Cardiovascular Metabolomics

Abstract

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.

Keywords: amino acids; cardiovascular diseases; heart failure; ketone bodies; metabolism.

Figure 1

A) The traditional view of the omics hierarchy. Genetic variation (genomics) leads to changes in gene expression (transcriptomics) which affects variations in proteins (proteomics). Protein variation determines, in part, enzymatic activity and thus metabolic variation (metabolomics) and rates through metabolic pathways (fluxomics). B) A metabolism-centric view of the omics hierarchy. The metabolic phenotype, including a static snapshot of metabolite concentrations as well as the dynamics of flux through metabolic pathways, is influenced by many factors. Variations in metabolism, in turn, can modify genomic (via epigenomics), transcriptomic (via epitranscriptomics) and proteomic (via post-translational modifications – PTM) outputs. These modifications feedback to alter global metabolism and thus create a complex molecular regulatory network that mediates healthy and diseased states.

Figure 2

General metabolomics workflow, which can be viewed as four distinct phases. Upon generation of a biological question of interest, an experiment is designed, performed, and specimens are collected/prepared for metabolomics assays. Secondly, samples are profiled by NMR or mass spectrometry and metabolite features are detected. Third, statistical analyses are performed to determine significant metabolite features, which can then be identified through database searching and quantified with targeted analyses. Lastly, metabolite data can be further interrogated with pathway/cluster analyses before the data is interpreted in light of the original hypothesis. In certain circumstances, the metabolomics analyses can result in mechanism/model formulation, which can be probed with stable isotope and flux analysis techniques. Broadly, metabolomics studies are hypothesis generating and can begin another cycle of studies.

Figure 3

Selection of ¹³C-substrates, which result in different labeling of acetyl-CoA, allows determination of substrate fluxes into the TCA cycle. Acetyl-CoA enrichments can be measured directly or through proxy metabolites, such as glutamate (NMR) or citrate (MS). (A) Substrates that produce [1-¹³C]acetyl-CoA enrich citrate/glutamate on the 5^th carbon. (B) Substrates that result in [2-¹³C]acetyl-CoA label citrate/glutamate on the 4^th carbon. (C) Substrates that provide [1,2-¹³C2]acetyl-CoA will enrich both the 4^th and 5^th carbons of citrate/glutamate. To interpret substrate fluxes from an NMR/MS experiment, the substrates of interest should be chosen to differ in acetyl-CoA labeling (e.g. [U-¹³C]glucose, C & [odd-¹³C]fatty acid, A).