Stable isotope-assisted metabolomics to detect metabolic flux changes in mammalian cell cultures

H/D Exchange Coupled with 2H-labeled Stable Isotope-Assisted Metabolomics Discover Transformation Products of Contaminants of Emerging Concern

Stable isotope-assisted metabolomics (SIAM) is a powerful tool for discovering transformation products (TPs) of contaminants. Nevertheless, the high cost or lack of isotope-labeled analytes limits its application. In-house H/D (hydrogen/deuterium) exchange reactions enable direct 2H labeling to target analytes with favorable reaction conditions, providing intuitive and easy-to-handle approaches for environmentally relevant laboratories to obtain cost-effective 2H-labeled contaminants of emerging concern (CECs). We first combined the use of in-house H/D exchange and 2H-SIAM to discover potential TPs of 6PPD (N-1,3-dimethylbutyl-N'-phenyl-p-phenylenediamine), providing a new strategy for finding TPs of CECs. 6PPD-d9 was obtained by in-house H/D exchange with favorable reaction conditions, and the impurities were carefully studied. Incomplete deuteride, for instance, 6PPD-d8 in this study, constitutes a major part of the impurities. Nevertheless, it has few adverse effects on the 2H-SIAM pipeline in discovering TPs of 6PPD. The 2H-SIAM pipeline annotated 9 TPs of 6PPD, and commercial standards further confirmed the annotated 6PPDQ (2-anilino-5-(4-methylpentan-2-ylamino)cyclohexa-2,5-diene-1,4-dione) and PPPD (N-phenyl-p-phenylenediamine). Additionally, a possible new formation mechanism for 6PPDQ was proposed, highlighting the performance of the strategy. In summary, this study highlighted a new strategy for discovering the TPs of CECs and broadening the application of SIAM in environmental studies.

Global Tracking of Transformation Products of Environmental Contaminants by 2H-Labeled Stable Isotope-Assisted Metabolomics

Stable isotope-assisted metabolomics (SIAM) enables global tracking of isotopic labels in nontargeted metabolomics in living organisms. However, its application in tracking transformation products (TPs, as metabolites of contaminants) of environmental contaminants is still a challenge due to limits in methodology, unmatured algorithms, and the high cost of ¹³C-labeled contaminants. Therefore, we developed a ²H-SIAM pipeline coupled with a highly flexible algorithm ²H-SIAM(1.0) (https://github.com/kechen1984/2H-SIAM), facilitating tracking TPs of contaminants in the environmental matrix. A detailed discussion illustrates the theory, behavior, and prospect of ²H-SIAM. We demonstrate that the proposed ²H-SIAM pipeline has unique advantages over ¹³C-SIAM, for example, cost-effective ²H-labeled contaminants, easy synthesis of ²H-labeled emerging contaminants, and providing more structural information. A pyrene soil degradation study further confirmed its high performance. It efficiently discarded 99% of noise signals and extracted 52 features from the nontargeted high resolution mass spectrometry (HRMS) data. Among them, 13 features were annotated as TPs of pyrene with identification confidence from Level 2a to Level 5, and 5 TPs were reported for the first time. In conclusion, the proposed ²H-SIAM pipeline is powerful in tracking potential TPs of environmental contaminants with unique advantages.

Isotopically nonstationary 13C metabolic flux analysis in resting and activated human platelets

Platelet metabolism is linked to platelet hyper- and hypoactivity in numerous human diseases. Developing a detailed understanding of the link between metabolic shifts and platelet activation state is integral to improving human health. Here, we show the first application of isotopically nonstationary ¹³C metabolic flux analysis to quantitatively measure carbon fluxes in both resting and thrombin activated platelets. Metabolic flux analysis results show that resting platelets primarily metabolize glucose to lactate via glycolysis, while acetate is oxidized to fuel the tricarboxylic acid cycle. Upon activation with thrombin, a potent platelet agonist, platelets increase their uptake of glucose 3-fold. This results in an absolute increase in flux throughout central metabolism, but when compared to resting platelets they redistribute carbon dramatically. Activated platelets decrease relative flux to the oxidative pentose phosphate pathway and TCA cycle from glucose and increase relative flux to lactate. These results provide the first report of reaction-level carbon fluxes in platelets and allow us to distinguish metabolic fluxes with much higher resolution than previous studies.

Stable Isotopic Tracer Phospholipidomics Reveals Contributions of Key Phospholipid Biosynthetic Pathways to Low Hepatocyte Phosphatidylcholine to Phosphatidylethanolamine Ratio Induced by Free Fatty Acids

There is a strong association between hepatocyte phospholipid homeostasis and non-alcoholic fatty liver disease (NAFLD). The phosphatidylcholine to phosphatidylethanolamine ratio (PC/PE) often draws special attention as genetic and dietary disruptions to this ratio can provoke steatohepatitis and other signs of NAFLD. Here we demonstrated that excessive free fatty acid (1:2 mixture of palmitic and oleic acid) alone was able to significantly lower the phosphatidylcholine to phosphatidylethanolamine ratio, along with substantial alterations to phospholipid composition in rat hepatocytes. This involved both a decrease in hepatocyte phosphatidylcholine (less prominent) and an increase in phosphatidylethanolamine, with the latter contributing more to the lowered ratio. Stable isotopic tracer phospholipidomic analysis revealed several previously unidentified changes that were triggered by excessive free fatty acid. Importantly, the enhanced cytidine diphosphate (CDP)-ethanolamine pathway activity appeared to be driven by the increased supply of preferred fatty acid substrates. By contrast, the phosphatidylethanolamine N-methyl transferase (PEMT) pathway was restricted by low endogenous methionine and consequently low S-adenosylmethionine, which resulted in a concomitant decrease in phosphatidylcholine and accumulation of phosphatidylethanolamine. Overall, our study identified several previously unreported links in the relationship between hepatocyte free fatty acid overload, phospholipid homeostasis, and the development of NAFLD.

Magnesium magnetic isotope effects in microbiology

Two main properties of atomic nuclei-mass and nuclear magnetic moments-are origin of many biological effects. Mass-dependent isotope effects have been studied for a long time. The effect of magnetic isotopes having a magnetic moment and spin was first shown in the early twenty-first century for the magnetic isotope magnesium ²⁵Mg on enzymatic ATP synthesis. This stimulated the search for experimental evidence and theoretical justification of magnetic nuclei influence on biological processes. This review contains the results of scientific research on the magnesium magnetic isotope effects in microbiology. Microorganisms have been found to be sensitive to the presence of nuclear magnetic moment of magnesium isotope ²⁵Mg compared with non-magnetic ^24,26Mg isotopes.

Assessing the Impact of the Nutrient Microenvironment on the Metabolism of Effector CD8+ T Cells

Immune cell function is tightly regulated by cellular metabolism, which in turn is strongly linked to the nutrient availability in the microenvironment surrounding the cells. This link is critical for effector CD8⁺ T cells which, after activation, must migrate from nutrient-rich environments into nutrient-scarce regions such as the tumor microenvironment. Assessing how nutrient availability modulates the metabolism of effector CD8⁺ T cells is thus key for understanding how harsh environments may impair their proliferation and effector function. Here, we describe an approach to systematically study the impact of the nutrient microenvironment on the metabolism of effector CD8⁺ T cells, based on performing stable ¹³C isotope labeling measurements on in vitro-differentiated murine effector CD8⁺ T cells.

Metabolomics studies on corticosterone-induced PC12 cells: A strategy for evaluating an in vitro depression model and revealing the metabolic regulation mechanism

There are three types of differentiated (un-, poorly- and well-differentiated) PC12 cells, which have been widely used as a model system for depression studies after the administration of corticosterone (CORT). In order to investigate the underlying metabolic profiles of CORT-induced PC12 cells and evaluate the suitable differentiated types of PC12 cells for use in depressive studies, proton nuclear magnetic resonance (¹H NMR) metabolomics coupled with network analysis approaches were employed. The results showed that CORT induced metabolic alterations in PC12 cells. There were 8 and 13 common differential metabolites in intracellular and extracellular extracts, respectively, of the three types of differentiated PC12 cells in response to CORT treatment, and the perturbed metabolic pathways were involved in amino acid metabolism, glutathione metabolism, pyruvate metabolism and inositol phosphate metabolism. Eighteen protein targets of depression were identified from the five different metabolic pathways from metabolomics and network analysis among the three types of CORT-induced differentiated PC12 cells, and these proteins were all found in the pathways that were perturbed by CORT treatment of poorly-differentiated PC12 cells. These results may indicate that the metabolism of CORT-induced PC12 cells is similar to the pathogenesis of depression, and poorly-differentiated PC12 cells are the most suitable cells for depressive research among the distinct types of differentiated PC12 cells. Thus, an effective predicative strategy to evaluate the in vitro disease models could be referenced.

High-Precision In Situ 87Sr/86Sr Analyses through Microsampling on Solid Samples: Applications to Earth and Life Sciences

An analytical protocol for high-precision, in situ microscale isotopic investigations is presented here, which combines the use of a high-performing mechanical microsampling device and high-precision TIMS measurements on micro-Sr samples, allowing for excellent results both in accuracy and precision. The present paper is a detailed methodological description of the whole analytical procedure from sampling to elemental purification and Sr-isotope measurements. The method offers the potential to attain isotope data at the microscale on a wide range of solid materials with the use of minimally invasive sampling. In addition, we present three significant case studies for geological and life sciences, as examples of the various applications of microscale ⁸⁷Sr/⁸⁶Sr isotope ratios, concerning (i) the pre-eruptive mechanisms triggering recent eruptions at Nisyros volcano (Greece), (ii) the dynamics involved with the initial magma ascent during Eyjafjallajökull volcano's (Iceland) 2010 eruption, which are usually related to the precursory signals of the eruption, and (iii) the environmental context of a MIS 3 cave bear, Ursus spelaeus. The studied cases show the robustness of the methods, which can be also be applied in other areas, such as cultural heritage, archaeology, petrology, and forensic sciences.

Stable isotope compounds - production, detection, and application

Stable isotopes are used in wide fields of application from natural tracers in biology, geology and archeology through studies of metabolic fluxes to their application as tracers in quantitative proteomics and structural biology. We review the use of stable isotopes of biogenic elements (H, C, N, O, S, Mg, Se) with the emphasis on hydrogen and its heavy isotope deuterium. We will discuss the limitations of enriching various compounds in stable isotopes when produced in living organisms. Finally, we overview methods for measuring stable isotopes, focusing on methods for detection in single cells in situ and their exploitation in modern biotechnologies.

Deuterium- and Tritium-Labelled Compounds: Applications in the Life Sciences

Hydrogen isotopes are unique tools for identifying and understanding biological and chemical processes. Hydrogen isotope labelling allows for the traceless and direct incorporation of an additional mass or radioactive tag into an organic molecule with almost no changes in its chemical structure, physical properties, or biological activity. Using deuterium-labelled isotopologues to study the unique mass-spectrometric patterns generated from mixtures of biologically relevant molecules drastically simplifies analysis. Such methods are now providing unprecedented levels of insight in a wide and continuously growing range of applications in the life sciences and beyond. Tritium (³ H), in particular, has seen an increase in utilization, especially in pharmaceutical drug discovery. The efforts and costs associated with the synthesis of labelled compounds are more than compensated for by the enhanced molecular sensitivity during analysis and the high reliability of the data obtained. In this Review, advances in the application of hydrogen isotopes in the life sciences are described.

Unravelling the complexity of signalling networks in cancer: A review of the increasing role for computational modelling

Cancer induction is a highly complex process involving hundreds of different inducers but whose eventual outcome is the same. Clearly, it is essential to understand how signalling pathways and networks generated by these inducers interact to regulate cell behaviour and create the cancer phenotype. While enormous strides have been made in identifying key networking profiles, the amount of data generated far exceeds our ability to understand how it all "fits together". The number of potential interactions is astronomically large and requires novel approaches and extreme computation methods to dissect them out. However, such methodologies have high intrinsic mathematical and conceptual content which is difficult to follow. This review explains how computation modelling is progressively finding solutions and also revealing unexpected and unpredictable nano-scale molecular behaviours extremely relevant to how signalling and networking are coherently integrated. It is divided into linked sections illustrated by numerous figures from the literature describing different approaches and offering visual portrayals of networking and major conceptual advances in the field. First, the problem of signalling complexity and data collection is illustrated for only a small selection of known oncogenes. Next, new concepts from biophysics, molecular behaviours, kinetics, organisation at the nano level and predictive models are presented. These areas include: visual representations of networking, Energy Landscapes and energy transfer/dissemination (entropy); diffusion, percolation; molecular crowding; protein allostery; quinary structure and fractal distributions; energy management, metabolism and re-examination of the Warburg effect. The importance of unravelling complex network interactions is then illustrated for some widely-used drugs in cancer therapy whose interactions are very extensive. Finally, use of computational modelling to develop micro- and nano- functional models ("bottom-up" research) is highlighted. The review concludes that computational modelling is an essential part of cancer research and is vital to understanding network formation and molecular behaviours that are associated with it. Its role is increasingly essential because it is unravelling the huge complexity of cancer induction otherwise unattainable by any other approach.

Towards quantitative mass spectrometry-based metabolomics in microbial and mammalian systems

Metabolome analyses are a suite of analytical approaches that enable us to capture changes in the metabolome (small molecular weight components, typically less than 1500 Da) in biological systems. Mass spectrometry (MS) has been widely used for this purpose. The key challenge here is to be able to capture changes in a reproducible and reliant manner that is representative of the events that take place in vivo Typically, the analysis is carried out in vitro, by isolating the system and extracting the metabolome. MS-based approaches enable us to capture metabolomic changes with high sensitivity and resolution. When developing the technique for different biological systems, there are similarities in challenges and differences that are specific to the system under investigation. Here, we review some of the challenges in capturing quantitative changes in the metabolome with MS based approaches, primarily in microbial and mammalian systems.This article is part of the themed issue 'Quantitative mass spectrometry'.

Metabolomics in rheumatic diseases: desperately seeking biomarkers

Metabolomics enables the profiling of large numbers of small molecules in cells, tissues and biological fluids. These molecules, which include amino acids, carbohydrates, lipids, nucleotides and their metabolites, can be detected quantitatively. Metabolomic methods, often focused on the information-rich analytical techniques of NMR spectroscopy and mass spectrometry, have potential for early diagnosis, monitoring therapy and defining disease pathogenesis in many therapeutic areas, including rheumatic diseases. By performing global metabolite profiling, also known as untargeted metabolomics, new discoveries linking cellular pathways to biological mechanisms are being revealed and are shaping our understanding of cell biology, physiology and medicine. These pathways can potentially be targeted to diagnose and treat patients with immune-mediated diseases.

Applications of NMR spectroscopy to systems biochemistry

The past decades of advancements in NMR have made it a very powerful tool for metabolic research. Despite its limitations in sensitivity relative to mass spectrometric techniques, NMR has a number of unparalleled advantages for metabolic studies, most notably the rigor and versatility in structure elucidation, isotope-filtered selection of molecules, and analysis of positional isotopomer distributions in complex mixtures afforded by multinuclear and multidimensional experiments. In addition, NMR has the capacity for spatially selective in vivo imaging and dynamical analysis of metabolism in tissues of living organisms. In conjunction with the use of stable isotope tracers, NMR is a method of choice for exploring the dynamics and compartmentation of metabolic pathways and networks, for which our current understanding is grossly insufficient. In this review, we describe how various direct and isotope-edited 1D and 2D NMR methods can be employed to profile metabolites and their isotopomer distributions by stable isotope-resolved metabolomic (SIRM) analysis. We also highlight the importance of sample preparation methods including rapid cryoquenching, efficient extraction, and chemoselective derivatization to facilitate robust and reproducible NMR-based metabolomic analysis. We further illustrate how NMR has been applied in vitro, ex vivo, or in vivo in various stable isotope tracer-based metabolic studies, to gain systematic and novel metabolic insights in different biological systems, including human subjects. The pathway and network knowledge generated from NMR- and MS-based tracing of isotopically enriched substrates will be invaluable for directing functional analysis of other 'omics data to achieve understanding of regulation of biochemical systems, as demonstrated in a case study. Future developments in NMR technologies and reagents to enhance both detection sensitivity and resolution should further empower NMR in systems biochemical research.

The use of 'Omics technology to rationally improve industrial mammalian cell line performance

Biologics represent an increasingly important class of therapeutics, with 7 of the 10 top selling drugs from 2013 being in this class. Furthermore, health authority approval of biologics in the immuno-oncology space is expected to transform treatment of patients with debilitating and deadly diseases. The growing importance of biologics in the healthcare field has also resulted in the recent approvals of several biosimilars. These recent developments, combined with pressure to provide treatments at lower costs to payers, are resulting in increasing need for the industry to quickly and efficiently develop high yielding, robust processes for the manufacture of biologics with the ability to control quality attributes within narrow distributions. Achieving this level of manufacturing efficiency and the ability to design processes capable of regulating growth, death and other cellular pathways through manipulation of media, feeding strategies, and other process parameters will undoubtedly be facilitated through systems biology tools generated in academic and public research communities. Here we discuss the intersection of systems biology, 'Omics technologies, and mammalian bioprocess sciences. Specifically, we address how these methods in conjunction with traditional monitoring techniques represent a unique opportunity to better characterize and understand host cell culture state, shift from an empirical to rational approach to process development and optimization of bioreactor cultivation processes. We summarize the following six key areas: (i) research applied to parental, non-recombinant cell lines; (ii) systems level datasets generated with recombinant cell lines; (iii) datasets linking phenotypic traits to relevant biomarkers; (iv) data depositories and bioinformatics tools; (v) in silico model development, and (vi) examples where these approaches have been used to rationally improve cellular processes. We critically assess relevant and state of the art research being conducted in academic, government and industrial laboratories. Furthermore, we apply our expertise in bioprocess to define a potential model for integration of these systems biology approaches into biologics development.

Metabolic characterization of cultured mammalian cells by mass balance analysis, tracer labeling experiments and computer-aided simulations

Studying metabolic directions and flow rates in cultured mammalian cells can provide key information for understanding metabolic function in the fields of cancer research, drug discovery, stem cell biology, and antibody production. In this work, metabolic engineering methodologies including medium component analysis, (13)C-labeling experiments, and computer-aided simulation analysis were applied to characterize the metabolic phenotype of soft tissue sarcoma cells derived from p53-null mice. Cells were cultured in medium containing [1-(13)C] glutamine to assess the level of reductive glutamine metabolism via the reverse reaction of isocitrate dehydrogenase (IDH). The specific uptake and production rates of glucose, organic acids, and the 20 amino acids were determined by time-course analysis of cultured media. Gas chromatography-mass spectrometry analysis of the (13)C-labeling of citrate, succinate, fumarate, malate, and aspartate confirmed an isotopically steady state of the cultured cells. After removing the effect of naturally occurring isotopes, the direction of the IDH reaction was determined by computer-aided analysis. The results validated that metabolic engineering methodologies are applicable to soft tissue sarcoma cells derived from p53-null mice, and also demonstrated that reductive glutamine metabolism is active in p53-null soft tissue sarcoma cells under normoxia.

Metabolomics of adherent mammalian cells by capillary electrophoresis-mass spectrometry: HT-29 cells as case study

In this work, the optimization of an effective protocol for cell metabolomics is described with special emphasis in the sample preparation and subsequent analysis of intracellular metabolites from adherent mammalian cells by capillary electrophoresis-mass spectrometry. As case study, colon cancer HT-29 cells, a human cell model to investigate colon cancer, are employed. The feasibility of the whole method for cell metabolomics is demonstrated via a fast and sensitive profiling of the intracellular metabolites HT-29 cells by capillary electrophoresis-time-of-flight mass spectrometry (CE-TOF MS). The suitability of this methodology is further corroborated through the examination of the metabolic changes in the polyamines pathway produced in colon cancer HT-29 cells by difluoromethylornithine (DFMO), a known potent ornithine decarboxylase inhibitor. The selection of the optimum extraction conditions allowed a higher sample volume injection that led to an increase in CE-TOF MS sensitivity. Following a non-targeted metabolomics approach, 10 metabolites (namely, putrescine, ornithine, gamma-aminobutyric acid (GABA), oxidized and reduced glutathione, 5'-deoxy-5'-(methylthio)adenosine, N-acetylputrescine, cysteinyl-glycine, spermidine and an unknown compound) were found to be significantly altered by DFMO (p<0.05) in HT-29 cells. In addition to the effect of DFMO on polyamine metabolism, minor modifications of other metabolic pathways (e.g., related to intracellular thiol redox state) were observed.

Methods and advances in metabolic flux analysis: a mini-review

Metabolic flux analysis (MFA) is one of the pillars of metabolic engineering. Over the past three decades, it has been widely used to quantify intracellular metabolic fluxes in both native (wild type) and engineered biological systems. Through MFA, changes in metabolic pathway fluxes are quantified that result from genetic and/or environmental interventions. This information, in turn, provides insights into the regulation of metabolic pathways and may suggest new targets for further metabolic engineering of the strains. In this mini-review, we discuss and classify the various methods of MFA that have been developed, which include stoichiometric MFA, (13)C metabolic flux analysis, isotopic non-stationary (13)C metabolic flux analysis, dynamic metabolic flux analysis, and (13)C dynamic metabolic flux analysis. For each method, we discuss key advantages and limitations and conclude by highlighting important recent advances in flux analysis approaches.

Principles and practice of lipidomics

The technical advances in mass spectrometry, particularly the development of (ultra)-high-resolution/mass accuracy measurement capabilities in combination with refinement of soft ionization techniques, have increased the application and success of lipidomics to answer biological questions in relation to lipid metabolism. Together with other omics technologies, lipidomics has become an important tool to practice systems biology as lipids comprise a very significant part of the metabolome and play pleiotropic roles in cellular functions. As an increasing number of disorders are linked to lipid metabolism, lipidomics is used to search for biomarkers, understand disease mechanism and follow the efficacy of therapeutic options. This review provides a first introduction to the major methodological strategies currently used for mass spectrometry-based lipidomics and associated data pre-processing and analysis.

How to measure metabolic fluxes: a taxonomic guide for (13)C fluxomics

Metabolic reaction rates (fluxes) contribute fundamentally to our understanding of metabolic phenotypes and mechanisms of cellular regulation. Stable isotope-based fluxomics integrates experimental data with biochemical networks and mathematical modeling to 'measure' the in vivo fluxes within an organism that are not directly observable. In recent years, (13)C fluxomics has evolved into a technology with great experimental, analytical, and mathematical diversity. This review aims at establishing a unified taxonomy by means of which the various fluxomics methods can be compared to each other. By linking the developed modeling approaches to recent studies, their challenges and opportunities are put into perspective. The proposed classification serves as a guide for scientific 'travelers' who are striving to resolve research questions with the currently available (13)C fluxomics toolset.

Metabolic network discovery by top-down and bottom-up approaches and paths for reconciliation

The primary focus in the network-centric analysis of cellular metabolism by systems biology approaches is to identify the active metabolic network for the condition of interest. Two major approaches are available for the discovery of the condition-specific metabolic networks. One approach starts from genome-scale metabolic networks, which cover all possible reactions known to occur in the related organism in a condition-independent manner, and applies methods such as the optimization-based Flux-Balance Analysis to elucidate the active network. The other approach starts from the condition-specific metabolome data, and processes the data with statistical or optimization-based methods to extract information content of the data such that the active network is inferred. These approaches, termed bottom-up and top-down, respectively, are currently employed independently. However, considering that both approaches have the same goal, they can both benefit from each other paving the way for the novel integrative analysis methods of metabolome data- and flux-analysis approaches in the post-genomic era. This study reviews the strengths of constraint-based analysis and network inference methods reported in the metabolic systems biology field; then elaborates on the potential paths to reconcile the two approaches to shed better light on how the metabolism functions.

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.

Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery

Metabolism represents the 'sharp end' of systems biology, because changes in metabolite concentrations are necessarily amplified relative to changes in the transcriptome, proteome and enzyme activities, which can be modulated by drugs. To understand such behaviour, we therefore need (and increasingly have) reliable consensus (community) models of the human metabolic network that include the important transporters. Small molecule 'drug' transporters are in fact metabolite transporters, because drugs bear structural similarities to metabolites known from the network reconstructions and from measurements of the metabolome. Recon2 represents the present state-of-the-art human metabolic network reconstruction; it can predict inter alia: (i) the effects of inborn errors of metabolism; (ii) which metabolites are exometabolites, and (iii) how metabolism varies between tissues and cellular compartments. However, even these qualitative network models are not yet complete. As our understanding improves so do we recognise more clearly the need for a systems (poly)pharmacology.

A stable-isotope mass spectrometry-based metabolic footprinting approach to analyze exudates from phytoplankton

Phytoplankton exudates play an important role in pelagic ecology and biogeochemical cycles of elements. Exuded compounds fuel the microbial food web and often encompass bioactive secondary metabolites like sex pheromones, allelochemicals, antibiotics, or feeding attractants that mediate biological interactions. Despite this importance, little is known about the bioactive compounds present in phytoplankton exudates. We report a stable-isotope metabolic footprinting method to characterise exudates from aquatic autotrophs. Exudates from (13)C-enriched alga were concentrated by solid phase extraction and analysed by high-resolution Fourier transform ion cyclotron resonance mass spectrometry. We used the harmful algal bloom forming dinoflagellate Alexandrium tamarense to prove the method. An algorithm was developed to automatically pinpoint just those metabolites with highly (13)C-enriched isotope signatures, allowing us to discover algal exudates from the complex seawater background. The stable-isotope pattern (SIP) of the detected metabolites then allowed for more accurate assignment to an empirical formula, a critical first step in their identification. This automated workflow provides an effective way to explore the chemical nature of the solutes exuded from phytoplankton cells and will facilitate the discovery of novel dissolved bioactive compounds.

Towards high resolution analysis of metabolic flux in cells and tissues

Metabolism extracts chemical energy from nutrients, uses this energy to form building blocks for biosynthesis, and interconverts between various small molecules that coordinate the activities of cellular pathways. The metabolic state of a cell is increasingly recognized to determine the phenotype of not only metabolically active cell types such as liver, muscle, and adipose, but also other specialized cell types such as neurons and immune cells. This review focuses on methods to quantify intracellular reaction flux as a measure of cellular metabolic activity, with emphasis on studies involving cells of mammalian tissue. Two key areas are highlighted for future development, single cell metabolomics and noninvasive imaging, which could enable spatiotemporally resolved analysis and thereby overcome issues of heterogeneity, a distinctive feature of tissue metabolism.

Mathematical modeling of folate metabolism

Folate metabolism is a complex biological process that is influenced by many variables including transporters, cofactors, and enzymes. Mathematical models provide a useful tool to evaluate this complex system and to elucidate hypotheses that would be otherwise untenable to test in vitro or in vivo. Forty years of model development and refinement along with enhancements in technology have led to systematic improvement in our biological understanding of these models. However, increased complexity does not always lead to increased understanding, and a balanced approach to modeling the system is often advantageous. This approach should address questions about sensitivity of the model to variation and incorporate genomic data. The folate model is a useful platform for investigating the effects of antifolates on the folate pathway. The utility of the model is demonstrated through interrogation of drug resistance, drug-drug interactions, drug selectivity, and drug doses and schedules. Mathematics can be used to create models with the ability to design and improve rationale therapeutic interventions.