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Integrated landscape of cardiac metabolism in end-stage human nonischemic dilated cardiomyopathy

Nature Cardiovascular Research volume 1, pages 817–829 (2022)Cite this article

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Abstract

Heart failure (HF) is a leading cause of mortality. Failing hearts undergo profound metabolic changes, but a comprehensive evaluation in humans is lacking. We integrate plasma and cardiac tissue metabolomics of 678 metabolites, genome-wide RNA-sequencing, and proteomic studies to examine metabolic status in 87 explanted human hearts from 39 patients with end-stage HF compared with 48 nonfailing donors. We confirm bioenergetic defects in human HF and reveal selective depletion of adenylate purines required for maintaining ATP levels. We observe substantial reductions in fatty acids and acylcarnitines in failing tissue, despite plasma elevations, suggesting defective import of fatty acids into cardiomyocytes. Glucose levels, in contrast, are elevated. Pyruvate dehydrogenase, which gates carbohydrate oxidation, is de-repressed, allowing increased lactate and pyruvate burning. Tricarboxylic acid cycle intermediates are significantly reduced. Finally, bioactive lipids are profoundly reprogrammed, with marked reductions in ceramides and elevations in lysoglycerophospholipids. These data unveil profound metabolic abnormalities in human failing hearts.

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Fig. 1: Cardiac and plasma metabolic alterations in human HF.
Fig. 2: Loss of adenylate purines and high-energy phosphate molecules in HF.
Fig. 3: Evidence of defective FA transport and FAO in failing hearts.
Fig. 4: Defects in glycolysis and evidence of increased lactate oxidation in failing hearts.
Fig. 5: Evidence of depressed cardiac anaplerosis and TCA cycle in failing hearts.
Fig. 6: Aberrant ketone metabolism in failing hearts.
Fig. 7: Aberrant amino acid metabolism in failing hearts.
Fig. 8: Decreased ceramides and elevated LPLs in failing hearts.

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Data availability

The analyzed metabolomics data are available in the supplementary information files. The raw metabolomics data generated in the present study are available from the corresponding author upon reasonable request. The RNA-seq data are publicly available in the NCBI GEO repository with accession no. GSE14190. The proteomics data are available in the ProteomeXchange Consortium with the dataset accession no. PXD008934. Source data are provided with this paper.

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Acknowledgements

We thank the Gift-of-Life Donor Program, Philadelphia, PA, who helped provide nonfailing heart tissue from unused donor hearts for this research. This work was supported by funding from National Heart, Lung, and Blood Institute (NHLBI; grant nos. R01-HL152446 to Z.A., T32 HL 7954-20 to E.F.), the Gund Family Fund to K.B.M., Department of Defense (grant no. W81XWH18-1-0503 to Z.A.), Edward Mallinckrodt Jr. Foundation to C.J., NHLBI (grant no. F30 HL142186-01A1) and the Blavatnik Family Foundation to D.M., grant no. R01GM132261 to N.W.S. and National Institutes of Health Diabetes Research Center (grant no. P30 DK019525). Human heart tissue was procured via support from the following grants: nos. R01 AG17022, R01 HL089847 and R01 HL105993 to K.B.M.

Author information

Authors and Affiliations

  1. Perelman School of Medicine, Cardiovascular Institute, University of Pennsylvania, Philadelphia, PA, USA

    Emily Flam, Danielle Murashige, Yifan Yang, Michael P. Morley, Kenneth C. Bedi Jr, Jeff Brandimarto, Benjamin L. Prosser, Thomas Cappola, Kenneth B. Margulies & Zolt Arany

  2. Department of Chemistry and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA

    Cholsoon Jang & Joshua D. Rabinowitz

  3. Department of Biological Chemistry, University of California Irvine, Irvine, CA, USA

    Cholsoon Jang & Sunhee Jung

  4. Center for Metabolic Disease Research, Department of Cardiovascular Science, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA

    Daniel S. Kantner, Hannah Pepper & Nathaniel W. Snyder

  5. Department of Physiology, Pennsylvania Muscle Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Benjamin L. Prosser

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  1. Emily Flam
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Contributions

E.F., Z.A., C.J. and K.B.M. designed the study. E.F created the cohort and processed samples. C.J. and J.D.R. performed MS and contributed to data analysis. C.J. and S.J. performed lipidomics analysis. N.W.S., H.P. and D.S.K. performed MS for CoA species, contributed to data analysis and contributed unique reagents. E.F. performed remaining data analysis. K.C.B. and K.B.M. did all human sample procurement. D.M. contributed to data analysis. Y.Y, M.P.M. and T.C. provided RNA-seq dataset and assisted with data integration and analysis. J.B. assisted with sample handling and processing. B.L.P. provided proteomics data. E.F. and Z.A. wrote the manuscript and created the figures. All authors discussed the results and edited the manuscript and figures.

Corresponding author

Correspondence to Zolt Arany.

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Nature Cardiovascular Research thanks Rong Tian and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1

a, b) Correlation between cohort 1 and 2 tissue (a) or plasma (b) of fold-changes (FC) between non-failing and failing samples of metabolites significantly altered (FDR < 0.05) in at least one cohort. One outlier was removed from analysis in figure b. c) Correlation between cohorts 1 and 2 of plasma fold-change of metabolites significantly altered in both cohorts (FDR < 0.05). d) Similarity matrix of samples from cohort 1 based on metabolomics data. Size of square is proportional to Pearson correlation coefficient. e) Principal component analysis (PCA) of non-failing tissue samples from cohort 1. Data points represent patients and are pseudo-colored to reflect sex of donor. f) PCA of non-failing tissue samples from cohort 1. Data points represent patients and are pseudo-colored to reflect race of donor. g) PCA plot of all tissue samples from cohort 1. Data points represent patients and are pseudo-colored to reflect heart failure (HF) vs nonfailing (NF).

Source data

Extended Data Fig. 2

a) PCA plot of mRNA expression from all tissue samples. Data points represent patients and are pseudo-colored to reflect heart failure (HF) vs nonfailing (NF). b) Similarity matrix of RNA-seq data between individual samples. c) Volcano plot of RNA-seq data from tissue samples. d) Correlation between the current cohort (combined 1 and 2) and a previously published data set (Sweet et al.) of significant fold-changes (FC; nominal p-val < 0.05 by two-sided t-test) in mRNA expression between non-failing vs failing cardiac samples.

Source data

Extended Data Fig. 3

a-c) Relative abundance of metabolites involved in adenylate (a), guanylate (b), or pyrimidine (c) metabolism in cardiac tissue from nonfailing donors (NF) or subjects with heart failure (HF). Whiskers represent 10th and 90th percentiles, midline represents median, edges of boxes represent first and third quartiles, and points represent data points outside the 10th-90th percentile range. N = 48 NF and N = 39 HF samples. ATP FDR = 0.000904; adenine FDR = 1.09E-05; adenosine FDR = 1.4E-05; hypoxanthine FDR = 0.00126; inosine FDR = 3.02E-13; guanosine FDR = 0.00227; uracil FDR = 0.00202; uridine FDR = 1.09E-05. d-e) Relative protein (d) and mRNA (e) expression of enzymes involved in nucleotide metabolism. Bars represent mean and standard error (N = 7 NF and N = 6 HF). RNA: PPAT FDR = 0.00425; GART FDR = 0.00967; PAICS FDR = 0.0292; ADSL FDR = 0.0204; ATIC FDR = 0.0144. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. P-values were determined by FDR-corrected two-tailed t-test.

Source data

Extended Data Fig. 4

a, b) Polar plots showing average relative abundance of tissue (a) and plasma (b) fatty acids between failing and non-failing subjects. Fold-change increases with distance from the origin, and shaded extended borders indicate standard error. c-d) Polar plots showing average relative abundance of carnitine species in tissue (c) and plasma (d) between failing and non-failing subjects. e) Volcano plots of differences in metabolite abundance in cardiac tissue after lipid extraction. f) Full western blot of ACSL1 protein (see Fig. 3 for quantification).

Source data

Extended Data Fig. 5

a) Relative mRNA expression of glucose transport genes. SLC2A1 FDR = 0.00196; SLC51A FDR = 5.88E-05. b) Western blots and quantification of various proteins from failing and non-failing tissues. Bars represent mean and standard error (N = 12 NF and N = 11 HF). ***P < 0.001, ****P < 0.0001. P-values were determined by FDR-corrected two-tailed t-test.

Source data

Extended Data Fig. 6

a, b) Relative mRNA (a) and protein (b) expression of malic enzyme isoforms. RNA: ME1 FDR = 0.000973; ME3 FDR = 0.0324. Bars represent mean and standard error (N = 7 NF and N = 6 HF). *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. P-values were determined by FDR-corrected two-tailed t-test. c) Western blots and quantification of ME1 and ME3 protein in failing and non-failing tissue. Bars represent mean and standard error (N = 12 NF and N = 11 HF). d) Volcano plot of differences in nuclear-encoded mRNAs (orange) and proteins (blue) composing the electron transport chain in cardiac tissue, comparing non-failing to failing samples. Y-axis represents FDR-corrected two-sided t-test between failing and non-failing samples. e, f) Relative expression of RNA (e) and protein (f) encoded by the mitochondrial genome. Bars represent mean and standard error (N = 7 NF and N = 6 HF). RNA: MT-ND6 FDR = 0.0296.

Source data

Extended Data Fig. 7

a) Western blots and quantification below of mTOR-related proteins from failing and non-failing tissues. Bars represent mean and standard error (N = 12 NF and N = 11 HF).

Source data

Extended Data Table 1 Patient demographics and clinical information for cohort 1 (left), cohort 2 (middle) and combined cohorts (right).
Full size table

Supplementary information

Reporting Summary.

44161_2022_117_MOESM2_ESM.xlsx

Supplementary Tables Supplementary Table 1 Tissue metabolomics data. Fold-change between failing and nonfailing tissue samples and associated two-tailed Student’s t-test p value, both nominal and FDR corrected (Bonferroni), are presented for each metabolite in cohort 1, cohort 2 and combined cohort. Fold-change is calculated as the average normalized value for the failing group divided by the average normalized value for the nonfailing group. Supplementary Table 2 Plasma metabolomics data. Fold-change between failing and nonfailing plasma samples and associated two-tailed Student’s t-test P value, both nominal and FDR corrected (Bonferroni), are presented for each metabolite in cohort 1, cohort 2 and combined cohort. Fold-change is calculated as the average normalized value for the failing group divided by the average normalized value for the nonfailing group. Supplementary Table 3 Tissue RNA-seq data. Fold-change between failing and nonfailing tissue samples and associated two-tailed Student’s t-test P value, both nominal and FDR corrected (Bonferroni), are presented for each detected gene. Fold-change is calculated as the average normalized value for the failing group divided by the average normalized value for the nonfailing group. Supplementary Table 4 Tissue proteomics data. Proteomics data (LFQ, label-free quantification; representative of reads) for each sample (ctr, nonfailing, DCM, failing) normalized to nonfailing average. Supplementary Table 5 CoA species. Fold-change between failing and nonfailing tissue samples and associated two-tailed Student’s t-test P value are presented for each detected metabolite. Fold-change is calculated as the average normalized value for the failing group divided by the average normalized value for the nonfailing group. Supplementary Table 6 CoA species. The log(fold-change) between failing and nonfailing tissue samples and associated log of the two-tailed Student’s t-test P value are presented for each detected metabolite. Fold-change is calculated as the average normalized value for the failing group divided by the average normalized value for the nonfailing group.

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Unprocessed western blots.

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Flam, E., Jang, C., Murashige, D. et al. Integrated landscape of cardiac metabolism in end-stage human nonischemic dilated cardiomyopathy. Nat Cardiovasc Res 1, 817–829 (2022). https://doi.org/10.1038/s44161-022-00117-6

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