13C Metabolic Flux Analysis Research Articles

Evaluation of freely available software tools for untargeted quantification of 13C isotopic enrichment in cellular metabolome from HR-LC/MS data

13C Metabolic Flux Analysis (13C-MFA) involves the quantification of isotopic enrichment in cellular metabolites and fitting the resultant data to the metabolic network model of the organism. Coverage and resolution of the resultant flux map depends on the total number of metabolites and fragments in which 13C enrichment can be quantified accurately. Experimental techniques for tracking 13C enrichment are evolving rapidly and large volumes of data are now routinely generated through the use of Liquid Chromatography coupled with High-Resolution Mass Spectrometry (HR-LC/MS). Therefore, the current manuscript is focused on the challenges in high-throughput analyses of such large datasets. Current 13C-MFA studies often have to rely on the targeted quantification of a small subset of metabolites, thereby leaving a large fraction of the data unexplored. A number of public domain software tools have been reported in recent years for the untargeted quantitation of isotopic enrichment. However, the suitability of their application across diverse datasets has not been investigated. Here, we test the software tools X13CMS, DynaMet, geoRge, and HiResTEC with three diverse datasets. The tools provided a global, untargeted view of 13C enrichment in metabolites in all three datasets and a much-needed automation in data analysis. Some inconsistencies were observed in results obtained from the different tools, which could be partially ascribed to the lack of baseline separation and potential mass conflicts. After removing the false positives manually, isotopic enrichment could be quantified reliably in a large repertoire of metabolites. Of the software tools explored, geoRge and HiResTEC consistently performed well for the untargeted analysis of all datasets tested.

13C-Metabolic Flux Analysis in Developing Flax (Linum usitatissinum L.) Embryos to Understand Storage Lipid Biosynthesis.

Flax (Linum usitatissinum L.) oil is an important source of α-linolenic (C18:3 ω-3). This polyunsaturated fatty acid is well known for its nutritional role in human and animal diets. Understanding storage lipid biosynthesis in developing flax embryos can lead to an increase in seed yield via marker-assisted selection. While a tremendous amount of work has been done on different plant species to highlight their metabolism during embryo development, a comprehensive analysis of metabolic flux in flax is still lacking. In this context, we have utilized in vitro cultured developing embryos of flax and determined net fluxes by performing three complementary parallel labeling experiments with 13C-labeled glucose and glutamine. Metabolic fluxes were estimated by computer-aided modeling of the central metabolic network including 11 cofactors of 118 reactions of the central metabolism and 12 pseudo-fluxes. A focus on lipid storage biosynthesis and the associated pathways was done in comparison with rapeseed, arabidopsis, maize and sunflower embryos. In our hands, glucose was determined to be the main source of carbon in flax embryos, leading to the conversion of phosphoenolpyruvate to pyruvate. The oxidative pentose phosphate pathway (OPPP) was identified as the producer of NADPH for fatty acid biosynthesis. Overall, the use of 13C-metabolic flux analysis provided new insights into the flax embryo metabolic processes involved in storage lipid biosynthesis. The elucidation of the metabolic network of this important crop plant reinforces the relevance of the application of this technique to the analysis of complex plant metabolic systems.

Repression of mitochondrial metabolism for cytosolic pyruvate-derived chemical production in Saccharomyces cerevisiae

BackgroundSaccharomyces cerevisiae is a suitable host for the industrial production of pyruvate-derived chemicals such as ethanol and 2,3-butanediol (23BD). For the improvement of the productivity of these chemicals, it is essential to suppress the unnecessary pyruvate consumption in S. cerevisiae to redirect the metabolic flux toward the target chemical production. In this study, mitochondrial pyruvate transporter gene (MPC1) or the essential gene for mitophagy (ATG32) was knocked-out to repress the mitochondrial metabolism and improve the production of pyruvate-derived chemical in S. cerevisiae.ResultsThe growth rates of both aforementioned strains were 1.6-fold higher than that of the control strain. 13C-metabolic flux analysis revealed that both strains presented similar flux distributions and successfully decreased the tricarboxylic acid cycle fluxes by 50% compared to the control strain. Nevertheless, the intracellular metabolite pool sizes were completely different, suggesting distinct metabolic effects of gene knockouts in both strains. This difference was also observed in the test-tube culture for 23BD production. Knockout of ATG32 revealed a 23.6-fold increase in 23BD titer (557.0 ± 20.6 mg/L) compared to the control strain (23.5 ± 12.8 mg/L), whereas the knockout of MPC1 revealed only 14.3-fold increase (336.4 ± 113.5 mg/L). Further investigation using the anaerobic high-density fermentation test revealed that the MPC1 knockout was more effective for ethanol production than the 23BD production.ConclusionThese results suggest that the engineering of the mitochondrial transporters and membrane dynamics were effective in controlling the mitochondrial metabolism to improve the productivities of chemicals in yeast cytosol.

Metabolic perturbations in mutants of glucose transporters and their applications in metabolite production in Escherichia coli

BackgroundMost microorganisms have evolved to maximize growth rate, with rapid consumption of carbon sources from the surroundings. However, fast growing phenotypes usually feature secretion of organic compounds. For example, E. coli mainly produced acetate in fast growing condition such as glucose rich and aerobic condition, which is troublesome for metabolic engineering because acetate causes acidification of surroundings, growth inhibition and decline of production yield. The overflow metabolism can be alleviated by reducing glucose uptake rate.ResultsAs glucose transporters or their subunits were knocked out in E. coli, the growth and glucose uptake rates decreased and biomass yield was improved. Alteration of intracellular metabolism caused by the mutations was investigated with transcriptome analysis and 13C metabolic flux analysis (13C MFA). Various transcriptional and metabolic perturbations were identified in the sugar transporter mutants. Transcription of genes related to glycolysis, chemotaxis, and flagella synthesis was downregulated, and that of gluconeogenesis, Krebs cycle, alternative transporters, quorum sensing, and stress induced proteins was upregulated in the sugar transporter mutants. The specific production yields of value-added compounds (enhanced green fluorescent protein, γ-aminobutyrate, lycopene) were improved significantly in the sugar transporter mutants.ConclusionsThe elimination of sugar transporter resulted in alteration of global gene expression and redirection of carbon flux distribution, which was purposed to increase energy yield and recycle carbon sources. When the pathways for several valuable compounds were introduced to mutant strains, specific yield of them were highly improved. These results showed that controlling the sugar uptake rate is a good strategy for ameliorating metabolite production.

Separation of 13C- and 15N-Isotopologues of Amino Acids with a Primary Amine without Mass Resolution by Means of O-Phthalaldehyde Derivatization and Collision Induced Dissociation.

Computational and experimental advances of recent years have culminated in establishing 13C-Metabolic Flux Analysis (13C-MFA) as a routine methodology to unravel the fluxome. As the acronym suggests, 13C-MFA has relied on the relative abundance of 13C-isotopes in metabolites for flux inference, most commonly measured by mass spectrometry. In this manuscript we expand the scope of labeling measurements to the case of simultaneous 13C- and 15N-labeling of amino acids. Analytically, the separation of isotopologues of this metabolite class can only be achieved at resolving power beyond 65,000. In this manuscript we harvest an overlooked property of the collision induced dissociation of amino acid adducts to discern 13C- and 15N- isotopologues of amino acids with a primary amine without separating them in the m/z domain.

A Combined Metabolomics and Fluxomics Analysis Identifies Steps Limiting Oil Synthesis in Maize Embryos.

Enhancing fatty acid synthesis (FAS) in maize (Zea mays) has tremendous potential nutritional and economic benefits due to the rapidly growing demand for vegetable oil. In maize kernels, the endosperm and the embryo are the main sites for synthesis and accumulation of starch and oil, respectively. So far, breeding efforts to achieve elevated oil content in maize have resulted in smaller endosperms and therefore lower yield. Directly changing their carbon metabolism may be the key to increasing oil content in maize kernels without affecting yield. To test this hypothesis, the intracellular metabolite levels were compared in maize embryos from two different maize lines, ALEXHO S K SYNTHETIC (Alex) and LH59, which accumulate 48% and 34% of oil, respectively. Comparative metabolomics highlighted the metabolites and pathways that were active in the embryos and important for oil production. The contribution of each pathway to FAS in terms of carbon, reductant, and energy provision was assessed by measuring the carbon flow through the metabolic network (13C-metabolic flux analysis) in developing Alex embryos to build a map of carbon flow through the central metabolism. This approach combined mathematical modeling with biochemical quantification to identify metabolic bottlenecks in FAS in maize embryos. This study describes a combination of innovative tools that will pave the way for controlling seed composition in important food crops.

Fluxomic Analysis Reveals Central Carbon Metabolism Adaptation for Diazotroph Azotobacter vinelandii Ammonium Excretion

Diazotrophic bacteria are an attractive biological alternative to synthetic nitrogen fertilizers due to their remarkable capacity to fix atmospheric nitrogen gas to ammonium via nitrogenase enzymes. However, how diazotrophic bacteria tailor central carbon catabolism to accommodate the energy requirement for nitrogenase activity is largely unknown. In this study, we used Azotobacter vinelandii DJ and an ammonium excreting mutant, AV3 (ΔNifL), to investigate central carbon metabolism fluxes and central cell bioenergetics in response to ammonium availability and nitrogenase activity. Enabled by the powerful and reliable methodology of 13C-metabolic flux analysis, we show that the respiratory TCA cycle is upregulated in association with increased nitrogenase activity and causes a monotonic decrease in specific growth rate. Whereas the activity of the glycolytic Entner–Doudoroff pathway is positively correlated with the cell growth rate. These new observations are formulated into a 13C-metabolic flux model which further improves the understanding and interpretation of intracellular bioenergetics. This analysis leads to the conclusion that, under aerobic conditions, respiratory TCA metabolism is responsible for the supply of additional ATP and reducing equivalents required for elevated nitrogenase activity. This study provides a quantitative relationship between central carbon and nitrogen metabolism in an aerobic diazotroph for the first time.

13C metabolic flux analysis on roles of malate transporter in lipid accumulation of Mucor circinelloides

BackgroundMitochondrial and cytoplasmic malate transporter proteins are responsible for transmembrane transport of malate, thereby linking malate metabolism in various subcellular regions of the cell. These transporters play an important role in fatty acid biosynthesis of oleaginous microorganisms. Our previous studies have found that lipid content of the recombinant Mucor circinelloides (M. circinelloides) strain with mitochondrial malate transporter (mt) gene overexpression was increased by 70%, while that of strain with mt gene knockout was decreased by 27%. However, the mechanism of malate transporter promoting the transport of mitochondrial malate and citrate related to lipid accumulation is not clear. Therefore, 13C-labeled glucose metabolic flux analysis was carried out to identify the metabolic network topology and estimate intracellular fluxes of genetically engineered M. circinelloides strains for the purpose of better understanding the roles of malate transporters in citrate transport systems and lipid accumulation.ResultsThe metabolic flux distribution analysis suggested that tricarboxylic acid (TCA) cycle flux ratio of mt-overexpression strains was decreased compared to that of the control strain, but in contrast, glyoxylic acid (GOX) cycle flux ratio was increased. Accordingly, the mt-knockout strain showed an opposite phenomenon with a higher TCA cycle flux ratio and a lower GOX cycle flux ratio than the control strain. GOX cycle might be more effective than TCA cycle in producing malate and oxaloacetate replenishment. Moreover, a relatively higher flux ratio of the pentose phosphate (PP) pathway was obtained in mt-overexpression strains, but no significant difference in the malic enzyme flux between recombinant strains and the control strain. Our results confirmed that PP pathway might play an important role for supplying NADPH and malic enzyme is not a limiting factor for fatty acid synthesis in oleaginous fungus M. circinelloides strains.ConclusionIntracellular metabolic flux information suggested that mt-overexpression strains had higher flux in PP pathway and GOX cycle, lower flux in TCA cycle, and no difference in malic enzyme cycle. Together, the role of malate transporter was assumed to further participate in transporting cycle of acetyl-CoA and drive PP pathway to supply NADPH required for lipid accumulation in recombinant M. circinelloides strains.

From Escherichia coli mutant 13C labeling data to a core kinetic model: A kinetic model parameterization pipeline.

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.

P13CMFA: Parsimonious 13C metabolic flux analysis.

Deciphering the mechanisms of regulation of metabolic networks subjected to perturbations, including disease states and drug-induced stress, relies on tracing metabolic fluxes. One of the most informative data to predict metabolic fluxes are 13C based metabolomics, which provide information about how carbons are redistributed along central carbon metabolism. Such data can be integrated using 13C Metabolic Flux Analysis (13C MFA) to provide quantitative metabolic maps of flux distributions. However, 13C MFA might be unable to reduce the solution space towards a unique solution either in large metabolic networks or when small sets of measurements are integrated. Here we present parsimonious 13C MFA (p13CMFA), an approach that runs a secondary optimization in the 13C MFA solution space to identify the solution that minimizes the total reaction flux. Furthermore, flux minimization can be weighted by gene expression measurements allowing seamless integration of gene expression data with 13C data. As proof of concept, we demonstrate how p13CMFA can be used to estimate intracellular flux distributions from 13C measurements and transcriptomics data. We have implemented p13CMFA in Iso2Flux, our in-house developed isotopic steady-state 13C MFA software. The source code is freely available on GitHub (https://github.com/cfoguet/iso2flux/releases/tag/0.7.2).

High-resolution 13C metabolic flux analysis.

Precise 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, and statistical analysis, as well as best practices developed through decades of experience. Experimental design to ensure that fluxes are estimated with the highest precision is an integral part of the protocol. The protocol is based on growing microbes in two (or more) parallel cultures with 13C-labeled glucose tracers, followed by gas chromatography-mass spectrometry (GC-MS) measurements of isotopic labeling of protein-bound amino acids, glycogen-bound glucose, and RNA-bound ribose. Fluxes are then estimated using 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 d and quantifies metabolic fluxes with a standard deviation of ≤2%, a substantial improvement over previous implementations. The presented protocol is exemplified using an Escherichia coli ΔtpiA case study with full supporting data, providing a hands-on opportunity to step through a complex troubleshooting scenario. Although applications to prokaryotic microbial systems are emphasized, this protocol can be easily adjusted for application to eukaryotic organisms.

Multi-Omics Analysis of Fatty Alcohol Production in Engineered Yeasts Saccharomyces cerevisiae and Yarrowia lipolytica.

Fatty alcohols are widely used in various applications within a diverse set of industries, such as the soap and detergent industry, the personal care, and cosmetics industry, as well as the food industry. The total world production of fatty alcohols is over 2 million tons with approximately equal parts derived from fossil oil and from plant oils or animal fats. Due to the environmental impact of these production methods, there is an interest in alternative methods for fatty alcohol production via microbial fermentation using cheap renewable feedstocks. In this study, we aimed to obtain a better understanding of how fatty alcohol biosynthesis impacts the host organism, baker’s yeast Saccharomyces cerevisiae or oleaginous yeast Yarrowia lipolytica. Producing and non-producing strains were compared in growth and nitrogen-depletion cultivation phases. The multi-omics analysis included physiological characterization, transcriptome analysis by RNAseq, 13Cmetabolic flux analysis, and intracellular metabolomics. Both species accumulated fatty alcohols under nitrogen-depletion conditions but not during growth. The fatty alcohol–producing Y. lipolytica strain had a higher fatty alcohol production rate than an analogous S. cerevisiae strain. Nitrogen-depletion phase was associated with lower glucose uptake rates and a decrease in the intracellular concentration of acetyl–CoA in both yeast species, as well as increased organic acid secretion rates in Y. lipolytica. Expression of the fatty alcohol–producing enzyme fatty acyl–CoA reductase alleviated the growth defect caused by deletion of hexadecenal dehydrogenase encoding genes (HFD1 and HFD4) in Y. lipolytica. RNAseq analysis showed that fatty alcohol production triggered a cell wall stress response in S. cerevisiae. RNAseq analysis also showed that both nitrogen-depletion and fatty alcohol production have substantial effects on the expression of transporter encoding genes in Y. lipolytica. In conclusion, through this multi-omics study, we uncovered some effects of fatty alcohol production on the host metabolism. This knowledge can be used as guidance for further strain improvement towards the production of fatty alcohols.

Metabolic switches from quiescence to growth in synchronized Saccharomyces cerevisiae

IntroductionThe switch from quiescence (G0) into G1 and cell cycle progression critically depends on specific nutrients and metabolic capabilities. Conversely, metabolic networks are regulated by enzyme–metabolite interaction and transcriptional regulation that lead to flux modifications to support cell growth. How cells process and integrate environmental information into coordinated responses is challenging to analyse and not yet described quantitatively.ObjectivesTo quantitatively monitor the central carbon metabolism during G0 exit and the first 2 h after reentering the cell cycle from synchronized Saccharomyces cerevisiae.MethodsDynamic tailored 13C metabolic flux analysis was used to observe the intracellular metabolite flux changes, and the metabolome and proteome were observed to identify regulatory mechanisms.ResultsG0 cells responded immediately to an extracellular increase of glucose. The intracellular metabolic flux changed in time and specific events were observed. High fluxes into trehalose and glycogen synthesis were observed during the G0 exit. Both fluxes then decreased, reaching a minimum at t = 65 min. Here, storage degradation contributed significantly (i.e. 21%) to the glycolytic flux. In contrast to these changes, the glucose uptake rate remained constant after the G0 exit. The flux into the oxidative pentose phosphate pathway was highest (29-fold increase, 36.4% of the glucose uptake) at t = 65 min, while it was very low at other time points. The maximum flux seems to correlate with a late G1 state preparing for the S phase transition. In the G1/S phase (t = 87 min), anaplerotic reactions such as glyoxylate shunt increased. Protein results show that during this transition, proteins belonging to clusters related with ribosome biogenesis and assembly, and initiation transcription factors clusters were continuously synthetised.ConclusionThe intracellular flux distribution changes dynamically and these major rearrangements highlight the coordinate reorganization of metabolic flux to meet requirements for growth during different cell state.

Mass Isotopologue Distribution of dimer ion adducts of intracellular metabolites for potential applications in 13C Metabolic Flux Analysis.

13C Metabolic Flux Analysis (13C-MFA) is a powerful tool for quantification of carbon flux distribution in metabolic pathways. However, the requirement to obtain accurate labeling patterns, especially for compounds with low abundance, poses a challenge. Chromatographic separation and high sensitivity of the modern mass spectrometers (MS) alleviate this problem to a certain extent. However, the presence of derivatives such as in-source fragments, multimer ion adducts, and multiply charged ions result in reduced intensity of the molecular ion. While multimer ion adducts have been reported in the field of metabolomics, their presence is considered undesirable in quantitative studies. Here, we demonstrate a novel application of dimer ion adducts in calculating the mass isotopologue distribution (MIDs) of the corresponding monomer ions for public domain and in-house generated datasets comprising of 13C-labeling time-course experiments. Out of the 100 standard compounds analyzed, we could detect multimer ion adducts in 24 of the intermediate metabolites. Further, a subset of these multimer ions were detected in all the biological samples analyzed. Majority of these ion adducts were either not detected in the original study or labeled as a putative features. Regression analysis was performed to estimate the monomer MIDs from those of the dimer. This resulted in accurate estimation regardless of the biological system, chromatographic method, the MS hardware, or the relative abundance of the dimer ion. We argue that this analysis may be useful in cases where satisfactory data cannot be extracted from the chromatographic peaks of the monomer ions.

Functional analysis of isoprenoid precursors biosynthesis by quantitative metabolomics and isotopologue profiling

IntroductionIsoprenoids are amongst the most abundant and diverse biological molecules and are involved in a broad range of biological functions. Functional understanding of their biosynthesis is thus key in many fundamental and applicative fields, including systems biology, medicine and biotechnology. However, available methods do not yet allow accurate quantification and tracing of stable isotopes incorporation for all the isoprenoids precursors.ObjectivesWe developed and validated a complete methodology for quantitative metabolomics and isotopologue profiling of isoprenoid precursors in the yeast Saccharomyces cerevisiae.MethodsThis workflow covers all the experimental and computational steps from sample collection and preparation to data acquisition and processing. It also includes a novel quantification method based on liquid chromatography coupled to high-resolution mass spectrometry. Method validation followed the Metabolomics Standards Initiative guidelines.ResultsThis workflow ensures accurate absolute quantification (RSD < 20%) of all mevalonate and prenyl pyrophosphates intermediates with a high sensitivity over a large linear range (from 0.1 to 50 pmol). In addition, we demonstrate that this workflow brings crucial information to design more efficient phytoene producers. Results indicate stable turnover rates of prenyl pyrophosphate intermediates in the constructed strains and provide quantitative information on the change of the biosynthetic flux of phytoene precursors.ConclusionThis methodology fills one of the last technical gaps for functional studies of isoprenoids biosynthesis and should be applicable to other eukaryotic and prokaryotic (micro)organisms after adaptation of some organism-dependent steps. This methodology also opens the way to 13C-metabolic flux analysis of isoprenoid biosynthesis.

Metabolic flux responses to deletion of 20 core enzymes reveal flexibility and limits of E. coli metabolism

Despite remarkable progress in mapping biochemistry and gene-protein-reaction relationships, quantitative systems-level understanding of microbial metabolism remains a persistent challenge. Here, 13C-metabolic flux 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 ΔpfkA 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.

A concerted systems biology analysis of phenol metabolism in Rhodococcus opacus PD630

Rhodococcus opacus PD630 metabolizes aromatic substrates and naturally produces branched-chain lipids, which are advantageous traits for lignin valorization. To provide insights into its lignocellulose hydrolysate utilization, we performed 13C-pathway tracing, 13C-pulse-tracing, transcriptional profiling, biomass composition analysis, and metabolite profiling in conjunction with 13C-metabolic flux analysis (13C-MFA) of phenol metabolism. We found that 1) phenol is metabolized mainly through the ortho–cleavage pathway; 2) phenol utilization requires a highly active TCA cycle; 3) NADPH is generated mainly via NADPH-dependent isocitrate dehydrogenase; 4) active cataplerotic fluxes increase plasticity in the TCA cycle; and 5) gluconeogenesis occurs partially through the reversed Entner–Doudoroff pathway (EDP). We also found that phenol-fed R. opacus PD630 generally has lower sugar phosphate concentrations (e.g., fructose 1,6-bisphosphatase) compared to metabolite pools in 13C-glucose-fed Escherichia coli (set as internal standards), while its TCA metabolites (e.g., malate, succinate, and α-ketoglutarate) accumulate intracellularly with measurable succinate secretion. In addition, we found that phenol utilization was inhibited by benzoate, while catabolite repressions by other tested carbon substrates (e.g., glucose and acetate) were absent in R. opacus PD630. Three adaptively-evolved strains display very different growth rates when fed with phenol as a sole carbon source, but they maintain a conserved flux network. These findings improve our understanding of R. opacus’ metabolism for future lignin valorization.

Reversible jump MCMC for multi-model inference in Metabolic Flux Analysis.

The validity of model based inference, as used in systems biology, depends on the underlying model formulation. Often, a vast number of competing models is available, that are built on different assumptions, all consistent with the existing knowledge about the studied biological phenomenon. As a remedy for this, Bayesian Model Averaging (BMA) facilitates parameter and structural inferences based on multiple models simultaneously. However, in fields where a vast number of alternative, high-dimensional and non-linear models are involved, the BMA-based inference task is computationally very challenging. Here we use BMA in the complex setting of Metabolic Flux Analysis (MFA) to infer whether potentially reversible reactions proceed uni- or bidirectionally, using 13C labeling data and metabolic networks. BMA is applied on a large set of candidate models with differing directionality settings, using a tailored multi-model Markov Chain Monte Carlo (MCMC) approach. The applicability of our algorithm is shown by inferring the in vivo probability of reaction bidirectionalities in a realistic network setup, thereby extending the scope of 13C MFA from parameter to structural inference. Supplementary data are available at Bioinformatics online.

The Design of FluxML: A Universal Modeling Language for 13C Metabolic Flux Analysis.

13C metabolic flux analysis (MFA) is the method of choice when a detailed inference of intracellular metabolic fluxes in living organisms under metabolic quasi-steady state conditions is desired. Being continuously developed since two decades, the technology made major contributions to the quantitative characterization of organisms in all fields of biotechnology and health-related research. 13C MFA, however, stands out from other “-omics sciences,” in that it requires not only experimental-analytical data, but also mathematical models and a computational toolset to infer the quantities of interest, i.e., the metabolic fluxes. At present, these models cannot be conveniently exchanged between different labs. Here, we present the implementation-independent model description language FluxML for specifying 13C MFA models. The core of FluxML captures the metabolic reaction network together with atom mappings, constraints on the model parameters, and the wealth of data configurations. In particular, we describe the governing design processes that shaped the FluxML language. We demonstrate the utility of FluxML to represent many contemporary experimental-analytical requirements in the field of 13C MFA. The major aim of FluxML is to offer a sound, open, and future-proof language to unambiguously express and conserve all the necessary information for model re-use, exchange, and comparison. Along with FluxML, several powerful computational tools are supplied for easy handling, but also to maintain a maximum of flexibility. Altogether, the FluxML collection is an “all-around carefree package” for 13C MFA modelers. We believe that FluxML improves scientific productivity as well as transparency and therewith contributes to the efficiency and reproducibility of computational modeling efforts in the field of 13C MFA.

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.