Identifying functional metabolic shifts in heart failure with the integration of omics data and a heart-specific, genome-scale model
Bonnie V. Dougherty,
Kristopher D. Rawls,
Glynis L. Kolling,
Kalyan C. Vinnakota,
Anders Wallqvist,
Jason A. Papin
Affiliations
Bonnie V. Dougherty
Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
Kristopher D. Rawls
Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
Glynis L. Kolling
Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA; Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA 22908, USA
Kalyan C. Vinnakota
Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD 21702, USA; The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, USA
Anders Wallqvist
Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD 21702, USA
Jason A. Papin
Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA; Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA 22908, USA; Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA; Corresponding author
Summary: In diseased states, the heart can shift to use different carbon substrates, measured through changes in uptake of metabolites by imaging methods or blood metabolomics. However, it is not known whether these measured changes are a result of transcriptional changes or external factors. Here, we explore transcriptional changes in late-stage heart failure using publicly available data integrated with a model of heart metabolism. First, we present a heart-specific genome-scale metabolic network reconstruction (GENRE), iCardio. Next, 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 gene expression for nitric oxide (NO) and N-acetylneuraminic acid (Neu5Ac) synthesis as common metabolic markers of heart failure. The methods presented here for constructing a tissue-specific model and identifying TIDEs can be extended to multiple tissues and diseases of interest.
