Cutting the Gordian Knot: Identifiability of anaplerotic reactions in Corynebacterium glutamicum by means of (13) C-metabolic flux analysis

Rethinking 13C-metabolic flux analysis - The Bayesian way of flux inference

Metabolic reaction rates (fluxes) play a crucial role in comprehending cellular phenotypes and are essential in areas such as metabolic engineering, biotechnology, and biomedical research. The state-of-the-art technique for estimating fluxes is metabolic flux analysis using isotopic labelling (13C-MFA), which uses a dataset-model combination to determine the fluxes. Bayesian statistical methods are gaining popularity in the field of life sciences, but the use of 13C-MFA is still dominated by conventional best-fit approaches. The slow take-up of Bayesian approaches is, at least partly, due to the unfamiliarity of Bayesian methods to metabolic engineering researchers. To address this unfamiliarity, we here outline similarities and differences between the two approaches and highlight particular advantages of the Bayesian way of flux analysis. With a real-life example, re-analysing a moderately informative labelling dataset of E. coli, we identify situations in which Bayesian methods are advantageous and more informative, pointing to potential pitfalls of current 13C-MFA evaluation approaches. We propose the use of Bayesian model averaging (BMA) for flux inference as a means of overcoming the problem of model uncertainty through its tendency to assign low probabilities to both, models that are unsupported by data, and models that are overly complex. In this capacity, BMA resembles a tempered Ockham's razor. With the tempered razor as a guide, BMA-based 13C-MFA alleviates the problem of model selection uncertainty and is thereby capable of becoming a game changer for metabolic engineering by uncovering new insights and inspiring novel approaches.

One-shot 13 C15 N-metabolic flux analysis for simultaneous quantification of carbon and nitrogen flux

Metabolic flux is the final output of cellular regulation and has been extensively studied for carbon but much less is known about nitrogen, which is another important building block for living organisms. For the tuberculosis pathogen, this is particularly important in informing the development of effective drugs targeting the pathogen's metabolism. Here we performed ¹³ C¹⁵ N dual isotopic labeling of Mycobacterium bovis BCG steady state cultures, quantified intracellular carbon and nitrogen fluxes and inferred reaction bidirectionalities. This was achieved by model scope extension and refinement, implemented in a multi-atom transition model, within the statistical framework of Bayesian model averaging (BMA). Using BMA-based ¹³ C¹⁵ N-metabolic flux analysis, we jointly resolve carbon and nitrogen fluxes quantitatively. We provide the first nitrogen flux distributions for amino acid and nucleotide biosynthesis in mycobacteria and establish glutamate as the central node for nitrogen metabolism. We improved resolution of the notoriously elusive anaplerotic node in central carbon metabolism and revealed possible operation modes. Our study provides a powerful and statistically rigorous platform to simultaneously infer carbon and nitrogen metabolism in any biological system.

A genome-scale metabolic model of Cupriavidus necator H16 integrated with TraDIS and transcriptomic data reveals metabolic insights for biotechnological applications

Exploiting biological processes to recycle renewable carbon into high value platform chemicals provides a sustainable and greener alternative to current reliance on petrochemicals. In this regard Cupriavidus necator H16 represents a particularly promising microbial chassis due to its ability to grow on a wide range of low-cost feedstocks, including the waste gas carbon dioxide, whilst also naturally producing large quantities of polyhydroxybutyrate (PHB) during nutrient-limited conditions. Understanding the complex metabolic behaviour of this bacterium is a prerequisite for the design of successful engineering strategies for optimising product yields. We present a genome-scale metabolic model (GSM) of C. necator H16 (denoted iCN1361), which is directly constructed from the BioCyc database to improve the readability and reusability of the model. After the initial automated construction, we have performed extensive curation and both theoretical and experimental validation. By carrying out a genome-wide essentiality screening using a Transposon-directed Insertion site Sequencing (TraDIS) approach, we showed that the model could predict gene knockout phenotypes with a high level of accuracy. Importantly, we indicate how experimental and computational predictions can be used to improve model structure and, thus, model accuracy as well as to evaluate potential false positives identified in the experiments. Finally, by integrating transcriptomics data with iCN1361 we create a condition-specific model, which, importantly, better reflects PHB production in C. necator H16. Observed changes in the omics data and in-silico-estimated alterations in fluxes were then used to predict the regulatory control of key cellular processes. The results presented demonstrate that iCN1361 is a valuable tool for unravelling the system-level metabolic behaviour of C. necator H16 and can provide useful insights for designing metabolic engineering strategies.

Genome-scale metabolic models (GSMs) provide a tool for unravelling the complex metabolic behaviour of bacteria and how they adapt to changing environments and genetic perturbations, and thus offer invaluable insights for biotechnology applications. For a GSM to be used efficiently for strain development purposes, however, the model must be easily readable and reusable by other researchers, whilst being able to predict metabolic behaviour with a high level of accuracy. In this work, we developed a GSM for Cupriavidus necator H16 that is linked to the BioCyc database, which provides an efficient way of application, model update, integration of experimental data and network visualisation for other researchers. Using our model, we demonstrate how integrating experimental observations, including Transposon-directed Insertion site Sequencing (TraDIS) and omics data, can be used to compensate for the lack of regulatory, kinetic and thermodynamic information in GSMs, and thus improve model accuracy. Importantly, we found that TraDIS in vivo screening and GSM analysis are complementary approaches, which can be used in combination to provide reliable gene essentiality predictions. Overall, our results offer an informed strategy for the deliberate manipulation of C. necator H16 metabolic capabilities, towards its industrial application to convert greenhouse gases into biochemicals and biofuels.

An energetic profile of Corynebacterium glutamicum underpinned by measured biomass yield on ATP

The supply and usage of energetic cofactors in metabolism is a central concern for systems metabolic engineering, particularly in case of energy intensive products. One of the most important parameters for systems wide balancing of energetic cofactors is the ATP requirement for biomass formation Y_ATP/Biomass. Despite its fundamental importance, Y_ATP/Biomass values for non-fermentative organisms are still rough estimates deduced from theoretical considerations. For the first time, we present an approach for the experimental determination of Y_ATP/Biomass using comparative ¹³C metabolic flux analysis (¹³C MFA) of a wild type strain and an ATP synthase knockout mutant. We show that the energetic profile of a cell can then be deduced from a genome wide stoichiometric model and experimental maintenance data. Particularly, the contributions of substrate level phosphorylation (SLP) and electron transport phosphorylation (ETP) to ATP generation become available which enables the overall energetic efficiency of a cell to be characterized. As a model organism, the industrial platform organism Corynebacterium glutamicum is used. C. glutamicum uses a respiratory type of energy metabolism, implying that ATP can be synthesized either by SLP or by ETP with the membrane-bound F₁F_O-ATP synthase using the proton motive force (pmf) as driving force. The presence of two terminal oxidases, which differ in their proton translocation efficiency by a factor of three, further complicates energy balancing for this organism. By integration of experimental data and network models, we show that in the wild type SLP and ETP contribute equally to ATP generation. Thus, the role of ETP in respiring bacteria may have been overrated in the past. Remarkably, in the genome wide setting 65% of the pmf is actually not used for ATP synthesis. However, it turns out that, compared to other organisms C. glutamicum still uses its energy budget rather efficiently.

Comprehensive Analysis of C. glutamicum Anaplerotic Deletion Mutants Under Defined d-Glucose Conditions

Wild-type C. glutamicum ATCC 13032 is known to possess two enzymes with anaplerotic (C4-directed) carboxylation activity, namely phosphoenolpyruvate carboxylase (PEPCx) and pyruvate carboxylase (PCx). On the other hand, C3-directed decarboxylation can be catalyzed by the three enzymes phosphoenolpyruvate carboxykinase (PEPCk), oxaloacetate decarboxylase (ODx), and malic enzyme (ME). The resulting high metabolic flexibility at the anaplerotic node compromises the unambigous determination of its carbon and energy flux in C. glutamicum wild type. To circumvent this problem we performed a comprehensive analysis of selected single or double deletion mutants in the anaplerosis of wild-type C. glutamicum under defined d-glucose conditions. By applying well-controlled lab-scale bioreactor experiments in combination with untargeted proteomics, quantitative metabolomics and whole-genome sequencing hitherto unknown, and sometimes counter-intuitive, genotype-phenotype relationships in these mutants could be unraveled. In comparison to the wild type the four mutants C. glutamiucm Δpyc, C. glutamiucm Δpyc Δodx, C. glutamiucm Δppc Δpyc, and C. glutamiucm Δpck showed lowered specific growth rates and d-glucose uptake rates, underlining the importance of PCx and PEPCk activity for a balanced carbon and energy flux at the anaplerotic node. Most interestingly, the strain C. glutamiucm Δppc Δpyc could be evolved to grow on d-glucose as the only source of carbon and energy, whereas this combination was previously considered lethal. The prevented anaplerotic carboxylation activity of PEPCx and PCx was found in the evolved strain to be compensated by an up-regulation of the glyoxylate shunt, potentially in combination with the 2-methylcitrate cycle.

Physicochemical and metabolic constraints for thermodynamics-based stoichiometric modelling under mesophilic growth conditions

Metabolic engineering in the post-genomic era is characterised by the development of new methods for metabolomics and fluxomics, supported by the integration of genetic engineering tools and mathematical modelling. Particularly, constraint-based stoichiometric models have been widely studied: (i) flux balance analysis (FBA) (in silico), and (ii) metabolic flux analysis (MFA) (in vivo). Recent studies have enabled the incorporation of thermodynamics and metabolomics data to improve the predictive capabilities of these approaches. However, an in-depth comparison and evaluation of these methods is lacking. This study presents a thorough analysis of two different in silico methods tested against experimental data (metabolomics and ¹³C-MFA) for the mesophile Escherichia coli. In particular, a modified version of the recently published matTFA toolbox was created, providing a broader range of physicochemical parameters. Validating against experimental data allowed the determination of the best physicochemical parameters to perform the TFA (Thermodynamics-based Flux Analysis). An analysis of flux pattern changes in the central carbon metabolism between ¹³C-MFA and TFA highlighted the limited capabilities of both approaches for elucidating the anaplerotic fluxes. In addition, a method based on centrality measures was suggested to identify important metabolites that (if quantified) would allow to further constrain the TFA. Finally, this study emphasised the need for standardisation in the fluxomics community: novel approaches are frequently released but a thorough comparison with currently accepted methods is not always performed.

Biotechnology has benefitted from the development of high throughput methods characterising living systems at different levels (e.g. concerning genes or proteins), allowing the industrial production of chemical commodities. Recently, focus has been placed on determining reaction rates (or metabolic fluxes) in the metabolic network of certain microorganisms, in order to identify bottlenecks hindering their exploitation. Two main approaches are commonly used, termed metabolic flux analysis (MFA) and flux balance analysis (FBA), based on measuring and estimating fluxes, respectively. While the influence of thermodynamics in living systems was accepted several decades ago, its application to study biochemical networks has only recently been enabled. In this sense, a multitude of different approaches constraining well-established modelling methods with thermodynamics has been suggested. However, physicochemical parameters are generally not properly adjusted to the experimental conditions, which might affect their predictive capabilities. In this study, we have explored the reliability of currently available tools by investigating the impact of varying said parameters in the simulation of metabolic fluxes and metabolite concentration values. Additionally, our in-depth analysis allowed us to highlight limitations and potential solutions that should be considered in future studies.

Revisiting the Growth Modulon of Corynebacterium glutamicum Under Glucose Limited Chemostat Conditions

Increasing the growth rate of the industrial host Corynebacterium glutamicum is a promising target to rise productivities of growth coupled product formation. As a prerequisite, detailed knowledge about the tight regulation network is necessary for identifying promising metabolic engineering goals. Here, we present comprehensive metabolic and transcriptional analysis of C. glutamicum ATCC 13032 growing under glucose limited chemostat conditions with μ = 0.2, 0.3, and 0.4 h^–1. Intermediates of central metabolism mostly showed rising pool sizes with increasing growth. ¹³C-metabolic flux analysis (¹³C-MFA) underlined the fundamental role of central metabolism for the supply of precursors, redox, and energy equivalents. Global, growth-associated, concerted transcriptional patterns were not detected giving rise to the conclusion that glycolysis, pentose-phosphate pathway, and citric acid cycle are predominately metabolically controlled under glucose-limiting chemostat conditions. However, evidence is found that transcriptional regulation takes control over glycolysis once glucose-rich growth conditions are installed.

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

Motivation

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.

Results

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 information

Supplementary data are available at Bioinformatics online.

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.

The anaplerotic node is essential for the intracellular survival of Mycobacterium tuberculosis

Enzymes at the phosphoenolpyruvate (PEP)–pyruvate–oxaloacetate or anaplerotic (ANA) node control the metabolic flux to glycolysis, gluconeogenesis, and anaplerosis. Here we used genetic, biochemical, and ¹³C isotopomer analysis to characterize the role of the enzymes at the ANA node in intracellular survival of the world's most successful bacterial pathogen, Mycobacterium tuberculosis (Mtb). We show that each of the four ANA enzymes, pyruvate carboxylase (PCA), PEP carboxykinase (PCK), malic enzyme (MEZ), and pyruvate phosphate dikinase (PPDK), performs a unique and essential metabolic function during the intracellular survival of Mtb. We show that in addition to PCK, intracellular Mtb requires PPDK as an alternative gateway into gluconeogenesis. Propionate and cholesterol detoxification was also identified as an essential function of PPDK revealing an unexpected role for the ANA node in the metabolism of these physiologically important intracellular substrates and highlighting this enzyme as a tuberculosis (TB)-specific drug target. We show that anaplerotic fixation of CO₂ through the ANA node is essential for intracellular survival of Mtb and that Mtb possesses three enzymes (PCA, PCK, and MEZ) capable of fulfilling this function. In addition to providing a back-up role in anaplerosis we show that MEZ also has a role in lipid biosynthesis. MEZ knockout strains have an altered cell wall and were deficient in the initial entry into macrophages. This work reveals that the ANA node is a focal point for controlling the intracellular replication of Mtb, which goes beyond canonical gluconeogenesis and represents a promising target for designing novel anti-TB drugs.

Cell-Type Specific Metabolic Flux Analysis: A Challenge for Metabolic Phenotyping and a Potential Solution in Plants

Stable isotope labelling experiments are used routinely in metabolic flux analysis (MFA) to determine the metabolic phenotype of cells and tissues. A complication arises in multicellular systems because single cell measurements of transcriptomes, proteomes and metabolomes in multicellular organisms suggest that the metabolic phenotype will differ between cell types. In silico analysis of simulated metabolite isotopomer datasets shows that cellular heterogeneity confounds conventional MFA because labelling data averaged over multiple cell types does not necessarily yield averaged flux values. A potential solution to this problem—the use of cell-type specific reporter proteins as a source of cell-type specific labelling data—is proposed and the practicality of implementing this strategy in the roots of Arabidopsis thaliana seedlings is explored. A protocol for the immunopurification of ectopically expressed green fluorescent protein (GFP) from Arabidopsis thaliana seedlings using a GFP-binding nanobody is developed, and through GC-MS analysis of protein hydrolysates it is established that constitutively expressed GFP reports accurately on the labelling of total protein in root tissues. It is also demonstrated that the constitutive expression of GFP does not perturb metabolism. The principal obstacle to the implementation of the method in tissues with cell-type specific GFP expression is the sensitivity of the GC-MS system.

Metabolism of the fast-growing bacterium Vibrio natriegens elucidated by 13C metabolic flux analysis

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

To be certain about the uncertainty: Bayesian statistics for 13 C metabolic flux analysis

¹³ C Metabolic Fluxes Analysis (¹³ C MFA) remains to be the most powerful approach to determine intracellular metabolic reaction rates. Decisions on strain engineering and experimentation heavily rely upon the certainty with which these fluxes are estimated. For uncertainty quantification, the vast majority of ¹³ C MFA studies relies on confidence intervals from the paradigm of Frequentist statistics. However, it is well known that the confidence intervals for a given experimental outcome are not uniquely defined. As a result, confidence intervals produced by different methods can be different, but nevertheless equally valid. This is of high relevance to ¹³ C MFA, since practitioners regularly use three different approximate approaches for calculating confidence intervals. By means of a computational study with a realistic model of the central carbon metabolism of E. coli, we provide strong evidence that confidence intervals used in the field depend strongly on the technique with which they were calculated and, thus, their use leads to misinterpretation of the flux uncertainty. In order to provide a better alternative to confidence intervals in ¹³ C MFA, we demonstrate that credible intervals from the paradigm of Bayesian statistics give more reliable flux uncertainty quantifications which can be readily computed with high accuracy using Markov chain Monte Carlo. In addition, the widely applied chi-square test, as a means of testing whether the model reproduces the data, is examined closer.

Comprehensive and accurate tracking of carbon origin of LC-tandem mass spectrometry collisional fragments for 13C-MFA

In recent years the benefit of measuring positionally resolved 13C-labeling enrichment from tandem mass spectrometry (MS/MS) collisional fragments for improved precision of 13C-Metabolic Flux Analysis (13C-MFA) has become evident. However, the usage of positional labeling information for 13C-MFA faces two challenges: (1) The mass spectrometric acquisition of a large number of potentially interfering mass transitions may hamper accuracy and sensitivity. (2) The positional identity of carbon atoms of product ions needs to be known. The present contribution addresses the latter challenge by deducing the maximal positional labeling information contained in LC-ESI-MS/MS spectra of product anions of central metabolism as well as product cations of amino acids. For this purpose, we draw on accurate mass spectrometry, selectively labeled standards, and published fragmentation pathways to structurally annotate all dominant mass peaks of a large collection of metabolites, some of which with a complete fragmentation pathway. Compiling all available information, we arrive at the most detailed map of carbon atom fate of LC-ESI-MS/MS collisional fragments yet, comprising 170 intense and structurally annotated product ions with unique carbon origin from 76 precursor ions of 72 metabolites. Our 13C-data proof that heuristic fragmentation rules often fail to yield correct fragment structures and we expose common pitfalls in the structural annotation of product ions. We show that the positionally resolved 13C-label information contained in the product ions that we structurally annotated allows to infer the entire isotopomer distribution of several central metabolism intermediates, which is experimentally demonstrated for malate using quadrupole-time-of-flight MS technology. Finally, the inclusion of the label information from a subset of these fragments improves flux precision in a Corynebacterium glutamicum model of the central carbon metabolism.