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Metabolic characterization of hypertrophic cardiomyopathy in human heart

Nature Cardiovascular Research volume 1, pages 445–461 (2022)Cite this article

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Abstract

Hypertrophic cardiomyopathy (HCM) is a common inherited cardiovascular disease with heterogeneous clinical presentations, governed by multiple molecular mechanisms. Metabolic perturbations underlie most cardiovascular diseases; however, the metabolic alterations and their function in HCM are unknown. Here, we describe the metabolome and lipidome of heart and plasma samples from individuals with and without HCM. Correlation analyses showed strong association between metabolic alterations and cardiac function and prognosis of patients with HCM. Using machine learning we identified metabolite panels as potential HCM diagnostic markers or predictors of survival. Clustering based on metabolome and lipidome of heart enabled stratification of patients with HCM into three subgroups with distinct cardiac function and survival. Integration of metabolomics and proteomics data identified metabolic pathways significantly altered in patients with HCM, with the pentose phosphate pathway and oxidative stress being particularly upregulated. Thus, targeting the pentose phosphate pathway and oxidative stress may serve as potential therapeutic strategies for HCM.

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Fig. 1: Genomic and metabolic landscape of the HCM cohort.
Fig. 2: Metabolomics perturbations in the heart tissues from patients with HCM.
Fig. 3: Lipidomics alterations in the heart tissues from patients with HCM.
Fig. 4: Metabolic disturbances in plasma samples from patients with HCM.
Fig. 5: Metabolic association with clinical characteristics in patients with HCM.
Fig. 6: Metabolic subtyping and prognostic prediction of patients with HCM.
Fig. 7: Metabolomics and proteomics analyses reveal potential therapeutic strategies for HCM.

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Data availability

The clinical information of each HCM patient is provided in Supplementary Table 1. The baseline clinical characteristics for DCM and non-HCM controls are included in Supplementary Table 2. As public sharing of the raw genomic data is restricted by the regulation of the Human Genetic Resources Administration of China, detailed results of WES are included in Supplementary Table 3. Raw metabolomics and lipidomics data are included in Supplementary Tables 4–6. The MS proteomics raw data is deposited in the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the iProX partner repository with dataset identifier PXD032097. Source data are provided with this paper.

Code availability

Codes for data analysis are available at https://github.com/WenminWang15/HCM_Nat-Cardiovasc-Res.

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Acknowledgements

We thank the members of the Hu laboratory for critiquing the manuscript and N. Xiao, J. Li, B. Peng, S. Mao and T. Xu for their helpful discussion. This study is supported by grants from the National Natural Science Foundation of China (32150024, 92057209) (received by Z.H.), (81870286) (received by L.S.), the CAMS Fund for Young Talents of Medical Science (2018RC310006) (received by J.W.), the CAMS Innovation Fund for Medical Sciences (CAMS-2020-I2M-C&T-A-006) (received by L.S.), National Key R&D Program of China (2020YFA0803300) (received by Z.H.), and Tsinghua-Peking Center for Life Sciences, Beijing Frontier Research Center for Biological Structure (received by Z.H.).

Author information

Author notes

  1. These authors contributed equally: Wenmin Wang, Jizheng Wang, Ke Yao, Shuiyun Wang.

Authors and Affiliations

  1. School of Pharmaceutical Sciences, Tsinghua-Peking Center for Life Sciences, Beijing Frontier Research Center for Biological Structure, Tsinghua University, Beijing, China

    Wenmin Wang, Ke Yao, Meng Nie, Yizi Zhao, Bohong Wang, Huanhuan Pang & Zeping Hu

  2. Shanghai Qi Zhi Institute, Shanghai, China

    Wenmin Wang, Ke Yao, Yizi Zhao, Tianshu Yang & Peng Li

  3. State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Jizheng Wang, Guixin Wu, Minjie Lu & Lei Song

  4. Department of Cardiac Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Shuiyun Wang

  5. Waters Corporation (China), Beijing, China

    Jingjing Xu

  6. Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Guixin Wu & Lei Song

  7. The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, China

    Nan Tang & Chunmei Qi

  8. School of Computer Science, University of Illinois Urbana Champaign, Champaign, IL, USA

    Hengzhi Pei

  9. Microsoft Research Asia, Shanghai, China

    Xufang Luo & Dongsheng Li

  10. Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, China

    Tianshu Yang & Peng Li

  11. Institute of Biophysics, Chinese Academy of Science, Beijing, China

    Qing Sun & Yan Li

  12. Division of Cardiothoracic and Vascular Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Xiang Wei & Dingsheng Jiang

  13. Key Laboratory of Organ Transplantation, Ministry of Education, Chinese Academy of Medical Sciences, Wuhan, China

    Xiang Wei & Dingsheng Jiang

  14. School of Life Sciences, Tsinghua University, Beijing, China

    Peng Li

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  1. Wenmin Wang
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  2. Jizheng Wang
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Contributions

W.W. and Z.H. designed the study and wrote the manuscript. W.W. performed data analyses, data integration and western blot experiments. K.Y. and W.W. performed targeted metabolomics experiments and data processing. K.Y. and J.X. performed lipidomics experiments and data processing. J.W., G.W. and M.L. performed WES and proteomics experiments. M.N., Y.Z., B.W., H.P. and P.L. assisted in data interpretation and manuscript editing. N.T., C.Q., Y.L., Q.S., X.W., D.J., J.W., G.W., S.W. and L.S. provided clinical samples and information. Y.Z., H.P., X,L., D.L. and T.Y. performed machine learning. Z.H. conceived and supervised the project.

Corresponding authors

Correspondence to Jizheng Wang, Lei Song or Zeping Hu.

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Nature Cardiovascular Research thanks Nicholas Larson and the other, anonymous, reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 CMR image of HCM and quality assessments for metabolomics and lipidomics data.

a, Representative cardiovascular magnetic resonance (CMR) images of non-HCM (Ctrl) and patients with HCM. The white arrow indicates the maximum thickness of cardiac interventricular septum. LA, left atrium; LV, left ventricular; RA, right atrium; RV, right ventricular. Bar: 20 mm. b, Principal component analysis (PCA) across omics assay. Each sample is colored by batch information. c–e, Coefficient of variation for metabolites detected by targeted metabolomics in cardiac tissues (n = 44) (c) and plasma (n = 22) (d) and lipidomics (for TAG, n = 30; for other lipid classes, n = 59) (e) of quality control samples. The box plots visualized as median and 25th and 75th percentiles, with whiskers indicating maximal and minimal values.

Source data

Extended Data Fig. 2 Overview of metabolomics data in the heart tissues from patients with HCM, related to Fig. 2.

a, Heatmap of metabolites relative abundance in non-HCM controls (Ctrl), HCM and DCM patients. b, PCA score plot of targeted metabolomics data on non-HCM controls, HCM and DCM patients. c, Venn diagram depicting the number of upregulated (left) and downregulated (right) metabolites in HCM and DCM group. d, Log2 fold change of differential metabolites in the HCM (x axis) or DCM (y axis) compared to non-HCM controls. e, Relative intensity of metabolites involved in TCA cycle in non-HCM controls (n = 16), HCM (n = 349) and DCM patients (n = 46). The box plots visualized as median and 25th and 75th percentiles, with whiskers indicating maximal and minimal values. f, Heatmap of spearman correlation coefficient of differential metabolites between HCM patients and non-HCM controls. Only pair metabolites with P value < 0.05 were colored and spearman correlation coefficient are labeled in the relevant box. g, Spearman correlation networks of differential metabolites with absolute correlation coefficient greater than 0.5. The solid line represents positive correlation and the dash line represents negative correlation. Statistical analyses were performed by two-sided Mann–Whitney U-test (e). (exact P values are provided in the source data). Asterisks indicate significance as follows: ns P ≥ 0.05, *P < 0.05, **P < 0.01.

Source data

Extended Data Fig. 3 Overview of lipidomics data in the heart tissues from HCM patients, related to Fig. 3.

a, PLS-DA score plot of lipidomics data on HCM patients and non-HCM controls (Ctrl). b-d, Volcano plots of sphingolipids (b), glycerophospholipids (c) and neutral lipids (d) alterations between HCM and non-HCM controls. Significantly upregulated, downregulated (FDR-corrected P value < 0.05, fold change > 1.5 or < 0.67) and unchanged metabolites were colored in red, blue, and gray, respectively. Top 10 most significantly increased or decreased metabolites of fold change in each group are labeled. The horizontal line denotes a FDR cutoff of 0.05, and the vertical lines denote a fold change of 1.5 or 0.67. e, Relative intensity of PG, PI and PS in HCM (n = 349) and non-HCM controls (n = 16). The box plots visualized as median and 25th and 75th percentiles, with whiskers indicating maximal and minimal values. f, Fold change of TAG with different numbers of carbon between HCM and non-HCM controls. Statistical analyses were performed by two-sided Mann–Whitney U-test (e) and followed by Benjamini–Hochberg correction (b-d).

Source data

Extended Data Fig. 4 Metabolic alterations in the plasma of HCM patients, related to Fig. 4.

a, Heatmap of metabolites relative abundance in the plasma of non-HCM controls (Ctrl) and HCM patients. b, PCA score plot of metabolomics data on the plasma of HCM patients and non-HCM controls. c, Venn diagram depicting the number of plasma and tissue differential metabolites between HCM and non-HCM controls group.

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Extended Data Fig. 5 Metabolic subtyping of HCM and their associations with clinical characteristics, related to Fig. 6.

a, b, Groups were identified based on metabolomics data of HCM cohort by K-means consensus clustering upon their abundance. Consensus results show consistency for k = 2 (a) and k = 4 (b), the abundance of TAGs was defined as the ratio of each TAG to total TAG abundance in each patient. c, Cumulative distribution function (CDF) plot of consensus clustering for HCM patients metabolomics data. d, Delta area (change in CDF area) plot of consensus clustering for HCM patients metabolomics data. e, f, The distribution of HCM patients with different NYHA class (e) and MWT (f) in each individual metabolic subtype. g, h, Kaplan–Meier curves for overall survival of HCM patients stratified by the relative intensity of isocitrate, fumarate (g), Uracil, GMP, dUMP and R5P (h) (two-sided log-rank test with no multiple comparisons adjustment). The median of metabolite abundances as the cutoff for expression dichotomization. i, j, Subgroups were identified based on lipidomics data of HCM cohort by K-means consensus clustering upon their abundance. Consensus results show consistency for k = 2 (i) and k = 4 (j). k, CDF plot of consensus clustering for HCM patients lipidomics data. l, Delta area (change in CDF area) plot of consensus clustering for HCM patients lipidomics data. m, n, The distribution of clinical parameters: NYHA class (m) and MWT (n) in each individual metabolic cluster. o, Bubble plot of significance between TAGs with different numbers of carbon and overall survival. Each dot represents a lipid species. Color coded per TAG with different numbers of carbon. Dot size indicates significance. The abundance of TAGs was defined as the ratio of each TAG to total TAG abundance in each patient. A two-sided log-rank test was used with no multiple comparisons adjustment. p, Kaplan–Meier curves for overall survival of HCM patients stratified by the relative intensity of HexCer d18:1/22:2 (two-sided log-rank test). The median of metabolite abundances as the cutoff for expression dichotomization.

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Supplementary information

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Description of Additional supplementary files, Supplementary Fig. 1

Reporting Summary

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Wang, W., Wang, J., Yao, K. et al. Metabolic characterization of hypertrophic cardiomyopathy in human heart. Nat Cardiovasc Res 1, 445–461 (2022). https://doi.org/10.1038/s44161-022-00057-1

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