Expanding the concepts and tools of metabolic engineering to elucidate cancer metabolism

Boundary flux analysis: an emerging strategy for investigating metabolic pathway activity in large cohorts

Many biological phenotypes are rooted in metabolic pathway activity rather than the concentrations of individual metabolites. Despite this, most metabolomics studies only capture steady-state metabolism - not metabolic flux. Although sophisticated metabolic flux analysis strategies have been developed, these methods are technically challenging and difficult to implement in large-cohort studies. Recently, a new boundary flux analysis (BFA) approach has emerged that captures large-scale metabolic flux phenotypes by quantifying changes in metabolite levels in the media of cultured cells. This approach is advantageous because it is relatively easy to implement yet captures complex metabolic flux phenotypes. We describe the opportunities and challenges of BFA and illustrate how it can be harnessed to investigate a wide transect of biological phenomena.

A novel insight for high-rate and low-efficiency glucose metabolism in depression through stable isotope-resolved metabolomics in CUMS-induced rats

BACKGROUND

Existing research has suggested that depression results in disorders of glucose metabolism in the organism which causing insufficient energy supply. However, the overall changes in glucose metabolism that arise from depression have not been clarified.

METHODS

In this study, the depression-like behavior in chronically unpredictable mild stressed rats was investigated, and the fate of glucose was tracked through isotope tracing and mass spectrometry, with a focus on metabolite changes in cecal contents.

RESULTS

As indicated by the results, the isotopic results of cecal contents can indicate the metabolic end of the organism. Moreover, the TCA cycle activity was notably reduced, and the gluconeogenesis pathway was abnormally up-regulated in the CUMS-induced rats. The organism expedited other glucose metabolism pathways to make up for the insufficiency of energy. As a result, the activity of the inefficient glycolysis pathway was increased.

LIMITATIONS

Existing research has only investigated the metabolism of 13C-glucose, and lipids and proteins have been rarely explored.

CONCLUSIONS

The chronic unpredictable mild stress can inhibit the entry of pyruvate into mitochondria in SD rats, such that the activity of TCA is reduced, and insufficient energy supply is caused. The organism is capable of expediting other glucose metabolism rate pathways to make up for the insufficiency of energy, whereas it still cannot compensate for the loss of energy. As a result, CUMS-induced rats exhibited high-rate and low-efficiency glucose metabolism.

Simultaneous Quantification of the Concentration and Carbon Isotopologue Distribution of Polar Metabolites in a Single Analysis by Gas Chromatography and Mass Spectrometry

¹³C-isotope tracing is a frequently employed approach to study metabolic pathway activity. When combined with the subsequent quantification of absolute metabolite concentrations, this enables detailed characterization of the metabolome in biological specimens and facilitates computational time-resolved flux quantification. Classically, a ¹³C-isotopically labeled sample is required to quantify ¹³C-isotope enrichments and a second unlabeled sample for the quantification of metabolite concentrations. The rationale for a second unlabeled sample is that the current methods for metabolite quantification rely mostly on isotope dilution mass spectrometry (IDMS) and thus isotopically labeled internal standards are added to the unlabeled sample. This excludes the absolute quantification of metabolite concentrations in ¹³C-isotopically labeled samples. To address this issue, we have developed and validated a new strategy using an unlabeled internal standard to simultaneously quantify metabolite concentrations and ¹³C-isotope enrichments in a single ¹³C-labeled sample based on gas chromatography-mass spectrometry (GC/MS). The method was optimized for amino acids and citric acid cycle intermediates and was shown to have high analytical precision and accuracy. Metabolite concentrations could be quantified in small tissue samples (≥20 mg). Also, we applied the method on ¹³C-isotopically labeled mammalian cells treated with and without a metabolic inhibitor. We proved that we can quantify absolute metabolite concentrations and ¹³C-isotope enrichments in a single ¹³C-isotopically labeled sample.

Polymeric Nanoparticle Versus Liposome Formulations: Comparative Physicochemical and Metabolomic Studies as L-Carnitine Delivery Systems

L-Carnitine has attracted much more attention especially in the treatment of crucial diseases such as diabetes, regional slimming, and obesity because of its metabolic activities. However, because of its short half-life, low bioavailability, and inability to be stored in the body, frequent dosing is required. In this study, L-carnitine-loaded liposome (lipo-carnitine) and PLGA nanoparticle (nano-carnitine) formulations were prepared and characterized. For lipo-carnitine and nano-carnitine formulations, particle size values were 97.88 ± 2.96 nm and 250.90 ± 6.15 nm; polydispersity index values were 0.35 ± 0.01 and 0.22 ± 0.03; zeta potential values were 6.36 ± 0.54 mV and - 32.80 ± 2.26 mV; and encapsulation efficiency percentage values were 14.26 ± 3.52% and 21.93 ± 4.17%, respectively. Comparative in vitro release studies of novel formulations and solution of L-carnitine revealed that L-carnitine released 90% of its content at the end of 1st hour. On the other hand, lipo-carnitine and nano-carnitine formulations maintained a controlled-release profile for 12 h. The in vitro efficacy of the formulations on cardiac fibroblasts (CFs) was evaluated by metabolomic studies and pathway analysis. Besides the prolonged release, lipo-carnitine/nano-carnitine formulations were also found to be effective on amino acid, carbohydrate, and lipid metabolisms. As a result, innovative nano-formulations were successfully developed as an alternative to conventional preparations which are available on the market.

Simultaneous Measurement of Metabolite Concentration and Isotope Incorporation by Mass Spectrometry

Studies of the topology, functioning, and regulation of metabolic systems are based on two main types of information that can be measured by mass spectrometry: the (absolute or relative) concentration of metabolites and their isotope incorporation in ¹³C-labeling experiments. These data are currently obtained from two independent experiments because the ¹³C-labeled internal standard (IS) used to determine the concentration of a given metabolite overlaps the ¹³C-mass fractions from which its ¹³C-isotopologue distribution (CID) is quantified. Here, we developed a generic method with a dedicated processing workflow to obtain these two sets of information simultaneously in a unique sample collected from a single cultivation, thereby reducing by a factor of 2 both the number of cultivations to perform and the number of samples to collect, prepare, and analyze. The proposed approach is based on an IS labeled with other isotope(s) that can be resolved from the ¹³C-mass fractions of interest. As proof-of-principle, we analyzed amino acids using a doubly labeled ¹⁵N¹³C-cell extract as IS. Extensive evaluation of the proposed approach shows a similar accuracy and precision compared to state-of-the-art approaches. We demonstrate the value of this approach by investigating the dynamic response of amino acids metabolism in mammalian cells upon activation of the protein kinase R (PKR)-like endoplasmic reticulum kinase (PERK), a key component of the unfolded protein response. Integration of metabolite concentrations and isotopic profiles reveals a reduced de novo biosynthesis of amino acids upon PERK activation. The proposed approach is generic and can be applied to other (micro)organisms, analytical platforms, isotopic tracers, or classes of metabolites.

Inhibition of mitochondrial 2-oxoglutarate dehydrogenase impairs viability of cancer cells in a cell-specific metabolism-dependent manner

2-Oxoglutarate dehydrogenase (OGDH) of the tricarboxylic acid (TCA) cycle is often implied to be inactive in cancer, but this was not experimentally tested. We addressed the question through specific inhibition of OGDH by succinyl phosphonate (SP). SP action on different cancer cells was investigated using indicators of cellular viability and reactive oxygen species (ROS), metabolic profiling and transcriptomics. Relative sensitivity of various cancer cells to SP changed with increasing SP exposure and could differ in the ATP- and NAD(P)H-based assays. Glioblastoma responses to SP revealed metabolic sub-types increasing or decreasing cellular ATP/NAD(P)H ratio under OGDH inhibition. Cancer cell homeostasis was perturbed also when viability indicators were SP-resistant, e.g. in U87 and N2A cells. The transcriptomics database analysis showed that the SP-sensitive cells, such as A549 and T98G, exhibit the lowest expression of OGDH compared to other TCA cycle enzymes, associated with higher expression of affiliated pathways utilizing 2-oxoglutarate. Metabolic profiling confirmed the dependence of cellular SP reactivity on cell-specific expression of the pathways. Thus, oxidative decarboxylation of 2-oxoglutarate is significant for the interdependent homeostasis of NAD(P)H, ATP, ROS and key metabolites in various cancer cells. Assessment of cell-specific responses to OGDH inhibition is of diagnostic value for anticancer strategies.

Traditional and novel tools to probe the mitochondrial metabolism in health and disease

Mitochondrial metabolism links energy production to other essential cellular processes such as signaling, cellular differentiation, and apoptosis. In addition to producing adenosine triphosphate (ATP) as an energy source, mitochondria are responsible for the synthesis of a myriad of important metabolites and cofactors such as tetrahydrofolate, α-ketoacids, steroids, aminolevulinic acid, biotin, lipoic acid, acetyl-CoA, iron-sulfur clusters, heme, and ubiquinone. Furthermore, mitochondria and their metabolism have been implicated in aging and several human diseases, including inherited mitochondrial disorders, cardiac dysfunction, heart failure, neurodegenerative diseases, diabetes, and cancer. Therefore, there is great interest in understanding mitochondrial metabolism and the complex relationship it has with other cellular processes. A large number of studies on mitochondrial metabolism have been conducted in the last 50 years, taking a broad range of approaches. In this review, we summarize and discuss the most commonly used tools that have been used to study different aspects of the metabolism of mitochondria: ranging from dyes that monitor changes in the mitochondrial membrane potential and pharmacological tools to study respiration or ATP synthesis, to more modern tools such as genetically encoded biosensors and trans-omic approaches enabled by recent advances in mass spectrometry, computation, and other technologies. These tools have allowed the large number of studies that have shaped our current understanding of mitochondrial metabolism. WIREs Syst Biol Med 2017, 9:e1373. doi: 10.1002/wsbm.1373 For further resources related to this article, please visit the WIREs website.

A Method to Constrain Genome-Scale Models with 13C Labeling Data

Current limitations in quantitatively predicting biological behavior hinder our efforts to engineer biological systems to produce biofuels and other desired chemicals. Here, we present a new method for calculating metabolic fluxes, key targets in metabolic engineering, that incorporates data from ¹³C labeling experiments and genome-scale models. The data from ¹³C labeling experiments provide strong flux constraints that eliminate the need to assume an evolutionary optimization principle such as the growth rate optimization assumption used in Flux Balance Analysis (FBA). This effective constraining is achieved by making the simple but biologically relevant assumption that flux flows from core to peripheral metabolism and does not flow back. The new method is significantly more robust than FBA with respect to errors in genome-scale model reconstruction. Furthermore, it can provide a comprehensive picture of metabolite balancing and predictions for unmeasured extracellular fluxes as constrained by ¹³C labeling data. A comparison shows that the results of this new method are similar to those found through ¹³C Metabolic Flux Analysis (¹³C MFA) for central carbon metabolism but, additionally, it provides flux estimates for peripheral metabolism. The extra validation gained by matching 48 relative labeling measurements is used to identify where and why several existing COnstraint Based Reconstruction and Analysis (COBRA) flux prediction algorithms fail. We demonstrate how to use this knowledge to refine these methods and improve their predictive capabilities. This method provides a reliable base upon which to improve the design of biological systems.

While metabolic fluxes constitute the most direct window into a cell’s metabolism, their accurate measurement is non trivial. The gold standard for flux measurement involves providing a labeled feed where some of the carbon atoms have been substituted by isotopes with higher atomic mass (¹³C instead of ¹²C). The ensuing labeling found in intracellular metabolites is then used to computationally infer the metabolic fluxes that produced the observed pattern. However, this procedure is typically performed with small metabolic models encompassing only central carbon metabolism. The genomic revolution has afforded us easily available genomes and, with them, comprehensive genome-scale models of cellular metabolism. It would be desirable to use the ¹³C labeling experimental data to constrain genome-scale models: these data constrain fluxes very effectively and provide in the labeling data fit an obvious proof that the underlying model correctly explains measured quantities. Here, we introduce a rigorous, self-consistent method that uses the full amount of information contained in ¹³C labeling data to constrain fluxes for a genome-scale model where underlying assumptions are explicitly stated.

A roadmap for interpreting (13)C metabolite labeling patterns from cells

Measuring intracellular metabolism has increasingly led to important insights in biomedical research. (13)C tracer analysis, although less information-rich than quantitative (13)C flux analysis that requires computational data integration, has been established as a time-efficient method to unravel relative pathway activities, qualitative changes in pathway contributions, and nutrient contributions. Here, we review selected key issues in interpreting (13)C metabolite labeling patterns, with the goal of drawing accurate conclusions from steady state and dynamic stable isotopic tracer experiments.

Decoding the dynamics of cellular metabolism and the action of 3-bromopyruvate and 2-deoxyglucose using pulsed stable isotope-resolved metabolomics

BACKGROUND

Cellular metabolism is highly dynamic and continuously adjusts to the physiological program of the cell. The regulation of metabolism appears at all biological levels: (post-) transcriptional, (post-) translational, and allosteric. This regulatory information is expressed in the metabolome, but in a complex manner. To decode such complex information, new methods are needed in order to facilitate dynamic metabolic characterization at high resolution.

RESULTS

Here, we describe pulsed stable isotope-resolved metabolomics (pSIRM) as a tool for the dynamic metabolic characterization of cellular metabolism. We have adapted gas chromatography-coupled mass spectrometric methods for metabolomic profiling and stable isotope-resolved metabolomics. In addition, we have improved robustness and reproducibility and implemented a strategy for the absolute quantification of metabolites.

CONCLUSIONS

By way of examples, we have applied this methodology to characterize central carbon metabolism of a panel of cancer cell lines and to determine the mode of metabolic inhibition of glycolytic inhibitors in times ranging from minutes to hours. Using pSIRM, we observed that 2-deoxyglucose is a metabolic inhibitor, but does not directly act on the glycolytic cascade.

13C isotope-assisted methods for quantifying glutamine metabolism in cancer cells

Glutamine has recently emerged as a key substrate to support cancer cell proliferation, and the quantification of its metabolic flux is essential to understand the mechanisms by which this amino acid participates in the metabolic rewiring that sustains the survival and growth of neoplastic cells. Glutamine metabolism involves two major routes, glutaminolysis and reductive carboxylation, both of which begin with the deamination of glutamine to glutamate and the conversion of glutamate into α-ketoglutarate. In glutaminolysis, α-ketoglutarate is oxidized via the tricarboxylic acid cycle and decarboxylated to pyruvate. In reductive carboxylation, α-ketoglutarate is reductively converted into isocitrate, which is isomerized to citrate to supply acetyl-CoA for de novo lipogenesis. Here, we describe methods to quantify the metabolic flux of glutamine through these two routes, as well as the contribution of glutamine to lipid synthesis. Examples of how these methods can be applied to study metabolic pathways of oncological relevance are provided.

Kinetic isotope effects significantly influence intracellular metabolite (13) C labeling patterns and flux determination

Rigorous mathematical modeling of carbon-labeling experiments allows estimation of fluxes through the pathways of central carbon metabolism, yielding powerful information for basic scientific studies as well as for a wide range of applications. However, the mathematical models that have been developed for flux determination from (13) C labeling data have commonly neglected the influence of kinetic isotope effects on the distribution of (13) C label in intracellular metabolites, as these effects have often been assumed to be inconsequential. We have used measurements of the (13) C isotope effects on the pyruvate dehydrogenase enzyme from the literature to model isotopic fractionation at the pyruvate node and quantify the modeling errors expected to result from the assumption that isotope effects are negligible. We show that under some conditions kinetic isotope effects have a significant impact on the (13) C labeling patterns of intracellular metabolites, and the errors associated with neglecting isotope effects in (13) C-metabolic flux analysis models can be comparable in size to measurement errors associated with GC-MS. Thus, kinetic isotope effects must be considered in any rigorous assessment of errors in (13) C labeling data, goodness-of-fit between model and data, confidence intervals of estimated metabolic fluxes, and statistical significance of differences between estimated metabolic flux distributions.

From measurement to implementation of metabolic fluxes

The intracellular reaction rates (fluxes) are the ultimate outcome of the activities of the complete inventory (from DNA to metabolite) and in their sum determine the cellular phenotype. The genotype-phenotype relationship is fundamental in such different fields as cancer research and biotechnology. Here, we summarize the developments in determining metabolic fluxes, inferring major pathways from the DNA-sequence, estimating optimal flux distributions, and how these flux distributions can be achieved in vivo. The technical advances to intervene with the many levels of the cellular architecture allow the implementation of new strategies in for example Metabolic Engineering.