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

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

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

A pathogen-specific isotope tracing approach reveals metabolic activities and fluxes of intracellular Salmonella.

Pathogenic bacteria proliferating inside mammalian host cells need to rapidly adapt to the intracellular environment. How they achieve this and scavenge essential nutrients from the host has been an open question due to the difficulties in distinguishing between bacterial and host metabolites in situ. Here, we capitalized on the inability of mammalian cells to metabolize mannitol to develop a stable isotopic labeling approach to track Salmonella enterica metabolites during intracellular proliferation in host macrophage and epithelial cells. By measuring label incorporation into Salmonella metabolites with liquid chromatography-mass spectrometry (LC-MS), and combining it with metabolic modeling, we identify relevant carbon sources used by Salmonella, uncover routes of their metabolization, and quantify relative reaction rates in central carbon metabolism. Our results underline the importance of the Entner-Doudoroff pathway (EDP) and the phosphoenolpyruvate carboxylase for intracellularly proliferating Salmonella. More broadly, our metabolic labeling strategy opens novel avenues for understanding the metabolism of pathogens inside host cells

A pathogen-specific isotope tracing approach reveals metabolic activities and fluxes of intracellular Salmonella.

Acknowledgements: We thank the EMBL Metabolomics Core Facility staff for their technical support. We are grateful to all members of the Typas, Patil, Alexandrov, and Nöh labs for fruitful discussions and technical input. We thank Jörg Büscher (MPI Freiburg) for his advice on metabolomics, and Vallo Varik, Joel Selkrig, and Jacob Bobonis, for critical reading of the manuscript. KM is very grateful to the Cold Spring Harbor Course on Metabolomics for the competent and in-depth instruction of basic metabolomics skills.Funder: European Molecular Biology Laboratory; funder-id: http://dx.doi.org/10.13039/100013060Pathogenic bacteria proliferating inside mammalian host cells need to rapidly adapt to the intracellular environment. How they achieve this and scavenge essential nutrients from the host has been an open question due to the difficulties in distinguishing between bacterial and host metabolites in situ. Here, we capitalized on the inability of mammalian cells to metabolize mannitol to develop a stable isotopic labeling approach to track Salmonella enterica metabolites during intracellular proliferation in host macrophage and epithelial cells. By measuring label incorporation into Salmonella metabolites with liquid chromatography-mass spectrometry (LC-MS), and combining it with metabolic modeling, we identify relevant carbon sources used by Salmonella, uncover routes of their metabolization, and quantify relative reaction rates in central carbon metabolism. Our results underline the importance of the Entner-Doudoroff pathway (EDP) and the phosphoenolpyruvate carboxylase for intracellularly proliferating Salmonella. More broadly, our metabolic labeling strategy opens novel avenues for understanding the metabolism of pathogens inside host cells

Intracellular Mycobacterium tuberculosis exploits multiple host nitrogen sources during growth in human macrophages

Nitrogen metabolism of Mycobacterium tuberculosis (Mtb) is crucial for the survival of this important pathogen in its primary human host cell, the macrophage, but little is known about the source(s) and their assimilation within this intracellular niche. Here, we have developed 15N-flux spectral ratio analysis (15N-FSRA) to explore Mtb’s nitrogen metabolism; we demonstrate that intracellular Mtb has access to multiple amino acids in the macrophage, including glutamate, glutamine, aspartate, alanine, glycine, and valine; and we identify glutamine as the predominant nitrogen donor. Each nitrogen source is uniquely assimilated into specific amino acid pools, indicating compartmentalized metabolism during intracellular growth. We have discovered that serine is not available to intracellular Mtb, and we show that a serine auxotroph is attenuated in macrophages. This work provides a systems-based tool for exploring the nitrogen metabolism of intracellular pathogens and highlights the enzyme phosphoserine transaminase as an attractive target for the development of novel anti-tuberculosis therapies

Metabolites for which MIDs could be quantified, were reliable using our standard isolation protocol (S2E Fig), were used to determine pathway activity (Figs 3A and S3B), were used for isotope tracing (Fig 6), or were included in the metabolic model (Figs 4 and S4); n.d. stands for not determined.

Metabolites for which MIDs could be quantified, were reliable using our standard isolation protocol (S2E Fig), were used to determine pathway activity (Figs 3A and S3B), were used for isotope tracing (Fig 6), or were included in the metabolic model (Figs 4 and S4); n.d. stands for not determined.</p

Mannitol is not metabolized by host cells, but internalized and used by intracellular <i>S</i>Tm.

(A) Experimental concept: Can 13C-labeled mannitol, supplied to infected host cells, traverse across mammalian cell membranes without degradation and be taken up by intracellular STm (yellow)? (B) Fractional 13C enrichment of hexose-phosphates and alanine from RAW264.7 cells, determined 48 h after the addition of U-13C mannitol (mtl) or U-13C glucose (glc) into the glucose-containing cell culture medium (DMEM with 1 g/L glucose, Methods). 13C from labeled glucose is incorporated (labeled fraction present), but not from mannitol (no labeled fraction present). Graphs show averages from biological triplicates. (C) Mannitol metabolism in STm: Mannitol enters and is phosphorylated via MtlA; mannitol 1P (toxic when accumulating) is oxidized by MtlD to fructose 6P, where it enters glycolysis. The uptake and metabolization of mannitol are subject to glucose repression via the dephosphorylated phosphocarrier protein HPr, which enhances the activity of the repressor MtlR [32]. (D) Growth yield of STm wild type (wt), ∆mtlA, and ∆mtlD, in MOPS medium with amino acids (Methods), and with combinations of glucose (glc), glycerol (glyc), and mannitol (mtl), as the main carbon sources. Bars depict the averages of technical duplicates and data are representative of 2 independent experiments. (E) Intracellular STm wt and ∆mtlD isolated from RAW264.7 macrophages 20 hpi in a gentamicin protection assay (MOI = 100) supplemented +/− mannitol (mtl), and then serially diluted and spotted on an LB agar plate. The image is representative of 2 independent experiments in biological triplicates. The data underlying this figure can be found in S1 Data. glc, glucose; MOI, multiplicity of infection; STm, Salmonella Typhimurium.</p