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.

An automated workflow that generates atom mappings for large-scale metabolic models and its application to Arabidopsis thaliana

Quantification of reaction fluxes of metabolic networks can help us understand how the integration of different metabolic pathways determines cellular functions. Yet, intracellular fluxes cannot be measured directly but are estimated with metabolic flux analysis (MFA), which relies on the patterns of isotope labeling of metabolites in the network. The application of MFA also requires a stoichiometric model with atom mappings that are currently not available for the majority of large-scale metabolic network models, particularly of plants. While automated approaches such as the Reaction Decoder Toolkit (RDT) can produce atom mappings for individual reactions, tracing the flow of individual atoms of the entire reactions across a metabolic model remains challenging. Here we establish an automated workflow to obtain reliable atom mappings for large-scale metabolic models by refining the outcome of RDT, and apply the workflow to metabolic models of Arabidopsis thaliana. We demonstrate the accuracy of RDT through a comparative analysis with atom mappings from a large database of biochemical reactions, MetaCyc. We further show the utility of our automated workflow by simulating ¹⁵ N isotope enrichment and identifying nitrogen (N)-containing metabolites which show enrichment patterns that are informative for flux estimation in future ¹⁵ N-MFA studies of A. thaliana. The automated workflow established in this study can be readily expanded to other species for which metabolic models have been established and the resulting atom mappings will facilitate MFA and graph-theoretic structural analyses with large-scale metabolic networks.

Robustifying Experimental Tracer Design for13C-Metabolic Flux Analysis

¹³C metabolic flux analysis (MFA) has become an indispensable tool to measure metabolic reaction rates (fluxes) in living organisms, having an increasingly diverse range of applications. Here, the choice of the¹³C labeled tracer composition makes the difference between an information-rich experiment and an experiment with only limited insights. To improve the chances for an informative labeling experiment, optimal experimental design approaches have been devised for¹³C-MFA, all relying on some a priori knowledge about the actual fluxes. If such prior knowledge is unavailable, e.g., for research organisms and producer strains, existing methods are left with a chicken-and-egg problem. In this work, we present a general computational method, termed robustified experimental design (R-ED), to guide the decision making about suitable tracer choices when prior knowledge about the fluxes is lacking. Instead of focusing on one mixture, optimal for specific flux values, we pursue a sampling based approach and introduce a new design criterion, which characterizes the extent to which mixtures are informative in view of all possible flux values. The R-ED workflow enables the exploration of suitable tracer mixtures and provides full flexibility to trade off information and cost metrics. The potential of the R-ED workflow is showcased by applying the approach to the industrially relevant antibiotic producer Streptomyces clavuligerus, where we suggest informative, yet economic labeling strategies.

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.

Streamlining the Analysis of Dynamic 13C-Labeling Patterns for the Metabolic Engineering of Corynebacterium glutamicum as l-Histidine Production Host

Today’s possibilities of genome editing easily create plentitudes of strain mutants that need to be experimentally qualified for configuring the next steps of strain engineering. The application of design-build-test-learn cycles requires the identification of distinct metabolic engineering targets as design inputs for subsequent optimization rounds. Here, we present the pool influx kinetics (PIK) approach that identifies promising metabolic engineering targets by pairwise comparison of up- and downstream ¹³C labeling dynamics with respect to a metabolite of interest. Showcasing the complex l-histidine production with engineered Corynebacterium glutamicuml-histidine-on-glucose yields could be improved to 8.6 ± 0.1 mol% by PIK analysis, starting from a base strain. Amplification of purA, purB, purH, and formyl recycling was identified as key targets only analyzing the signal transduction kinetics mirrored in the PIK values.

IsoSearch: An Untargeted and Unbiased Metabolite and Lipid Isotopomer Tracing Strategy from HR-LC-MS/MS Datasets

Stable isotopic tracer analysis is a technique used to determine carbon or nitrogen atom incorporation into biological systems. A number of mass spectrometry based approaches have been developed for this purpose, including high-resolution tandem mass spectrometry (HR-LC-MS/MS), selected reaction monitoring (SRM) and parallel reaction monitoring (PRM). We have developed an approach for analyzing untargeted metabolomic and lipidomic datasets using high-resolution mass spectrometry with polarity switching and implemented our approach in the open-source R script IsoSearch and in Scaffold Elements software. Using our strategy, which requires an unlabeled reference dataset and isotope labeled datasets across various biological conditions, we traced metabolic isotopomer alterations in breast cancer cells (MCF-7) treated with the metabolic drugs 2-deoxy-glucose, 6-aminonicotinamide, compound 968, and rapamycin. Metabolites and lipids were first identified by the commercial software Scaffold Elements and LipidSearch, then IsoSearch successfully profiled the ¹³C-isotopomers extracted metabolites and lipids from ¹³C-glucose labeled MCF-7 cells. The results interpreted known models, such as glycolysis and pentose phosphate pathway inhibition, but also helped to discover new metabolic/lipid flux patterns, including a reactive oxygen species (ROS) defense mechanism induced by 6AN and triglyceride accumulation in rapamycin treated cells. The results suggest the IsoSearch/Scaffold Elements platform is effective for studying metabolic tracer analysis in diseases, drug metabolism, and metabolic engineering for both polar metabolites and non-polar lipids.

Metabolomics markers in Neurology: current knowledge and future perspectives for therapeutic targeting

INTRODUCTION

Metabolomics is an emerging approach providing new insights into the metabolic changes and underlying mechanisms involved in the pathogenesis of neurological disorders.

AREAS COVERED

Here, the authors present an overview of the current knowledge of metabolic profiling (metabolomics) to provide critical insight on the role of biochemical markers and metabolic alterations in neurological diseases.

EXPERT OPINION

Elucidation of characteristic metabolic alterations in neurological disorders is crucial for a better understanding of their pathogenesis, and for identifying potential biomarkers and drug targets. Nevertheless, discrepancies in diagnostic criteria, sample handling protocols, and analytical methods still affect the generalizability of current study results.

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.

Ex vivo and in vivo stable isotope labelling of central carbon metabolism and related pathways with analysis by LC-MS/MS

Targeted tandem mass spectrometry (LC-MS/MS) has been extremely useful for profiling small molecules extracted from biological sources, such as cells, bodily fluids and tissues. Here, we present a protocol for analysing incorporation of the non-radioactive stable isotopes carbon-13 (¹³C) and nitrogen-15 (¹⁵N) into polar metabolites in central carbon metabolism and related pathways. Our platform utilizes selected reaction monitoring (SRM) with polarity switching and amide hydrophilic interaction liquid chromatography (HILIC) to capture transitions for carbon and nitrogen incorporation into selected metabolites using a hybrid triple quadrupole (QQQ) mass spectrometer. This protocol represents an extension of a previously published protocol for targeted metabolomics of unlabeled species and has been used extensively in tracing the metabolism of nutrients such as ¹³C-labeled glucose, ¹³C-glutamine and ¹⁵N-glutamine in a variety of biological settings (e.g., cell culture experiments and in vivo mouse labelling via i.p. injection). SRM signals are integrated to produce an array of peak areas for each labelling form that serve as the output for further analysis. The processed data are then used to obtain the degree and distribution of labelling of the targeted molecules (termed fluxomics). Each method can be customized on the basis of known unlabeled Q1/Q3 SRM transitions and adjusted to account for the corresponding ¹³C or ¹⁵N incorporation. The entire procedure takes ~6-7 h for a single sample from experimental labelling and metabolite extraction to peak integration.

HILIC-Enabled 13C Metabolomics Strategies: Comparing Quantitative Precision and Spectral Accuracy of QTOF High- and QQQ Low-Resolution Mass Spectrometry

Dynamic ¹³C-tracer-based flux analyses of in vivo reaction networks still require a continuous development of advanced quantification methods applying state-of-the-art mass spectrometry platforms. Utilizing alkaline HILIC chromatography, we adapt strategies for a systematic quantification study in non- and ¹³C-labeled multicomponent endogenous Corynebacterium glutamicum extracts by LC-QTOF high resolution (HRMS) and LC-QQQ tandem mass spectrometry (MS/MS). Without prior derivatization, a representative cross-section of 17 central carbon and anabolic key intermediates were analyzed with high selectivity and sensitivity under optimized ESI-MS settings. In column detection limits for the absolute quantification range were between 6.8-304.7 (QQQ) and 28.7-881.5 fmol (QTOF) with comparable linearities (3-5 orders of magnitude) and enhanced precision using QQQ-MRM detection. Tailor-made preparations of uniformly (U)¹³C-labeled cultivation extracts for isotope dilution mass spectrometry enabled the accurate quantification in complex sample matrices and extended linearities without effect on method parameters. Furthermore, evaluation of metabolite-specific m+1-to-m+0 ratios (ISR_1:0) in non-labeled extracts exhibited sufficient methodical spectral accuracies with mean deviations of 3.89 ± 3.54% (QTOF) and 4.01 ± 3.01% (QQQ). Based on the excellent HILIC performance, conformity analysis of time-resolved isotopic enrichments in ¹³C-tracer experiments revealed sufficient spectral accuracy for QQQ-SIM detection. However, only QTOF-HRMS ensures determination of the full isotopologue space in complex matrices without mass interferences.

Current and future perspectives of functional metabolomics in disease studies-A review

Functional metabolomics is a new concept, which studies the functions of metabolites and related enzymes focused on metabolomics. It overcomes the shortcomings of traditional discovery metabolomics of mainly relying on literatures for biological interpretation. Functional metabolomics has many advantages. Firstly, the functional roles of metabolites and related metabolic enzymes are focused. Secondly, the in vivo and in vitro experiments are conducted to validate the metabolomics findings, therefore, increasing the reliability of metabolomics study and producing the new knowledge. Thirdly, functional metabolomics can be used by biologists to investigate functions of metabolites, and related genes and proteins. In this review, we summarize the analytical, biological and clinical platforms used in functional metabolomics studies. Recent progresses of functional metabolomics in cancer, metabolic diseases and biological phenotyping are reviewed, and future development is also predicted. Because of the tremendous advantages of functional metabolomics, it will have a bright future.

Stage-specific metabolic features of differentiating neurons: Implications for toxicant sensitivity

Developmental neurotoxicity (DNT) may be induced when chemicals disturb a key neurodevelopmental process, and many tests focus on this type of toxicity. Alternatively, DNT may occur when chemicals are cytotoxic only during a specific neurodevelopmental stage. The toxicant sensitivity is affected by the expression of toxicant targets and by resilience factors. Although cellular metabolism plays an important role, little is known how it changes during human neurogenesis, and how potential alterations affect toxicant sensitivity of mature vs. immature neurons. We used immature (d0) and mature (d6) LUHMES cells (dopaminergic human neurons) to provide initial answers to these questions. Transcriptome profiling and characterization of energy metabolism suggested a switch from predominantly glycolytic energy generation to a more pronounced contribution of the tricarboxylic acid cycle (TCA) during neuronal maturation. Therefore, we used pulsed stable isotope-resolved metabolomics (pSIRM) to determine intracellular metabolite pool sizes (concentrations), and isotopically non-stationary ¹³C-metabolic flux analysis (INST ¹³C-MFA) to calculate metabolic fluxes. We found that d0 cells mainly use glutamine to fuel the TCA. Furthermore, they rely on extracellular pyruvate to allow continuous growth. This metabolic situation does not allow for mitochondrial or glycolytic spare capacity, i.e. the ability to adapt energy generation to altered needs. Accordingly, neuronal precursor cells displayed a higher sensitivity to several mitochondrial toxicants than mature neurons differentiated from them. In summary, this study shows that precursor cells lose their glutamine dependency during differentiation while they gain flexibility of energy generation and thereby increase their resistance to low concentrations of mitochondrial toxicants.

A comprehensive evaluation of constraining amino acid biosynthesis in compartmented models for metabolic flux analysis

Recent advances in the availability and applicability of genetic tools for non-conventional yeasts have raised high hopes regarding the industrial applications of such yeasts; however, quantitative physiological data on these yeasts, including intracellular flux distributions, are scarce and have rarely aided in the development of novel yeast applications. The compartmentation of eukaryotic cells adds to model complexity. Model constraints are ideally based on biochemical evidence, which is rarely available for non-conventional yeast and eukaryotic cells. A small-scale model for ¹³C-based metabolic flux analysis of central yeast carbon metabolism was developed that is universally valid and does not depend on localization information regarding amino acid anabolism. The variable compartmental origin of traced metabolites is a feature that allows application of the model to yeasts with uncertain genomic and transcriptional backgrounds. The presented test case includes the baker's yeast Saccharomyces cerevisiae and the methylotrophic yeast Hansenula polymorpha. Highly similar flux solutions were computed using either a model with undefined pathway localization or a model with constraints based on curated (S. cerevisiae) or computationally predicted (H. polymorpha) localization information, while false solutions were found with incorrect localization constraints. These results indicate a potentially adverse effect of universally assuming Saccharomyces-like constraints on amino acid biosynthesis for non-conventional yeasts and verify the validity of neglecting compartmentation constraints using a small-scale metabolic model. The model was specifically designed to investigate the intracellular metabolism of wild-type yeasts under various growth conditions but is also expected to be useful for computing fluxes of other eukaryotic cells.

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Compartmentation influences computed intracellular fluxes.

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Improper localization constraints potentially produce false flux solutions.

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Minimal compartmentation constraints result in high-quality flux computations.

Robust identification of metabolic control for microbial l-methionine production following an easy-to-use puristic approach

The identification of promising metabolic engineering targets is a key issue in metabolic control analysis (MCA). Conventional approaches make intensive use of model-based studies, such as exploiting post-pulse metabolic dynamics after proper perturbation of the microbial system. Here, we present an easy-to-use, purely data-driven approach, defining pool efflux capacities (PEC) for identifying reactions that exert the highest flux control in linear pathways. Comparisons with linlog-based MCA and data-driven substrate elasticities (DDSE) showed that similar key control steps were identified using PEC. Using the example of l-methionine production with recombinant Escherichia coli, PEC consistently and robustly identified main flux controls using perturbation data after a non-labeled ¹²C-l-serine stimulus. Furthermore, the application of full-labeled ¹³C-l-serine stimuli yielded additional insights into stimulus propagation to l-methionine. PEC analysis performed on the ¹³C data set revealed the same targets as the ¹²C data set. Notably, the typical drawback of metabolome analysis, namely, the omnipresent leakage of metabolites, was excluded using the ¹³C PEC approach.

Recent advances in high-throughput 13C-fluxomics

The rise of high throughput (HT) strain engineering tools accompanying the area of synthetic biology is supporting the generation of a large number of microbial cell factories. A current bottleneck in process development is our limited capacity to rapidly analyze the metabolic state of the engineered strains, and in particular their intracellular fluxes. HT ¹³C-fluxomics workflows have not yet become commonplace, despite the existence of several HT tools at each of the required stages. This includes cultivation and sampling systems, analytics for isotopic analysis, and software for data processing and flux calculation. Here, we review recent advances in the field and highlight bottlenecks that must be overcome to allow the emergence of true HT ¹³C-fluxomics workflows.