Myocardial phenotyping using isotopomer analysis of metabolic fluxes

Metabolic Tracing Using Stable Isotope-Labeled Substrates and Mass Spectrometry in the Perfused Mouse Heart

There has been a resurgence of interest for the field of cardiac metabolism catalyzed by evidence demonstrating a role of metabolic dysregulation in the pathogenesis of heart disease as well as the increased need for new therapeutic targets for patients with these diseases. In this regard, measuring substrate fluxes is critical in providing insight into the dynamics of cellular metabolism and in delineating the regulation of metabolite production and utilization. This chapter provides a comprehensive description of concepts, guidelines, and tips to assess metabolic fluxes relevant to energy substrate metabolism using (13)C-labeled substrates and (13)C-isotopomer analysis by gas chromatography-mass spectrometry (GC-MS), and the ex vivo working heart as study model. The focus will be on the mouse and on flux parameters, which are commonly assessed in the field, namely, those relevant to substrate selection for energy metabolism, specifically the relative contribution of carbohydrate (glucose, lactate, and pyruvate) and fatty acid oxidation to acetyl-CoA formation for citrate synthesis, glycolysis, as well as anaplerosis. We provide detailed procedures for the heart isolation and perfusion in the working mode as well as for sample processing for metabolite extraction and analysis by GC-MS and subsequent data processing for calculation of metabolic flux parameters. Finally, we address practical considerations and discuss additional applications and future challenges.

Sensitivity analysis of flux determination in heart by H₂ ¹⁸O -provided labeling using a dynamic Isotopologue model of energy transfer pathways

To characterize intracellular energy transfer in the heart, two organ-level methods have frequently been employed: inversion and saturation transfer, and dynamic labeling. Creatine kinase (CK) fluxes obtained by following oxygen labeling have been considerably smaller than the fluxes determined by saturation transfer. It has been proposed that dynamic labeling determines net flux through CK shuttle, whereas saturation transfer measures total unidirectional flux. However, to our knowledge, no sensitivity analysis of flux determination by oxygen labeling has been performed, limiting our ability to compare flux distributions predicted by different methods. Here we analyze oxygen labeling in a physiological heart phosphotransfer network with active CK and adenylate kinase (AdK) shuttles and establish which fluxes determine the labeling state. A mathematical model consisting of a system of ordinary differential equations was composed describing enrichment in each phosphoryl group and inorganic phosphate. By varying flux distributions in the model and calculating the labeling, we analyzed labeling sensitivity to different fluxes in the heart. We observed that the labeling state is predominantly sensitive to total unidirectional CK and AdK fluxes and not to net fluxes. We conclude that measuring dynamic incorporation of into the high-energy phosphotransfer network in heart does not permit unambiguous determination of energetic fluxes with a higher magnitude than the ATP synthase rate when the bidirectionality of fluxes is taken into account. Our analysis suggests that the flux distributions obtained using dynamic labeling, after removing the net flux assumption, are comparable with those from inversion and saturation transfer.

In heart, the movement of energy metabolites between force-producing myosin, other ATPases, and mitochondria is vital for its function and closely related to heart pathologies. In addition to diffusion, transport of ATP, ADP, Pi, and phosphocreatine occurs along parallel pathways such as the adenylate kinase and creatine kinase shuttles. Two organ-level methods have been developed to study the relative flux through these pathways. However, their results differ. It was recently demonstrated that studies often suffer from the exclusion of compartmentation from their metabolic models. One study overcame this limitation by using compartmental models and statistical methods on multiple experiments. Here, we analyzed the sensitivity of the other method - dynamic labeling of phosphoryl groups and inorganic phosphate. For that, we composed a mathematical model tracking enrichment of the metabolites and evaluated sensitivity of labeling to different flux distribution scenarios. Our study shows that the dynamic method provides a measure of total flux, and not net flux as presumed previously, making the fluxes predicted from both methods consistent. Importantly, conclusions derived on the basis of labeling analysis, particularly those regarding the net flux through the shuttles in control and pathological cases, need to be reevaluated.

Metabolic fluxes and beyond-systems biology understanding and engineering of microbial metabolism

The recent years have seen tremendous progress towards the understanding of microbial metabolism on a higher level of the entire functional system. Hereby, huge achievements including the sequencing of complete genomes and efficient post-genomic approaches provide the basis for a new, fascinating era of research-analysis of metabolic and regulatory properties on a global scale. Metabolic flux (fluxome) analysis displays the first systems oriented approach to unravel the physiology of microorganisms since it combines experimental data with metabolic network models and allows determining absolute fluxes through larger networks of central carbon metabolism. Hereby, fluxes are of central importance for systems level understanding because they fundamentally represent the cellular phenotype as integrated output of the cellular components, i.e. genes, transcripts, proteins, and metabolites. A currently emerging and promising area of research in systems biology and systems metabolic engineering is therefore the integration of fluxome data in multi-omics studies to unravel the multiple layers of control that superimpose the flux network and enable its optimal operation under different environmental conditions.