Isotopomer analysis of myocardial substrate metabolism: a systems biology approach

Cardiovascular Metabolomics

Disturbances in cardiac metabolism underlie most cardiovascular diseases. Metabolomics, one of the newer omics technologies, has emerged as a powerful tool for defining changes in both global and cardiac-specific metabolism that occur across a spectrum of cardiovascular disease states. Findings from metabolomics studies have contributed to better understanding of the metabolic changes that occur in heart failure and ischemic heart disease and have identified new cardiovascular disease biomarkers. As technologies advance, the metabolomics field continues to evolve rapidly. In this review, we will discuss the current state of metabolomics technologies, including consideration of various metabolomics platforms and elements of study design; the emerging utility of stable isotopes for metabolic flux studies; and the use of metabolomics to better understand specific cardiovascular diseases, with an emphasis on recent advances in the field.

Comprehensive metabolic modeling of multiple 13C-isotopomer data sets to study metabolism in perfused working hearts

In many forms of cardiomyopathy, alterations in energy substrate metabolism play a key role in disease pathogenesis. Stable isotope tracing in rodent heart perfusion systems can be used to determine cardiac metabolic fluxes, namely those relative fluxes that contribute to pyruvate, the acetyl-CoA pool, and pyruvate anaplerosis, which are critical to cardiac homeostasis. Methods have previously been developed to interrogate these relative fluxes using isotopomer enrichments of measured metabolites and algebraic equations to determine a predefined metabolic flux model. However, this approach is exquisitely sensitive to measurement error, thus precluding accurate relative flux parameter determination. In this study, we applied a novel mathematical approach to determine relative cardiac metabolic fluxes using ¹³C-metabolic flux analysis (¹³C-MFA) aided by multiple tracer experiments and integrated data analysis. Using ¹³C-MFA, we validated a metabolic network model to explain myocardial energy substrate metabolism. Four different ¹³C-labeled substrates were queried (i.e., glucose, lactate, pyruvate, and oleate) based on a previously published study. We integrated the analysis of the complete set of isotopomer data gathered from these mouse heart perfusion experiments into a single comprehensive network model that delineates substrate contributions to both pyruvate and acetyl-CoA pools at a greater resolution than that offered by traditional methods using algebraic equations. To our knowledge, this is the first rigorous application of ¹³C-MFA to interrogate data from multiple tracer experiments in the perfused heart. We anticipate that this approach can be used widely to study energy substrate metabolism in this and other similar biological systems.

Estimating biological elementary flux modes that decompose a flux distribution by the minimal branching property

MOTIVATION

Elementary flux mode (EFM) is a useful tool in constraint-based modeling of metabolic networks. The property that every flux distribution can be decomposed as a weighted sum of EFMs allows certain applications of EFMs to studying flux distributions. The existence of biologically infeasible EFMs and the non-uniqueness of the decomposition, however, undermine the applicability of such methods. Efforts have been made to find biologically feasible EFMs by incorporating information from transcriptional regulation and thermodynamics. Yet, no attempt has been made to distinguish biologically feasible EFMs by considering their graphical properties. A previous study on the transcriptional regulation of metabolic genes found that distinct branches at a branch point metabolite usually belong to distinct metabolic pathways. This suggests an intuitive property of biologically feasible EFMs, i.e. minimal branching.

RESULTS

We developed the concept of minimal branching EFM and derived the minimal branching decomposition (MBD) to decompose flux distributions. Testing in the core Escherichia coli metabolic network indicated that MBD can distinguish branches at branch points and greatly reduced the solution space in which the decomposition is often unique. An experimental flux distribution from a previous study on mouse cardiomyocyte was decomposed using MBD. Comparison with decomposition by a minimum number of EFMs showed that MBD found EFMs more consistent with established biological knowledge, which facilitates interpretation. Comparison of the methods applied to a complex flux distribution in Lactococcus lactis similarly showed the advantages of MBD. The minimal branching EFM concept underlying MBD should be useful in other applications.

CONTACT

sinhu@bio.dtu.dk or p.ji@polyu.edu.hk

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Doxorubicin increases oxidative metabolism in HL-1 cardiomyocytes as shown by 13C metabolic flux analysis

Doxorubicin (DXR), an anticancer drug, is limited in its use due to severe cardiotoxic effects. These effects are partly caused by disturbed myocardial energy metabolism. We analyzed the effects of therapeutically relevant but nontoxic DXR concentrations for their effects on metabolic fluxes, cell respiration, and intracellular ATP. (13)C isotope labeling studies using [U-(13)C(6)]glucose, [1,2-(13)C(2)]glucose, and [U-(13)C(5)]glutamine were carried out on HL-1 cardiomyocytes exposed to 0.01 and 0.02 μM DXR and compared with the untreated control. Metabolic fluxes were calculated by integrating production and uptake rates of extracellular metabolites (glucose, lactate, pyruvate, and amino acids) as well as (13)C-labeling in secreted lactate derived from the respective (13)C-labeled substrates into a metabolic network model. The investigated DXR concentrations (0.01 and 0.02 μM) had no effect on cell viability and beating of the HL-1 cardiomyocytes. Glycolytic fluxes were significantly reduced in treated cells at tested DXR concentrations. Oxidative metabolism was significantly increased (higher glucose oxidation, oxidative decarboxylation, TCA cycle rates, and respiration) suggesting a more efficient use of glucose carbon. These changes were accompanied by decrease of intracellular ATP. We conclude that DXR in nanomolar range significantly changes central carbon metabolism in HL-1 cardiomyocytes, which results in a higher coupling of glycolysis and TCA cycle. The myocytes probably try to compensate for decreased intracellular ATP, which in turn may be the result of a loss of NADH electrons via either formation of reactive oxygen species or electron shunting.

Eukaryotic metabolism: measuring compartment fluxes

Metabolic compartmentation represents a major characteristic of eukaryotic cells. The analysis of compartmented metabolic networks is complicated by separation and parallelization of pathways, intracellular transport, and the need for regulatory systems to mediate communication between interdependent compartments. Metabolic flux analysis (MFA) has the potential to reveal compartmented metabolic events, although it is a challenging task requiring demanding experimental techniques and sophisticated modeling. At present no ready-made solution can be provided to cope with the complexity of compartmented metabolic networks, but new powerful tools are emerging. This review gives an overview of different strategies to approach this issue, focusing on different MFA methods and highlighting the additional information that should be included to improve the outcome of an experiment and associate estimation procedures.

Metabolic flux analysis gives an insight on verapamil induced changes in central metabolism of HL-1 cells

Verapamil has been shown to inhibit glucose transport in several cell types. However, the consequences of this inhibition on central metabolism are not well known. In this study we focused on verapamil induced changes in metabolic fluxes in a murine atrial cell line (HL-1 cells). These cells were adapted to serum free conditions and incubated with 4 μM verapamil and [U-¹³C₅] glutamine. Specific extracellular metabolite uptake/production rates together with mass isotopomer fractions in alanine and glutamate were implemented into a metabolic network model to calculate metabolic flux distributions in the central metabolism. Verapamil decreased specific glucose consumption rate and glycolytic activity by 60%. Although the HL-1 cells show Warburg effect with high lactate production, verapamil treated cells completely stopped lactate production after 24 h while maintaining growth comparable to the untreated cells. Calculated fluxes in TCA cycle reactions as well as NADH/FADH₂ production rates were similar in both treated and untreated cells. This was confirmed by measurement of cell respiration. Reduction of lactate production seems to be the consequence of decreased glucose uptake due to verapamil. In case of tumors, this may have two fold effects; firstly depriving cancer cells of substrate for anaerobic glycolysis on which their growth is dependent; secondly changing pH of the tumor environment, as lactate secretion keeps the pH acidic and facilitates tumor growth. The results shown in this study may partly explain recent observations in which verapamil has been proposed to be a potential anticancer agent. Moreover, in biotechnological production using cell lines, verapamil may be used to reduce glucose uptake and lactate secretion thereby increasing protein production without introduction of genetic modifications and application of more complicated fed-batch processes.

Metabolic flux analysis in systems biology of mammalian cells

Reaction rates or metabolic fluxes reflect the integrated phenotype of genome, transcriptome and proteome interactions, including regulation at all levels of the cellular hierarchy. Different methods have been developed in the past to analyse intracellular fluxes. However, compartmentation of mammalian cells, varying utilisation of multiple substrates, reversibility of metabolite uptake and production, unbalanced growth behaviour and adaptation of cells to changing environment during cultivation are just some reasons that make metabolic flux analysis (MFA) in mammalian cell culture more challenging compared to microorganisms. In this article MFA using the metabolite balancing methodology and the advantages and disadvantages of (13)C MFA in mammalian cell systems are reviewed. Application examples of MFA in the optimisation of cell culture processes for the production of biopharmaceuticals are presented with a focus on the metabolism of the main industrial workhorse. Another area in which mammalian cell culture plays a key role is in medical and toxicological research. It is shown that MFA can be used to understand pathophysiological mechanisms and can assist in understanding effects of drugs or other compounds on cellular metabolism.

Exploring mitochondrial evolution and metabolism organization principles by comparative analysis of metabolic networks

The endosymbiotic theory proposed that mitochondrial genomes are derived from an alpha-proteobacterium-like endosymbiont, which was concluded from sequence analysis. We rebuilt the metabolic networks of mitochondria and 22 relative species, and studied the evolution of mitochondrial metabolism at the level of enzyme content and network topology. Our phylogenetic results based on network alignment and motif identification supported the endosymbiotic theory from the point of view of systems biology for the first time. It was found that the mitochondrial metabolic network were much more compact than the relative species, probably related to the higher efficiency of oxidative phosphorylation of the specialized organelle, and the network is highly clustered around the TCA cycle. Moreover, the mitochondrial metabolic network exhibited high functional specificity to the modules. This work provided insight to the understanding of mitochondria evolution, and the organization principle of mitochondrial metabolic network at the network level.

Imaging and modeling of myocardial metabolism

Current imaging methods have focused on evaluation of myocardial anatomy and function. However, since myocardial metabolism and function are interrelated, metabolic myocardial imaging techniques, such as positron emission tomography, single photon emission tomography, and magnetic resonance spectroscopy present novel opportunities for probing myocardial pathology and developing new therapeutic approaches. Potential clinical applications of metabolic imaging include hypertensive and ischemic heart disease, heart failure, cardiac transplantation, as well as cardiomyopathies. Furthermore, response to therapeutic intervention can be monitored using metabolic imaging. Analysis of metabolic data in the past has been limited, focusing primarily on isolated metabolites. Models of myocardial metabolism, however, such as the oxygen transport and cellular energetics model and constraint-based metabolic network modeling, offer opportunities for evaluation interactions between greater numbers of metabolites in the heart. In this review, the roles of metabolic myocardial imaging and analysis of metabolic data using modeling methods for expanding our understanding of cardiac pathology are discussed.

Advances in analysis of microbial metabolic fluxes via (13)C isotopic labeling

Metabolic flux analysis via (13)C labeling ((13)C MFA) quantitatively tracks metabolic pathway activity and determines overall enzymatic function in cells. Three core techniques are necessary for (13)C MFA: (1) a steady state cell culture in a defined medium with labeled-carbon substrates; (2) precise measurements of the labeling pattern of targeted metabolites; and (3) evaluation of the data sets obtained from mass spectrometry measurements with a computer model to calculate the metabolic fluxes. In this review, we summarize recent advances in the (13)C-flux analysis technologies, including mini-bioreactor usage for tracer experiments, isotopomer analysis of metabolites via high resolution mass spectrometry (such as GC-MS, LC-MS, or FT-ICR), high performance and large-scale isotopomer modeling programs for flux analysis, and the integration of fluxomics with other functional genomics studies. It will be shown that there is a significant value for (13)C-based metabolic flux analysis in many biological research fields.

Optimization in computational systems biology

Optimization aims to make a system or design as effective or functional as possible. Mathematical optimization methods are widely used in engineering, economics and science. This commentary is focused on applications of mathematical optimization in computational systems biology. Examples are given where optimization methods are used for topics ranging from model building and optimal experimental design to metabolic engineering and synthetic biology. Finally, several perspectives for future research are outlined.

Integrating metabolomics and phenomics with systems models of cardiac hypoxia

Hypoxia is the major cause of necrotic cell death in myocardial infarction. Cellular energy supply and demand under hypoxic conditions is regulated by many interacting signaling and transcriptional networks, which complicates studies on individual proteins and pathways. We apply an integrated systems approach to understand the metabolic and functional response to hypoxia in muscle cells of the fruit fly Drosophila melanogaster. In addition to its utility as a hypoxia-tolerant model organism, Drosophila also offers advantages due to its small size, fecundity, and short life cycle. These traits, along with a large library of single-gene mutations, motivated us to develop new, computer-automated technology for gathering in vivo measurements of heart function under hypoxia for a large number of mutant strains. Phenotype data can be integrated with in silico cellular networks, metabolomic data, and microarrays to form qualitative and quantitative network models for prediction and hypothesis generation. Here we present a framework for a systems approach to hypoxia in the cardiac myocyte, starting from nuclear magnetic resonance (NMR) metabolomics, a constraint-based metabolic model, and phenotypic profiles.

Systems analysis of energy metabolism elucidates the affected respiratory chain complex in Leigh's syndrome

Leigh's syndrome is a complex neurological disease with little known correlation between causes and symptoms. Mutations in pyruvate dehydrogenase and electron transport chain complexes have been associated with this syndrome, although the identification of affected enzymes is difficult, if not impossible, with non-invasive clinical tests. In this study, isotopomer analysis is used to characterize the metabolic phenotype of normal and Leigh's syndrome fibroblasts (GM01503), thereby identifying affected enzymes in the diseased cells. Fibroblasts are grown with DMEM media enriched with (13)C labeled glucose. Amino acids from media and proteins as well as lactate are analyzed with GC-MS to identify their label distributions. A computational model accounting for all major pathways in fibroblast metabolism (including 430 metabolites and 508 reactions) is built to determine the metabolic steady states of the normal and Leigh's cell lines based on measured substrate uptake and secretion rates and isotopomer data. Results show that (i) Leigh's syndrome affected cells have slower metabolism than control fibroblasts as evidenced by their overall slower substrate utilization and lower secretion of end products; (ii) intracellular fluxes predicted by the models, some of which are validated by biochemical studies published in the literature, show that the respiratory chain in Leigh's affected cells can produce ATP at a similar rate as the controls, but with a more restricted flux range; and (iii) mutations causing the defects observed in the Leigh's cells are likely to be in succinate cytochrome c reductase.