Interpreting metabolic complexity via isotope-assisted metabolic flux analysis.

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

Omic Studies on In Vitro Cystinosis Model: siRNA-Mediated CTNS Gene Silencing in HK-2 Cells.

Cystinosis is an autosomal recessive disease caused by mutations in the CTNS gene encoding a protein called cystinosine, which is a lysosomal cystine transporter. Disease-causing mutations lead to accumulation of cystine crystals in the lysosomes, thereby causing dysfunction of vital organs. Determination of the increased leukocyte cystine level is one of the most used methods for diagnosis. However, this method is expensive, difficult to perform, and may yield different results in different laboratories. In this study, a disease model was created with CTNS gene-silenced HK2 cells, which can mimic cystinosis in cell culture, and multiomics methods (ie, proteomics, metabolomics, and fluxomics) were implemented at this cell culture to investigate new biomarkers for the diagnosis. CTNS-silenced cell line exhibited distinct metabolic profiles compared with the control cell line. Pathway analysis highlighted significant alterations in various metabolic pathways, including alanine, aspartate, and glutamate metabolism; glutathione metabolism; aminoacyl-tRNA biosynthesis; arginine and proline metabolism; beta-alanine metabolism; ascorbate and aldarate metabolism; and histidine metabolism upon CTNS silencing. Fluxomics analysis revealed increased cycle rates of Krebs cycle intermediates such as fumarate, malate, and citrate, accompanied by enhanced activation of inorganic phosphate and ATP production. Furthermore, proteomic analysis unveiled differential expression levels of key proteins involved in crucial cellular processes. Notably, peptidyl-prolyl cis-trans isomerase A, translation elongation factor 1-beta (EF-1beta), and 60S acidic ribosomal protein decreased in CTNS-silenced cells. Additionally, levels of P0 and tubulin α-1A chain were reduced, whereas levels of 40S ribosomal protein S8 and Midasin increased. Overall, our study, through the utilization of an in vitro cystinosis model and comprehensive multiomics approach, led to the way toward the identification of potential new biomarkers while offering valuable insights into the pathogenesis of cystinosis.

Metabolic impact of heterologous protein production in Pseudomonas putida: Insights into carbon and energy flux control.

For engineered microorganisms, the production of heterologous proteins that are often useless to host cells represents a burden on resources, which have to be shared with normal cellular processes. Within a certain metabolic leeway, this competitive process has no impact on growth. However, once this leeway, or free capacity, is fully utilized, the extra load becomes a metabolic burden that inhibits cellular processes and triggers a broad cellular response, reducing cell growth and often hindering the production of heterologous proteins. In this study, we sought to characterize the metabolic rearrangements occurring in the central metabolism of Pseudomonas putida at different levels of metabolic load. To this end, we constructed a P. putida KT2440 strain that expressed two genes encoding fluorescent proteins, one in the genome under constitutive expression to monitor the free capacity, and the other on an inducible plasmid to probe heterologous protein production. We found that metabolic fluxes are considerably reshuffled, especially at the level of periplasmic pathways, as soon as the metabolic load exceeds the free capacity. Heterologous protein production leads to the decoupling of anabolism and catabolism, resulting in large excess energy production relative to the requirements of protein biosynthesis. Finally, heterologous protein production was found to exert a stronger control on carbon fluxes than on energy fluxes, indicating that the flexible nature of P. putida's central metabolic network is solicited to sustain energy production.

Omics data for sampling thermodynamically feasible kinetic models.

Kinetic models are key to understanding and predicting the dynamic behaviour of metabolic systems. Traditional models require kinetic parameters which are not always available and are often estimated in vitro. Ensemble models overcome this challenge by sampling thermodynamically feasible models around a measured reference point. However, it is unclear if the convenient distributions used to generate the ensemble produce a natural distribution of model parameters and hence if the model predictions are reasonable. In this paper, we produced a detailed kinetic model for the central carbon metabolism of Escherichia coli. The model consists of 82 reactions (including 13 reactions with allosteric regulation) and 79 metabolites. To sample the model, we used metabolomic and fluxomic data from a single steady-state time point for E. coli K-12 MG1655 growing on glucose minimal M9 medium (average sampling time for 1000 models: 11.21 ± 0.14 min). Afterwards, in order to examine whether our sampled models are biologically sound, we calculated Km, Vmax and kcat for the reactions and compared them to previously published values. Finally, we used metabolic control analysis to identify enzymes with high control over the fluxes in the central carbon metabolism. Our analyses demonstrate that our platform samples thermodynamically feasible kinetic models, which are in agreement with previously published experimental results and can be used to investigate metabolic control patterns within cells. This renders it a valuable tool for the study of cellular metabolism and the design of metabolic pathways.

High throughput 13C-metabolic flux analysis of 3-hydroxypropionic acid producing Pichia pastoris reveals limited availability of acetyl-CoA and ATP due to tight control of the glycolytic flux.

BACKGROUND: Production of 3-hydroxypropionic acid (3-HP) through the malonyl-CoA pathway has yielded promising results in Pichia pastoris (Komagataella phaffii), demonstrating the potential of this cell factory to produce this platform chemical and other acetyl-CoA-derived products using glycerol as a carbon source. However, further metabolic engineering of the original P. pastoris 3-HP-producing strains resulted in unexpected outcomes, e.g., significantly lower product yield and/or growth rate. To gain an understanding on the metabolic constraints underlying these observations, the fluxome (metabolic flux phenotype) of ten 3-HP-producing P. pastoris strains has been characterized using a high throughput 13C-metabolic flux analysis platform. Such platform enabled the operation of an optimised workflow to obtain comprehensive maps of the carbon flux distribution in the central carbon metabolism in a parallel-automated manner, thereby accelerating the time-consuming strain characterization step in the design-build-test-learn cycle for metabolic engineering of P. pastoris. RESULTS: We generated detailed maps of the carbon fluxes in the central carbon metabolism of the 3-HP producing strain series, revealing the metabolic consequences of different metabolic engineering strategies aimed at improving NADPH regeneration, enhancing conversion of pyruvate into cytosolic acetyl-CoA, or eliminating by-product (arabitol) formation. Results indicate that the expression of the POS5 NADH kinase leads to a reduction in the fluxes of the pentose phosphate pathway reactions, whereas an increase in the pentose phosphate pathway fluxes was observed when the cytosolic acetyl-CoA synthesis pathway was overexpressed. Results also show that the tight control of the glycolytic flux hampers cell growth due to limited acetyl-CoA biosynthesis. When the cytosolic acetyl-CoA synthesis pathway was overexpressed, the cell growth increased, but the product yield decreased due to higher growth-associated ATP costs. Finally, the six most relevant strains were also cultured at pH 3.5 to assess the effect of a lower pH on their fluxome. Notably, similar metabolic fluxes were observed at pH 3.5 compared to the reference condition at pH 5. CONCLUSIONS: This study shows that existing fluoxomics workflows for high-throughput analyses of metabolic phenotypes can be adapted to investigate P. pastoris, providing valuable information on the impact of genetic manipulations on the metabolic phenotype of this yeast. Specifically, our results highlight the metabolic robustness of P. pastoris's central carbon metabolism when genetic modifications are made to increase the availability of NADPH and cytosolic acetyl-CoA. Such knowledge can guide further metabolic engineering of these strains. Moreover, insights into the metabolic adaptation of P. pastoris to an acidic pH have also been obtained, showing the capability of the fluoxomics workflow to assess the metabolic impact of environmental changes.

Marine bacteria and omic approaches: A novel and potential repository for bioremediation assessment.

Marine environments accommodating diverse assortments of life constitute a great pool of differentiated natural resources. The cumulative need to remedy unpropitious effects of anthropogenic activities on estuaries and coastal marine ecosystems has propelled the development of effective bioremediation strategies. Marine bacteria producing biosurfactants are promising agents for bio-remediating oil pollution in marine environments, making them prospective candidates for enhancing oil recovery. Molecular omics technologies are considered an emerging field of research in ecological and diversity assessment owing to their utility in environmental surveillance and bioremediation of polluted sites. A thorough literature review was undertaken to understand the applicability of different omic techniques used for bioremediation assessment using marine bacteria. This review further establishes that for bioremediation of environmental pollutants (i.e. heavy metals, hydrocarbons, xenobiotic and numerous recalcitrant compounds), organisms isolated from marine environments can be better used for their removal. The literature survey shows that omics approaches can provide exemplary knowledge about microbial communities and their role in the bioremediation of environmental pollutants. This review centres on applications of marine bacteria in enhanced bioremediation, using the omics approaches that can be a vital biological contrivance in environmental monitoring to tackle environmental degradation. The paper aims to identify the gaps in investigations involving marine bacteria to help researchers, ecologists and decision-makers to develop a holistic understanding regarding their utility in bioremediation assessment.

SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions.

BACKGROUND: The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms-two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR. RESULTS: We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6-27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification. CONCLUSIONS: SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.

Metabolic remodelling of glucose, fatty acid and redox pathways in the heart of type 2 diabetic mice.

KEY POINTS: Hearts from type 2 diabetic animals display perturbations in excitation-contraction coupling, impairing myocyte contractility and delaying relaxation, along with altered substrate consumption patterns. Under high glucose and ß-adrenergic stimulation conditions, palmitate can, at least in part, offset left ventricle (LV) dysfunction in hearts from diabetic mice, improving contractility and relaxation while restoring coronary perfusion pressure. Fluxome calculations of central catabolism in diabetic hearts show that, in the presence of palmitate, there is a metabolic remodelling involving tricarboxylic acid cycle, polyol and pentose phosphate pathways, leading to improved redox balance in cytoplasmic and mitochondrial compartments. Under high glucose and increased energy demand, the metabolic/fluxomic redirection leading to restored redox balance imparted by palmitate helps explain maintained LV function and may contribute to designing novel therapeutic approaches to prevent cardiac dysfunction in diabetic patients. ABSTRACT: Type-2 diabetes (T2DM) leads to reduced myocardial performance, and eventually heart failure. Excessive accumulation of lipids and glucose is central to T2DM cardiomyopathy. Previous data showed that palmitate (Palm) or glutathione preserved heart mitochondrial energy/redox balance under excess glucose, rescuing ß-adrenergic-stimulated cardiac excitation-contraction coupling. However, the mechanisms underlying the accompanying improved contractile performance have been largely ignored. Herein we explore in intact heart under substrate excess the metabolic remodelling associated with cardiac function in diabetic db/db mice subjected to stress given by ß-adrenergic stimulation with isoproterenol and high glucose compared to their non-diabetic controls (+/+, WT) under euglycaemic conditions. When perfused with Palm, T2DM hearts exhibited improved contractility/relaxation compared to WT, accompanied by extensive metabolic remodelling as demonstrated by metabolomics-fluxomics combined with bioinformatics and computational modelling. The T2DM heart metabolome showed significant differences in the abundance of metabolites in pathways related to glucose, lipids and redox metabolism. Using a validated computational model of heart's central catabolism, comprising glucose and fatty acid (FA) oxidation in cytoplasmic and mitochondrial compartments, we estimated that fluxes through glucose degradation pathways are ∼2-fold lower in heart from T2DM vs. WT under all conditions studied. Palm addition elicits improvement of the redox status via enhanced ß-oxidation and decreased glucose uptake, leading to flux-redirection away from redox-consuming pathways (e.g. polyol) while maintaining the flux through redox-generating pathways together with glucose-FA 'shared fuelling' of oxidative phosphorylation. Thus, available FAs such as Palm may help improve function via enhanced redox balance in T2DM hearts during peaks of hyperglycaemia and increased workload.

Multi-omics analysis unravels a segregated metabolic flux network that tunes co-utilization of sugar and aromatic carbons in Pseudomonas putida.

Pseudomonas species thrive in different nutritional environments and can catabolize divergent carbon substrates. These capabilities have important implications for the role of these species in natural and engineered carbon processing. However, the metabolic phenotypes enabling Pseudomonas to utilize mixed substrates remain poorly understood. Here, we employed a multi-omics approach involving stable isotope tracers, metabolomics, fluxomics, and proteomics in Pseudomonas putida KT2440 to investigate the constitutive metabolic network that achieves co-utilization of glucose and benzoate, respectively a monomer of carbohydrate polymers and a derivative of lignin monomers. Despite nearly equal consumption of both substrates, metabolite isotopologues revealed nonuniform assimilation throughout the metabolic network. Gluconeogenic flux of benzoate-derived carbons from the tricarboxylic acid cycle did not reach the upper Embden-Meyerhof-Parnas pathway nor the pentose-phosphate pathway. These latter two pathways were populated exclusively by glucose-derived carbons through a cyclic connection with the Entner-Doudoroff pathway. We integrated the 13C-metabolomics data with physiological parameters for quantitative flux analysis, demonstrating that the metabolic segregation of the substrate carbons optimally sustained biosynthetic flux demands and redox balance. Changes in protein abundance partially predicted the metabolic flux changes in cells grown on the glucose:benzoate mixture versus on glucose alone. Notably, flux magnitude and directionality were also maintained by metabolite levels and regulation of phosphorylation of key metabolic enzymes. These findings provide new insights into the metabolic architecture that affords adaptability of P. putida to divergent carbon substrates and highlight regulatory points at different metabolic nodes that may underlie the high nutritional flexibility of Pseudomonas species.

Increasing field strength versus advanced isotope labeling for NMR-based fluxomics.

Nuclear magnetic resonance (NMR)-based fluxomics seeks to measure the incorporation of isotope labels in selected metabolites to follow kinetically the synthesis of the latter. It can however equally be used to understand the biosynthetic origin of the same metabolites. We investigate here different NMR approaches to optimize such experiments in terms of resolution and time requirement. Using the isoleucine biosynthesis as an example, we explore the use of different field strengths ranging from 500 MHz to 1.1 GHz. Because of the different field dependence of chemical shift and heteronuclear J couplings, the spectra change at different field strengths. We equally explore the approach to silence the leucine/valine methyl signals through the use of a suitable deuterated precursor, thereby allowing selective observation of the Ile 13 C labeling pattern. Combining both approaches, we arrive at an efficient procedure for the NMR-based exploration of Ile biosynthesis.

A Non-Invasive Tool for Real-Time Measurement of Sulfate in Living Cells.

Sulfur (S) is an essential element for all forms of life. It is involved in numerous essential processes because S is considered as the primary source of one of the essential amino acids, methionine, which plays an important role in biological events. For the control and regulation of sulfate in a metabolic network through fluxomics, a non-invasive tool is highly desirable that opens the door to monitor the level of the sulfate in real time and space in living cells without fractionation of the cells or tissue. Here, we engineered a FRET (fluorescence resonance energy transfer) based sensor for sulfate, which is genetically-encoded and named as FLIP-SP (Fluorescent indicator protein for sulfate). The FLIP-SP can measure the level of the sulfate in live cells. This sensor was constructed by the fusion of fluorescent proteins at the N- and C-terminus of sulfate binding protein (sbp). The FLIP-SP is highly specific to sulfate, and showed pH stability. Real-time monitoring of the level of sulfate in prokaryotic and eukaryotic cells showed sensor bio-compatibility with living cells. We expect that this sulfate sensor offers a valuable strategy in the understanding of the regulation of the flux of sulfate in the metabolic network.

Analysis of metabolic flux in felid spermatozoa using metabolomics and 13C-based fluxomics†.

Spermatozoa from three feline species-the domestic cat (Felis catus), the cheetah (Acinonyx jubatus), and the clouded leopard (Neofelis nebulosa)-were analyzed using metabolomic profiling and 13C-based fluxomics to address questions raised regarding their energy metabolism. Metabolic profiles and utilization of 13C-labeled energy substrates were detected and quantified using gas chromatography-mass spectrometry (GC-MS). Spermatozoa were collected by electroejaculation and incubated in media supplemented with 1.0 mM [U13C]-glucose, [U13C]-fructose, or [U13C]-pyruvate. Evaluation of intracellular metabolites following GC-MS analysis revealed the uptake and utilization of labeled glucose and fructose in sperm, as indicated by the presence of heavy ions in glycolytic products lactate and pyruvate. Despite evidence of substrate utilization, neither glucose nor fructose had an effect on the sperm motility index of ejaculated spermatozoa from any of the three felid species, and limited entry of pyruvate derived from these hexose substrates into mitochondria and the tricarboxylic acid cycle was detected. However, pathway utilization was species-specific for the limited number of individuals (four to seven males per species) assessed in these studies. An inhibitor of fatty acid beta-oxidation (FAO), etomoxir, altered metabolic profiles of all three felid species but decreased motility only in the cheetah. While fluxomic analysis provided direct evidence that glucose and fructose undergo catabolic metabolism, other endogenous substrates such as endogenous lipids may provide energy to fuel motility.

A model for determining cardiac mitochondrial substrate utilisation using stable 13C-labelled metabolites.

INTRODUCTION: Relative oxidation of different metabolic substrates in the heart varies both physiologically and pathologically, in order to meet metabolic demands under different circumstances. 13C labelled substrates have become a key tool for studying substrate use-yet an accurate model is required to analyse the complex data produced as these substrates become incorporated into the Krebs cycle. OBJECTIVES: We aimed to generate a network model for the quantitative analysis of Krebs cycle intermediate isotopologue distributions measured by mass spectrometry, to determine the 13C labelled proportion of acetyl-CoA entering the Krebs cycle. METHODS: A model was generated, and validated ex vivo using isotopic distributions measured from isolated hearts perfused with buffer containing 11 mM glucose in total, with varying fractions of universally labelled with 13C. The model was then employed to determine the relative oxidation of glucose and triacylglycerol by hearts perfused with 11 mM glucose and 0.4 mM equivalent Intralipid (a triacylglycerol mixture). RESULTS: The contribution of glucose to Krebs cycle oxidation was measured to be 79.1 ± 0.9%, independent of the fraction of buffer glucose which was U-13C labelled, or of which Krebs cycle intermediate was assessed. In the presence of Intralipid, glucose and triglyceride were determined to contribute 58 ± 3.6% and 35.6 ± 0.8% of acetyl-CoA entering the Krebs cycle, respectively. CONCLUSION: These results demonstrate the accuracy of a functional model of Krebs cycle metabolism, which can allow quantitative determination of the effects of therapeutics and pathology on cardiac substrate metabolism.

Rapid two-dimensional ALSOFAST-HSQC experiment for metabolomics and fluxomics studies: application to a 13C-enriched cancer cell model treated with gold nanoparticles.

Isotope labeling enables the use of 13C-based metabolomics techniques with strongly improved resolution for a better identification of relevant metabolites and tracing of metabolic fluxes in cell and animal models, as required in fluxomics studies. However, even at high NMR-active isotope abundance, the acquisition of one-dimensional 13C and classical two-dimensional 1H,13C-HSQC experiments remains time consuming. With the aim to provide a shorter, more efficient alternative, herein we explored the ALSOFAST-HSQC experiment with its rapid acquisition scheme for the analysis of 13C-labeled metabolites in complex biological mixtures. As an initial step, the parameters of the pulse sequence were optimized to take into account the specific characteristics of the complex samples. We then applied the fast two-dimensional experiment to study the effect of different kinds of antioxidant gold nanoparticles on a HeLa cancer cell model grown on 13C glucose-enriched medium. As a result, 1H,13C-2D correlations could be obtained in a couple of seconds to few minutes, allowing a simple and reliable identification of various 13C-enriched metabolites and the determination of specific variations between the different sample groups. Thus, it was possible to monitor glucose metabolism in the cell model and study the antioxidant effect of the coated gold nanoparticles in detail. Finally, with an experiment time of only half an hour, highly resolved 1H,13C-HSQC spectra using the ALSOFAST-HSQC pulse sequence were acquired, revealing the isotope-position-patterns of the corresponding 13C-nuclei from carbon multiplets. Graphical abstract Fast NMR applied to metabolomics and fluxomics studies with gold nanoparticles.

Metabolic flexibility of a butyrate pathway mutant of Clostridium acetobutylicum.

Clostridium acetobutylicum possesses two homologous buk genes, buk (or buk1) and buk2, which encode butyrate kinases involved in the last step of butyrate formation. To investigate the contribution of buk in detail, an in-frame deletion mutant was constructed. However, in all the Δbuk mutants obtained, partial deletions of the upstream ptb gene were observed, and low phosphotransbutyrylase and butyrate kinase activities were measured. This demonstrates that i) buk (CA_C3075) is the key butyrate kinase-encoding gene and that buk2 (CA_C1660) that is poorly transcribed only plays a minor role; and ii) strongly suggests that a Δbuk mutant is not viable if the ptb gene is not also inactivated, probably due to the accumulation of butyryl-phosphate, which might be toxic for the cell. One of the ΔbukΔptb mutants was subjected to quantitative transcriptomic (mRNA molecules/cell) and fluxomic analyses in acidogenic, solventogenic and alcohologenic chemostat cultures. In addition to the low butyrate production, drastic changes in metabolic fluxes were also observed for the mutant: i) under acidogenic conditions, the primary metabolite was butanol and a new metabolite, 2-hydroxy-valerate, was produced ii) under solventogenesis, 58% increased butanol production was obtained compared to the control strain under the same conditions, and a very high yield of butanol formation (0.3gg-1) was reached; and iii) under alcohologenesis, the major product was lactate. Furthermore, at the transcriptional level, adhE2, which encodes an aldehyde/alcohol dehydrogenase and is known to be a gene specifically expressed in alcohologenesis, was surprisingly highly expressed in all metabolic states in the mutant. The results presented here not only support the key roles of buk and ptb in butyrate formation but also highlight the metabolic flexibility of C. acetobutylicum in response to genetic alteration of its primary metabolism.

Natural isotope correction of MS/MS measurements for metabolomics and (13)C fluxomics.

Fluxomics and metabolomics are crucial tools for metabolic engineering and biomedical analysis to determine the in vivo cellular state. Especially, the application of (13)C isotopes allows comprehensive insights into the functional operation of cellular metabolism. Compared to single MS, tandem mass spectrometry (MS/MS) provides more detailed and accurate measurements of the metabolite enrichment patterns (tandem mass isotopomers), increasing the accuracy of metabolite concentration measurements and metabolic flux estimation. MS-type data from isotope labeling experiments is biased by naturally occurring stable isotopes (C, H, N, O, etc.). In particular, GC-MS(/MS) requires derivatization for the usually non-volatile intracellular metabolites introducing additional natural isotopes leading to measurements that do not directly represent the carbon labeling distribution. To make full use of LC- and GC-MS/MS mass isotopomer measurements, the influence of natural isotopes has to be eliminated (corrected). Our correction approach is analyzed for the two most common applications; (13)C fluxomics and isotope dilution mass spectrometry (IDMS) based metabolomics. Natural isotopes can have an impact on the calculated flux distribution which strongly depends on the substrate labeling and the actual flux distribution. Second, we show that in IDMS based metabolomics natural isotopes lead to underestimated concentrations that can and should be corrected with a nonlinear calibration. Our simulations indicate that the correction for natural abundance in isotope based fluxomics and quantitative metabolomics is essential for correct data interpretation.

New technologies - new insights into the pathogenesis of hepatic encephalopathy.

Hepatic encephalopathy (HE) is a neuropsychiatric syndrome which frequently accompanies acute or chronic liver disease. It is characterized by a variety of symptoms of different severity such as cognitive deficits and impaired motor functions. Currently, HE is seen as a consequence of a low grade cerebral oedema associated with the formation of cerebral oxidative stress and deranged cerebral oscillatory networks. However, the pathogenesis of HE is still incompletely understood as liver dysfunction triggers exceptionally complex metabolic derangements in the body which need to be investigated by appropriate technologies. This review summarizes technological approaches presented at the ISHEN conference 2014 in London which may help to gain new insights into the pathogenesis of HE. Dynamic in vivo 13C nuclear magnetic resonance spectroscopy was performed to analyse effects of chronic liver failure in rats on brain energy metabolism. By using a genomics approach, microRNA expression changes were identified in plasma of animals with acute liver failure which may be involved in interorgan interactions and which may serve as organ-specific biomarkers for tissue damage during acute liver failure. Genomics were also applied to analyse glutaminase gene polymorphisms in patients with liver cirrhosis indicating that haplotype-dependent glutaminase activity is an important pathogenic factor in HE. Metabonomics represents a promising approach to better understand HE, by capturing the systems level metabolic changes associated with disease in individuals, and enabling monitoring of metabolic phenotypes in real time, over a time course and in response to treatment, to better inform clinical decision making. Targeted fluxomics allow the determination of metabolic reaction rates thereby discriminating metabolite level changes in HE in terms of production, consumption and clearance.

A novel platform for automated high-throughput fluxome profiling of metabolic variants.

Advances in metabolic engineering are enabling the creation of a large number of cell factories. However, high-throughput platforms do not yet exist for rapidly analyzing the metabolic network of the engineered cells. To fill the gap, we developed an integrated solution for fluxome profiling of large sets of biological systems and conditions. This platform combines a robotic system for (13)C-labelling experiments and sampling of labelled material with NMR-based isotopic fingerprinting and automated data interpretation. As a proof-of-concept, this workflow was applied to discriminate between Escherichia coli mutants with gradual expression of the glucose-6-phosphate dehydrogenase. Metabolic variants were clearly discriminated while pathways that support metabolic flexibility towards modulation of a single enzyme were elucidating. By directly connecting the data flow between cell cultivation and flux quantification, considerable advances in throughput, robustness, release of resources and screening capacity were achieved. This will undoubtedly facilitate the development of efficient cell factories.

Selected Ion Monitoring for Orbitrap-Based Metabolomics.

Orbitrap mass spectrometry in full scan mode enables the simultaneous detection of hundreds of metabolites and their isotope-labeled forms. Yet, sensitivity remains limiting for many metabolites, including low-concentration species, poor ionizers, and low-fractional-abundance isotope-labeled forms in isotope-tracing studies. Here, we explore selected ion monitoring (SIM) as a means of sensitivity enhancement. The analytes of interest are enriched in the orbitrap analyzer by using the quadrupole as a mass filter to select particular ions. In tissue extracts, SIM significantly enhances the detection of ions of low intensity, as indicated by improved signal-to-noise (S/N) ratios and measurement precision. In addition, SIM improves the accuracy of isotope-ratio measurements. SIM, however, must be deployed with care, as excessive accumulation in the orbitrap of similar m/z ions can lead, via space-charge effects, to decreased performance (signal loss, mass shift, and ion coalescence). Ion accumulation can be controlled by adjusting settings including injection time and target ion quantity. Overall, we suggest using a full scan to ensure broad metabolic coverage, in tandem with SIM, for the accurate quantitation of targeted low-intensity ions, and provide methods deploying this approach to enhance metabolome coverage.

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

(13)C-Metabolic flux analysis ((13)C-MFA) is a powerful model-based analysis technique for determining intracellular metabolic fluxes in living cells. It has become a standard tool in many labs for quantifying cell physiology, e.g., in metabolic engineering, systems biology, biotechnology, and biomedical research. With the increasing number of (13)C-MFA studies published each year, it is now ever more important to provide practical guidelines for performing and publishing (13)C-MFA studies so that quality is not sacrificed as the number of publications increases. The main purpose of this paper is to provide an overview of good practices in (13)C-MFA, which can eventually be used as minimum data standards for publishing (13)C-MFA studies. The motivation for this work is two-fold: (1) currently, there is no general consensus among researchers and journal editors as to what minimum data standards should be required for publishing (13)C-MFA studies; as a result, there are great discrepancies in terms of quality and consistency; and (2) there is a growing number of studies that cannot be reproduced or verified independently due to incomplete information provided in these publications. This creates confusion, e.g. when trying to reconcile conflicting results, and hinders progress in the field. Here, we review current status in the (13)C-MFA field and highlight some of the shortcomings with regards to (13)C-MFA publications. We then propose a checklist that encompasses good practices in (13)C-MFA. We hope that these guidelines will be a valuable resource for the community and allow (13)C-flux studies to be more easily reproduced and accessed by others in the future.