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Tachycardia-induced metabolic rewiring as a driver of contractile dysfunction

Nature Biomedical Engineering (2023)Cite this article

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Abstract

Prolonged tachycardia—a risk factor for cardiovascular morbidity and mortality—can induce cardiomyopathy in the absence of structural disease in the heart. Here, by leveraging human patient data, a canine model of tachycardia and engineered heart tissue generated from human induced pluripotent stem cells, we show that metabolic rewiring during tachycardia drives contractile dysfunction by promoting tissue hypoxia, elevated glucose utilization and the suppression of oxidative phosphorylation. Mechanistically, a metabolic shift towards anaerobic glycolysis disrupts the redox balance of nicotinamide adenine dinucleotide (NAD), resulting in increased global protein acetylation (and in particular the acetylation of sarcoplasmic/endoplasmic reticulum Ca2+-ATPase), a molecular signature of heart failure. Restoration of NAD redox by NAD+ supplementation reduced sarcoplasmic/endoplasmic reticulum Ca2+-ATPase acetylation and accelerated the functional recovery of the engineered heart tissue after tachycardia. Understanding how metabolic rewiring drives tachycardia-induced cardiomyopathy opens up opportunities for therapeutic intervention.

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Fig. 1: Tachycardia is associated with the downregulation of OXPHOS, TCA cycle and fatty acid oxidation in patients with HF.
Fig. 2: Tachypacing downregulates OXPHOS, TCA cycle and fatty acid oxidation in dogs.
Fig. 3: Validation of the EHT tachypacing setup.
Fig. 4: Tachypacing induces reversible contractile dysfunction in human EHTs.
Fig. 5: PKA signalling is dysregulated by tachypacing in a biphasic manner.
Fig. 6: Tachycardia promotes tissue hypoxia.
Fig. 7: Increased glucose utilization in tachypaced EHTs.
Fig. 8: Disrupted NAD homeostasis and SERCA2a acetylation underlie tachycardia-induced cardiac dysfunction.

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Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. Source data for the figures are available in figshare, with the identifier https://doi.org/10.6084/m9.figshare.24112587. RNA-seq data are available at the National Center for Biotechnology Information Gene Expression Omnibus repository, under accession number GSE242727. Publicly available data used in this study are available at the National Center for Biotechnology Information Gene Expression Omnibus repository, under accession numbers GSE116250 and GSE9794. The raw and analysed datasets generated during the study are available for research purposes from the corresponding authors on reasonable request. Source data are provided with this paper.

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Acknowledgements

C.T. discloses support for the research described in this study from the American Heart Association (AHA) (20POST35080175) and the National Institutes of Health (NIH) (K99 HL164962). A.C. discloses support for the publication of this study from AHA (908136). H.Z. discloses support for the publication of this study from AHA (23CDA1050577). O.J.A. discloses support for the publication of this study from the NIH (K01 HL130608). F.A.R. discloses support for the publication of this study from NIH (R01 HL151345). J.C.W. discloses support for the publication of this study from the NIH (R01 HL163680, R01 HL141371, R01 HL113006, R01 HL150693 and P01 HL141084) and the National Aeronautics and Space Administration (80ARC022CA003).

Author information

Authors and Affiliations

  1. Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA

    Chengyi Tu, Arianne Caudal, Yu Liu, Hao Zhang, Chi Keung Lam, Angela Zhang, Alexa Wnorowski, Matthew A. Wu, Huaxiao Yang, Oscar J. Abilez & Joseph C. Wu

  2. Aging + Cardiovascular Discovery Center, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA

    Nikoloz Gorgodze & Fabio A. Recchia

  3. Sarafan ChEM-H, Stanford University, Stanford, CA, USA

    Yuqin Dai

  4. Greenstone Biosciences, Palo Alto, CA, USA

    Angela Zhang & Matthew A. Wu

  5. Department of Pathology, Stanford University, Stanford, CA, USA

    Xuchao Lyu

  6. Department of Medicine, Stanford University, Stanford, CA, USA

    Sanjiv M. Narayan & Joseph C. Wu

  7. Human Medical Genetics and Genomics, University of Colorado, Aurora, CO, USA

    Luisa Mestroni & Matthew R. G. Taylor

  8. Cardiovascular Institute and Adult Medical Genetics Program, University of Colorado, Aurora, CO, USA

    Luisa Mestroni & Matthew R. G. Taylor

  9. Scuola Superiore Sant’Anna, Pisa, Italy

    Fabio A. Recchia

  10. Institute of Clinical Physiology of the National Research Council, Pisa, Italy

    Fabio A. Recchia

  11. Department of Radiology, Stanford University, Stanford, CA, USA

    Joseph C. Wu

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Contributions

C.T. and J.C.W. initiated and oversaw the entire study. C.T. and A.C. performed and analysed the experiments. Y.L. performed the transcriptomic analysis. C.T. and Y.D. designed and performed the metabolomic experiments. A.W., O.J.A., L.M., M.R.G.T., H.Y., A.Z., M.A.W. and X.L. performed the data analysis. N.G. and F.A.R. provided critical experimental materials. C.K.L., H.Z., S.M.N., N.G. and F.A.R. provided critical input on the experimental design. C.T., A.C., C.K.L., S.M.N. and J.C.W. wrote and edited the manuscript.

Corresponding author

Correspondence to Joseph C. Wu.

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Competing interests

J.C.W. is a co-founder and scientific advisory board member of Greenstone Biosciences. All other authors declare no competing interests.

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Nature Biomedical Engineering thanks Thomas Eschenhagen, Zachary Laksman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Clinical characteristics of the patient cohorts.

a, Gender, age, and common co-morbidities of the patients. n = 14 patients without heart failure, 16 patients with heart failure, and 19 patients with heart failure and tachycardia. The number of patients with or without a co-morbidity is indicated by red or blue boxes respectively. b, Summary of medical therapies used on the patients. The number of patients using or not using a therapy is indicated by red or blue boxes respectively.

Extended Data Fig. 2 Improved iPSC-CM maturation by hormone and fatty acid supplementation.

Treatment of human iPSC-CMs (SCVI-273) with a maturation media containing triiodothyronine (10 nM), dexamethasone (1 μM), oleic acid (30 μM), and palmitic acid (80 μM) for 6 days promoted the maturation of iPSC-CMs. a, qPCR analysis of maturation markers in iPSC-CMs with or without the maturation treatment. n = 3 technical replicates. Unpaired Student’s t-test. b, Western blot analysis of OXPHOS proteins in EHTs with or without the maturation treatment, including NDUFB8, SDHB, ubiquinol-cytochrome c reductase core protein 2 (UQCRC2), MTCO2, and ATP5A. n = 4 EHTs per group. Unpaired Student’s t-test. c, Measurement of oxygen consumption rate (OCR) of iPSC-CMs with or without the maturation treatment. n = 17 wells. 10,000 cells/well. Two-tailed Mann–Whitney test. d, Analysis of calcium handling using Fluo-4 dye in iPSC-CMs with or without the maturation treatment. n = 31 immature cells and n = 30 mature cells. Two-tailed Mann–Whitney test. e, Contractility analysis of EHTs with or without the maturation treatment. n = 8 EHTs per group. Two-tailed Mann–Whitney test. For a, b, and e, data were normalized against the untreated cells or EHTs. Data are displayed as mean ± s.e.m.

Source data

Extended Data Fig. 3 Pacing at a physiological rate does not induce contractile dysfunction in EHTs.

EHTs (SCVI-273) were tachypaced or unpaced for 10 days and contractility measurements were performed on days 0, 2, 7, and 10. Functional parameters generated from the analysis include maximum contraction velocity (a), maximum relaxation velocity (b), spontaneous beating rate (c), and contractile force (d). n = 16 for unpaced EHTs and n = 14 for paced EHTs. Two-way ANOVA with Bonferroni’s multiple comparisons test. Data are displayed as mean ± s.e.m.

Extended Data Fig. 4 Pharmacological suppression of the beating rate increase blunts the deterioration of EHT contractility.

a, Experimental outline: EHTs (SCVI-273) were tachypaced for 5 days with or without drug treatment. Contractility was measured before and after tachypacing. b, Effect of carvedilol (250 ng/mL) on the beating rate (unpaced or paced) and the contractile force of tachypaced EHTs. n = 4 EHTs per group. c, Effect of FK506 (5 μM) on the beating rate (unpaced or paced) and the contractile force of tachypaced EHTs. n = 8 EHTs for the beating rate measurements and n = 5 EHTs for contractile force measurements. d, Effect of ivabradine (5 μM) on the beating rate (unpaced or paced) and the contractile force of tachypaced EHTs. n = 4 untreated EHTs and n = 8 ivabradine-treated EHTs. Unpaired Student’s t-test. Data are displayed as mean ± s.e.m.

Extended Data Fig. 5 Hypertrophic cardiomyopathy (HCM) EHTs have increased sensitivity to tachypacing-induced contractile dysfunction.

a, Experimental outline: HCM (MYBPC3 mutation) EHTs (SCVI-591) were tachypaced at 3 Hz for 5 days, then allowed to recover for 5 days. Contractile force (b), maximum contraction velocity (c), maximum relaxation velocity (d), and beating rate (e) were measured before tachypacing, 5 days after tachypacing, 1 day, 2 days, and 5 days after recovery. Data points of beating rates for EHTs that stopped beating were not shown. Data were normalized against the baseline values from day 0. n = 11 EHTs. One-way paired ANOVA with Tukey’s multiple comparison test. Only comparisons with day 0 are shown for statistical significance. *: P < 0.05; **: P < 0.01; ***: P < 0.001. Data are displayed as repeated measures of each EHT’s contractility over time.

Extended Data Fig. 6 Functional changes in the canine model of tachypacing-induced HF.

Functional parameters including: ejection fraction, heart rate, dp/dt max, systolic pressure, end-diastolic pressure and end-diastolic diameter were measured in healthy (NF) and tachypaced dogs (HF). n = 3 NF dogs and n = 4 HF dogs. Unpaired Student’s t-test. Data are displayed as mean ± s.e.m.

Extended Data Fig. 7 Reversible activation of glycolysis and hypoxia genes by tachypacing.

Tachypaced EHTs, unpaced control EHTs, and EHTs recovered from tachypacing were subjected to qPCR analysis for DEGs identified through RNA-Seq. Expression levels were normalized against the control EHTs. n = 3 technical replicates. One-way ANOVA with Bonferroni’s multiple comparisons test. EHTs were generated from line SCVI-273. Data are displayed as mean ± s.e.m.

Extended Data Fig. 8 Hypoxia downregulates oxidative phosphorylation genes.

iPSC-CMs (SCVI-273) were exposed to hypoxia (<1% O2) for 24 hours. Expression of complex I genes was quantified with qPCR analysis, including NFUFAB1, NDUFAF1, NDUFA2, NDUFA3, NDUFB9, NDUFV1, NDUFS5 and NDUFB11. Expression levels were normalized against hypoxia-treated cells. n = 6 wells of cells. Unpaired Student’s t-test. Data are displayed as mean ± s.e.m.

Extended Data Fig. 9 Effect of NAD+ supplementation on glucose metabolites.

Metabolomic analysis of EHTs (SCVI-273) treated with 1 mM NAD+ or vehicle (water) for 1 day after tachypacing. a, Quantification of glycolysis metabolites: glucose-6-phosphate, pyruvate, fructose-1-phosphate and dihydroxyacetone phosphate. b, Quantification of HBP pathway metabolites: glucosamine-6-phosphate and UDP‐GlcNAc. c, Quantification of PPP pathway metabolite: ribulose-5-phosphate. d, Quantification of TCA cycle metabolites: malic acid, aconitic acid, succinic acid, fumaric acid and citric acid. n = 7 untreated EHTs and n = 8 NAD+-treated EHTs. Two-tailed Mann–Whitney test. Data are displayed as mean ± s.e.m.

Extended Data Fig. 10 Upregulation of ECM remodelling genes in tachypaced EHTs after recovery.

The transcriptome of EHTs recovered from tachypacing was compared with the transcriptome of unpaced control EHTs (SCVI-273). a, Heatmap of the expression of DEGs. Gene ontology analysis for b) biological process, c) cellular component, and d) molecular function of the DEGs.

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Tu, C., Caudal, A., Liu, Y. et al. Tachycardia-induced metabolic rewiring as a driver of contractile dysfunction. Nat. Biomed. Eng (2023). https://doi.org/10.1038/s41551-023-01134-x

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