Identifying functional metabolic shifts in heart failure with the integration of omics data and a cardiomyocyte-specific, genome-scale model

Abstract

SummaryThe heart is a metabolic omnivore, known to consume many different carbon substrates in order to maintain function. In diseased states, the heart’s metabolism can shift between different carbon substrates; however, there is some disagreement in the field as to the metabolic shifts seen in end-stage heart failure and whether all heart failure converges to a common metabolic phenotype. Here, we present a new, validated cardiomyocyte-specific GEnome-scale metabolic Network REconstruction (GENRE), iCardio, and use the model to identify common shifts in metabolic functions across heart failure omics datasets. We demonstrate the utility of iCardio in interpreting heart failure gene expression data by identifying Tasks Inferred from Differential Expression (TIDEs) which represent metabolic functions associated with changes in gene expression. We identify decreased NO and Neu5Ac synthesis as common metabolic markers of heart failure across datasets. Further, we highlight the differences in metabolic functions seen across studies, further highlighting the complexity of heart failure. The methods presented for constructing a tissue-specific model and identifying TIDEs can be extended to multiple tissue and diseases of interest.

Multi-omics approaches for revealing the complexity of cardiovascular disease

“…However, human models have greatly expanded since Recon1 to enable a more comprehensive description of human metabolism, and this has been captured in a recently published human GEM, termed iHsa [ 106 ], which was built in parallel as an expansion of the Human Metabolic Reaction 2.0 database [ 107 ]. This GEM offered the opportunity to create a more comprehensive cardiomyocyte-specific metabolic model ( Figure 3C ), termed iCardio [ 108 ], built using iHsa and data from the Human Protein Atlas (HPA) [ 16 ]. The model was integrated with multiple heart failure omics datasets to identify standard shifts in metabolic functions that are associated with heart failure, and this approach identified decreased NO ( Figure 3D ) and Neu5Ac synthesis as common metabolic markers of heart failure across transcriptomics datasets [ 108 ].…”

“…This GEM offered the opportunity to create a more comprehensive cardiomyocyte-specific metabolic model ( Figure 3C ), termed iCardio [ 108 ], built using iHsa and data from the Human Protein Atlas (HPA) [ 16 ]. The model was integrated with multiple heart failure omics datasets to identify standard shifts in metabolic functions that are associated with heart failure, and this approach identified decreased NO ( Figure 3D ) and Neu5Ac synthesis as common metabolic markers of heart failure across transcriptomics datasets [ 108 ]. The GEM approach can also be utilized to identify causal factors for cardiac remodelling whether it is loss of function mutations in cardiovascular-related genes altering metabolic fluxes [ 109 ] or different diets leading to profound changes in the cardiac mitochondria, which may ultimately result in cell damage and heart failure [ 110 ].…”