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N-Cadherin promotes cardiac regeneration by potentiating pro-mitotic β-Catenin signaling in cardiomyocytes

Author

Listed:
  • Yi-Wei Tsai

    (National Taiwan University College of Medicine
    National Taiwan University Hospital)

  • Yi-Shuan Tseng

    (National Taiwan University College of Medicine)

  • Yu-Shuo Wu

    (National Taiwan University College of Medicine)

  • Wei-Lun Song

    (National Taiwan University College of Medicine)

  • Min-Yi You

    (National Taiwan University College of Medicine)

  • Yun-Chia Hsu

    (National Taiwan University College of Medicine)

  • Wen-Pin Chen

    (National Taiwan University College of Medicine)

  • Wei-Han Huang

    (National Taiwan University College of Medicine)

  • Jia-Ci Chng

    (Academia Sinica
    National Yang Ming Chiao Tung University)

  • Chai-Ling Lim

    (Academia Sinica)

  • Ke-Hsuan Wei

    (National Yang Ming Chiao Tung University
    National Defense Medical Center)

  • Shih-Lei Lai

    (Academia Sinica
    National Yang Ming Chiao Tung University
    National Defense Medical Center)

  • Wen-Chih Lee

    (National Taiwan University College of Medicine
    Academia Sinica)

  • Kai-Chien Yang

    (National Taiwan University College of Medicine
    Academia Sinica
    National Yang Ming Chiao Tung University
    National Taiwan University)

Abstract

Adult human hearts exhibit limited regenerative capacity. Post-injury cardiomyocyte (CM) loss can lead to myocardial dysfunction and failure. Although neonatal mammalian hearts can regenerate, the underlying molecular mechanisms remain elusive. Herein, comparative transcriptome analyses identify adherens junction protein N-Cadherin as a crucial regulator of CM proliferation/renewal. Its expression correlates positively with mitotic genes and shows an age-dependent reduction. N-Cadherin is upregulated in the neonatal mouse heart following injury, coinciding with increased CM mitotic activities. N-Cadherin knockdown reduces, whereas overexpression increases, the proliferation activity of neonatal mouse CMs and human induced pluripotent stem cell-derived CMs. Mechanistically, N-Cadherin binds and stabilizes pro-mitotic transcription regulator β-Catenin, driving CM self-renewal. Targeted N-Cadherin deletion in CMs impedes cardiac regeneration in neonatal mice, leading to excessive scarring. N-Cadherin overexpression, by contrast, promotes regeneration in adult mouse hearts following ischemic injury. N-Cadherin targeting presents a promising avenue for promoting cardiac regeneration and restoring function in injured adult human hearts.

Suggested Citation

  • Yi-Wei Tsai & Yi-Shuan Tseng & Yu-Shuo Wu & Wei-Lun Song & Min-Yi You & Yun-Chia Hsu & Wen-Pin Chen & Wei-Han Huang & Jia-Ci Chng & Chai-Ling Lim & Ke-Hsuan Wei & Shih-Lei Lai & Wen-Chih Lee & Kai-Chi, 2025. "N-Cadherin promotes cardiac regeneration by potentiating pro-mitotic β-Catenin signaling in cardiomyocytes," Nature Communications, Nature, vol. 16(1), pages 1-19, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-56216-y
    DOI: 10.1038/s41467-025-56216-y
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    References listed on IDEAS

    as
    1. Zhang Bin & Horvath Steve, 2005. "A General Framework for Weighted Gene Co-Expression Network Analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 4(1), pages 1-45, August.
    2. Yuka Morikawa & Todd Heallen & John Leach & Yang Xiao & James F. Martin, 2017. "Dystrophin–glycoprotein complex sequesters Yap to inhibit cardiomyocyte proliferation," Nature, Nature, vol. 547(7662), pages 227-231, July.
    3. Yuji Nakada & Diana C. Canseco & SuWannee Thet & Salim Abdisalaam & Aroumougame Asaithamby & Celio X. Santos & Ajay M. Shah & Hua Zhang & James E. Faber & Michael T. Kinter & Luke I. Szweda & Chao Xin, 2017. "Hypoxia induces heart regeneration in adult mice," Nature, Nature, vol. 541(7636), pages 222-227, January.
    4. Elad Bassat & Yara Eid Mutlak & Alex Genzelinakh & Ilya Y. Shadrin & Kfir Baruch Umansky & Oren Yifa & David Kain & Dana Rajchman & John Leach & Daria Riabov Bassat & Yael Udi & Rachel Sarig & Irit Sa, 2017. "The extracellular matrix protein agrin promotes heart regeneration in mice," Nature, Nature, vol. 547(7662), pages 179-184, July.
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