioning through central metabolism. Central carbon metabolic steps are directly linked to lipid production by serving to generate the precursors for fatty acid biosynthesis and lipid assembly. Additionally some of the ideas for labeling techniques that may be most applicable for lipid metabolism in the future were originally developed to investigate other aspects of central metabolism. We conclude by describing recent advances that will play an important future role in quantifying flux and metabolic operation in plant tissues.

OpenFLUX2: (13)C-MFA modeling software package adjusted for the comprehensive analysis of single and parallel labeling experiments

BACKGROUND

Steady-state (13)C-based metabolic flux analysis ((13)C-MFA) is the most powerful method available for the quantification of intracellular fluxes. These analyses include concertedly linked experimental and computational stages: (i) assuming the metabolic model and optimizing the experimental design; (ii) feeding the investigated organism using a chosen (13)C-labeled substrate (tracer); (iii) measuring the extracellular effluxes and detecting the (13)C-patterns of intracellular metabolites; and (iv) computing flux parameters that minimize the differences between observed and simulated measurements, followed by evaluating flux statistics. In its early stages, (13)C-MFA was performed on the basis of data obtained in a single labeling experiment (SLE) followed by exploiting the developed high-performance computational software. Recently, the advantages of parallel labeling experiments (PLEs), where several LEs are conducted under the conditions differing only by the tracer(s) choice, were demonstrated, particularly with regard to improving flux precision due to the synergy of complementary information. The availability of an open-source software adjusted for PLE-based (13)C-MFA is an important factor for PLE implementation.

RESULTS

The open-source software OpenFLUX, initially developed for the analysis of SLEs, was extended for the computation of PLE data. Using the OpenFLUX2, in silico simulation confirmed that flux precision is improved when (13)C-MFA is implemented by fitting PLE data to the common model compared with SLE-based analysis. Efficient flux resolution could be achieved in the PLE-mediated analysis when the choice of tracer was based on an experimental design computed to minimize the flux variances from different parts of the metabolic network. The analysis provided by OpenFLUX2 mainly includes (i) the optimization of the experimental design, (ii) the computation of the flux parameters from LEs data, (iii) goodness-of-fit testing of the model's adequacy, (iv) drawing conclusions concerning the identifiability of fluxes and construction of a contribution matrix reflecting the relative contribution of the measurement variances to the flux variances, and (v) precise determination of flux confidence intervals using a fine-tunable and convergence-controlled Monte Carlo-based method.

CONCLUSIONS

The developed open-source OpenFLUX2 provides a friendly software environment that facilitates beginners and existing OpenFLUX users to implement LEs for steady-state (13)C-MFA including experimental design, quantitative evaluation of flux parameters and statistics.

Steady-state and instationary modeling of proteinogenic and free amino acid isotopomers for flux quantification

Metabolic flux analysis (MFA) is a powerful tool for exploring and quantifying carbon traffic in metabolic networks. Accurate flux quantification requires (1) high-quality isotopomer measurements, usually of biomass components including proteinogenic/free amino acids or central carbon metabolites, and (2) a mathematical model that relates the unknown fluxes to the measured isotopomers. Modeling requires a thorough knowledge of the structure of the underlying metabolic network, often available from many databases, as well as the ability to make reasonable assumptions that will enable simplification of the model. Here we describe a general methodology underlying computer-aided mathematical modeling of a flux-isotopomer relationship and some of the accompanying data-processing steps. One of two modeling strategies will need to be employed, depending on the type of isotope labeling experiment performed. These strategies-steady-state modeling and instationary modeling-have different experimental and computational demands. We discuss the concepts underlying these two types of modeling and demonstrate steady-state modeling in a step-by-step manner. Our methodology should be applicable to most isotope-assisted MFA applications and should serve as a general framework applicable to many realistic metabolic networks with little modification.

Flux and reflux: metabolite reflux in plant suspension cells and its implications for isotope-assisted metabolic flux analysis

Isotope-assisted metabolic flux analysis (MFA) is a powerful methodology to quantify intracellular fluxes via isotope labeling experiments (ILEs). In batch cultures, which are often convenient, inexpensive or inevitable especially for eukaryotic systems, MFA is complicated by the presence of the initially present biomass. This unlabeled biomass may either mix with the newly synthesized labeled biomass or reflux into the metabolic network, thus masking the true labeling patterns in the newly synthesized biomass. Here, we report a detailed investigation of such metabolite reflux in cell suspensions of the tree poplar. In ILEs supplying 28% or 98% U-(13)C glucose as the sole organic carbon source, biomass components exhibited lower (13)C enrichments than the supplied glucose as well as anomalous isotopomers not explainable by simple mixing of the initial and newly synthesized biomass. These anomalous labeling patterns were most prominent in a 98% U-(13)C glucose ILE. By comparing the performance of light- and dark-grown cells as well as by analyzing the isotope labeling patterns in aspartic and glutamic acids, we eliminated photosynthetic or anaplerotic fixation of extracellular (12)CO2 as explanations for the anomalous labeling patterns. We further investigated four different metabolic models for interpreting the labeling patterns and evaluating fluxes: (i) a carbon source (glucose) dilution model, (ii) an isotopomer correction model with uniform dilution for all amino acids, (iii) an isotopomer correction model with variable dilution for different amino acids, and (iv) a comprehensive metabolite reflux model. Of these, the metabolite reflux model provided a substantially better fit for the observed labeling patterns (sum of squared residues: 538) than the other three models whose sum of squared residues were (i) 4626, (ii) 4983, and (iii) 1748, respectively. We compared fluxes determined using the metabolite reflux model to those determined using an independent methodology involving an excessively long ILE to wash out initial biomass and a minimal reflux model. This comparison showed identical or similar distributions for a majority of fluxes, thus validating our comprehensive reflux model. In summary, we have demonstrated the need for quantifying interactions between initially present biomass and newly synthesized biomass in batch ILEs, especially through the use of ≈100% U-(13)C carbon sources. Our ILEs reveal a high amount of metabolite reflux in poplar cell suspensions, which is well explained by a comprehensive metabolite reflux model.

Quantification of peptide m/z distributions from 13C-labeled cultures with high-resolution mass spectrometry

Isotopic labeling studies of primary metabolism frequently utilize GC/MS to quantify (13)C in protein-hydrolyzed amino acids. During processing some amino acids are degraded, which reduces the size of the measurement set. The advent of high-resolution mass spectrometers provides a tool to assess molecular masses of peptides with great precision and accuracy and computationally infer information about labeling in amino acids. Amino acids that are isotopically labeled during metabolism result in labeled peptides that contain spatial and temporal information that is associated with the biosynthetic origin of the protein. The quantification of isotopic labeling in peptides can therefore provide an assessment of amino acid metabolism that is specific to subcellular, cellular, or temporal conditions. A high-resolution orbital trap was used to quantify isotope labeling in peptides that were obtained from unlabeled and isotopically labeled soybean embryos and Escherichia coli cultures. Standard deviations were determined by estimating the multinomial variance associated with each element of the m/z distribution. Using the estimated variance, quantification of the m/z distribution across multiple scans was achieved by a nonlinear fitting approach. Observed m/z distributions of uniformly labeled E. coli peptides indicated no significant differences between observed and simulated m/z distributions. Alternatively, amino acid m/z distributions obtained from GC/MS were convolved to simulate peptide m/z distributions but resulted in distinct profiles due to the production of protein prior to isotopic labeling. The results indicate that peptide mass isotopologue measurements faithfully represent mass distributions, are suitable for quantification of isotope-labeling-based studies, and provide additional information over existing methods.

Metabolic flux analysis using ¹³C peptide label measurements

¹³C metabolic flux analysis (MFA) has become the experimental method of choice to investigate the cellular metabolism of microbes, cell cultures and plant seeds. Conventional steady-state MFA utilizes isotopic labeling measurements of amino acids obtained from protein hydrolysates. To retain spatial information in conventional steady-state MFA, tissues or subcellular fractions must be dissected or biochemically purified. In contrast, peptides retain their identity in complex protein extracts, and may therefore be associated with a specific time of expression, tissue type and subcellular compartment. To enable 'single-sample' spatially and temporally resolved steady-state flux analysis, we investigated the suitability of peptide mass distributions (PMDs) as an alternative to amino acid label measurements. PMDs are the discrete convolution of the mass distributions of the constituent amino acids of a peptide. We investigated the requirements for the unique deconvolution of PMDs into amino acid mass distributions (AAMDs), the influence of peptide sequence length on parameter sensitivity, and how AAMD and flux estimates that are determined through deconvolution compare to estimates from a conventional GC-MS measurement-based approach. Deconvolution of PMDs of the storage protein β-conglycinin of soybean (Glycine max) resulted in good AAMD and flux estimates if fluxes were directly fitted to PMDs. Unconstrained deconvolution resulted in inferior AAMD and flux estimates. PMD measurements do not include amino acid backbone fragments, which increase the information content in GC-MS-derived analyses. Nonetheless, the resulting flux maps were of comparable quality due to the precision of Orbitrap quantification and the larger number of peptide measurements.

Mathematical modeling of isotope labeling experiments for metabolic flux analysis

Isotope labeling experiments (ILEs) offer a powerful methodology to perform metabolic flux analysis. However, the task of interpreting data from these experiments to evaluate flux values requires significant mathematical modeling skills. Toward this, this chapter provides background information and examples to enable the reader to (1) model metabolic networks, (2) simulate ILEs, and (3) understand the optimization and statistical methods commonly used for flux evaluation. A compartmentalized model of plant glycolysis and pentose phosphate pathway illustrates the reconstruction of a typical metabolic network, whereas a simpler example network illustrates the underlying metabolite and isotopomer balancing techniques. We also discuss the salient features of commonly used flux estimation software 13CFLUX2, Metran, NMR2Flux+, FiatFlux, and OpenFLUX. Furthermore, we briefly discuss methods to improve flux estimates. A graphical checklist at the end of the chapter provides a reader a quick reference to the mathematical modeling concepts and resources.

Experimental evidence and isotopomer analysis of mixotrophic glucose metabolism in the marine diatom Phaeodactylum tricornutum

BACKGROUND

Heterotrophic fermentation using simple sugars such as glucose is an established and cost-effective method for synthesizing bioproducts from bacteria, yeast and algae. Organisms incapable of metabolizing glucose have limited applications as cell factories, often despite many other advantageous characteristics. Therefore, there is a clear need to investigate glucose metabolism in potential cell factories. One such organism, with a unique metabolic network and a propensity to synthesize highly reduced compounds as a large fraction of its biomass, is the marine diatom Phaeodactylum tricornutum (Pt). Although Pt has been engineered to metabolize glucose, conflicting lines of evidence leave it unresolved whether Pt can natively consume glucose.

RESULTS

Isotope labeling experiments in which Pt was mixotrophically grown under light on 100% U-(13)C glucose and naturally abundant (~99% (12)C) dissolved inorganic carbon resulted in proteinogenic amino acids with an average 13C-enrichment of 88%, thus providing convincing evidence of glucose uptake and metabolism. The dissolved inorganic carbon was largely incorporated through anaplerotic rather than photosynthetic fixation. Furthermore, an isotope labeling experiment utilizing 1-(13)C glucose and subsequent metabolic pathway analysis indicated that (i) the alternative Entner-Doudoroff and Phosphoketolase glycolytic pathways are active during glucose metabolism, and (ii) during mixotrophic growth, serine and glycine are largely synthesized from glyoxylate through photorespiratory reactions rather than from 3-phosphoglycerate. We validated the latter result for mixotrophic growth on glycerol by performing a 2-(13)C glycerol isotope labeling experiment. Additionally, gene expression assays showed that known, native glucose transporters in Pt are largely insensitive to glucose or light, whereas the gene encoding cytosolic fructose bisphosphate aldolase 3, an important glycolytic enzyme, is overexpressed in light but insensitive to glucose.

CONCLUSION

We have shown that Pt can use glucose as a primary carbon source when grown in light, but cannot use glucose to sustain growth in the dark. We further analyzed the metabolic mechanisms underlying the mixotrophic metabolism of glucose and found isotopic evidence for unusual pathways active in Pt. These insights expand the envelope of Pt cultivation methods using organic substrates. We anticipate that they will guide further engineering of Pt towards sustainable production of fuels, pharmaceuticals, and platform chemicals.

Publishing 13C metabolic flux analysis studies: a review and future perspectives

(13)C-Metabolic flux analysis ((13)C-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 (13)C-MFA studies published each year, it is now ever more important to provide practical guidelines for performing and publishing (13)C-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 (13)C-MFA, which can eventually be used as minimum data standards for publishing (13)C-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 (13)C-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 (13)C-MFA field and highlight some of the shortcomings with regards to (13)C-MFA publications. We then propose a checklist that encompasses good practices in (13)C-MFA. We hope that these guidelines will be a valuable resource for the community and allow (13)C-flux studies to be more easily reproduced and accessed by others in the future.

COMPLETE-MFA: complementary parallel labeling experiments technique for metabolic flux analysis

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-(13)C], [2-(13)C], [3-(13)C], [4-(13)C], [5-(13)C], and [6-(13)C]glucose to determine precise metabolic fluxes for wild-type Escherichia 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 (13)C-flux analysis.

Metabolic analyses elucidate non-trivial gene targets for amplifying dihydroartemisinic acid production in yeast

Synthetic biology enables metabolic engineering of industrial microbes to synthesize value-added molecules. In this, a major challenge is the efficient redirection of carbon to the desired metabolic pathways. Pinpointing strategies toward this goal requires an in-depth investigation of the metabolic landscape of the organism, particularly primary metabolism, to identify precursor and cofactor availability for the target compound. The potent antimalarial therapeutic artemisinin and its precursors are promising candidate molecules for production in microbial hosts. Recent advances have demonstrated the production of artemisinin precursors in engineered yeast strains as an alternative to extraction from plants. We report the application of in silico and in vivo metabolic pathway analyses to identify metabolic engineering targets to improve the yield of the direct artemisinin precursor dihydroartemisinic acid (DHA) in yeast. First, in silico extreme pathway (ExPa) analysis identified NADPH-malic enzyme and the oxidative pentose phosphate pathway (PPP) as mechanisms to meet NADPH demand for DHA synthesis. Next, we compared key DHA-synthesizing ExPas to the metabolic flux distributions obtained from in vivo (13)C metabolic flux analysis of a DHA-synthesizing strain. This comparison revealed that knocking out ethanol synthesis and overexpressing glucose-6-phosphate dehydrogenase in the oxidative PPP (gene YNL241C) or the NADPH-malic enzyme ME2 (YKL029C) are vital steps toward overproducing DHA. Finally, we employed in silico flux balance analysis and minimization of metabolic adjustment on a yeast genome-scale model to identify gene knockouts for improving DHA yields. The best strategy involved knockout of an oxaloacetate transporter (YKL120W) and an aspartate aminotransferase (YKL106W), and was predicted to improve DHA yields by 70-fold. Collectively, our work elucidates multiple non-trivial metabolic engineering strategies for improving DHA yield in yeast.