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APOE–NOTCH axis governs elastogenesis during human cardiac valve remodeling

Nature Cardiovascular Research volume 3, pages 933–950 (2024)Cite this article

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Abstract

Valve remodeling is a process involving extracellular matrix organization and elongation of valve leaflets. Here, through single-cell RNA sequencing of human fetal valves, we identified an elastin-producing valve interstitial cell (VIC) subtype (apolipoprotein E (APOE)+, elastin-VICs) spatially located underneath valve endothelial cells (VECs) sensing unidirectional flow. APOE knockdown in fetal VICs resulted in profound elastogenesis defects. In valves with pulmonary stenosis (PS), we observed elastin fragmentation and decreased expression of APOE along with other genes regulating elastogenesis. Cell–cell interaction analysis revealed that jagged 1 (JAG1) from unidirectional VECs activates elastogenesis in elastin-VICs through NOTCH2. Similar observations were made in VICs cocultured with VECs under unidirectional flow. Notably, a drastic reduction of JAG1–NOTCH2 was also observed in PS valves. Lastly, we found that APOE controls JAG1-induced NOTCH activation and elastogenesis in VICs through the extracellular signal-regulated kinase pathway. Our study suggests important roles of both APOE and NOTCH in regulating elastogenesis during human valve remodeling.

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Fig. 1: scRNA-seq identifies elastin-VICs in human fetal heart valves.
Fig. 2: APOE is essential for elastogenesis in elastin-VICs.
Fig. 3: APOE deficiency and elastin fragmentation in PVs with PS.
Fig. 4: JAG1 derived from UDVECs regulates NOTCH signaling in elastin-VICs through NOTCH2.
Fig. 5: VEC–VIC interaction under unidirectional flow activates APOE-mediated elastogenesis in VICs.
Fig. 6: Impairment of NOTCH signaling in PVs with PS.
Fig. 7: APOE regulates NOTCH activity and elastogenesis through the MAPK signaling pathway.

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Data availability

The raw and processed data from single-cell RNA sequencing in this study have been deposited to the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE228638. The datasets are open to the public for research purposes without restriction. GSE117011 (Hulin et al.21, PMID: 30796046) and GSE106118 (Cui et al.19, PMID: 30759401) were also used as published source data. Source data are provided with this paper.

Code availability

Code for scRNA-seq CCA integration and cell–cell interaction can be accessed via GitHub at https://github.com/ly19901105/-APOE-NOTCH-Axis-Governs-Elastogenesis-During-Human-Cardiac-Valve-Remodeling.git.

References

  1. Aikawa, E. et al. Human semilunar cardiac valve remodeling by activated cells from fetus to adult: implications for postnatal adaptation, pathology, and tissue engineering. Circulation 113, 1344–1352 (2006).

    Article  PubMed  Google Scholar 

  2. Hinton, R. B. Jr. et al. Extracellular matrix remodeling and organization in developing and diseased aortic valves. Circ. Res. 98, 1431–1438 (2006).

    Article  CAS  PubMed  Google Scholar 

  3. Votteler, M. et al. Elastogenesis at the onset of human cardiac valve development. Development 140, 2345–2353 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Lindsey, S. E., Butcher, J. T. & Yalcin, H. C. Mechanical regulation of cardiac development. Front. Physiol. 5, 318 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Stuart, A. G. & Williams, A. Marfan’s syndrome and the heart. Arch. Dis. Child. 92, 351–356 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Waller, B. F., Howard, J. & Fess, S. Pathology of pulmonic valve stenosis and pure regurgitation. Clin. Cardiol. 18, 45–50 (1995).

    Article  CAS  PubMed  Google Scholar 

  7. Liu, A. C., Joag, V. R. & Gotlieb, A. I. The emerging role of valve interstitial cell phenotypes in regulating heart valve pathobiology. Am. J. Pathol. 171, 1407–1418 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Zhang, H. et al. Structure and expression of fibrillin-2, a novel microfibrillar component preferentially located in elastic matrices. J. Cell Biol. 124, 855–863 (1994).

    Article  CAS  PubMed  Google Scholar 

  9. Sakai, L. Y., Keene, D. R. & Engvall, E. Fibrillin, a new 350-kD glycoprotein, is a component of extracellular microfibrils. J. Cell Biol. 103, 2499–2509 (1986).

    Article  CAS  PubMed  Google Scholar 

  10. Bressan, G. M. et al. Emilin, a component of elastic fibers preferentially located at the elastin–microfibrils interface. J. Cell Biol. 121, 201–212 (1993).

    Article  CAS  PubMed  Google Scholar 

  11. Kielty, C. M., Sherratt, M. J., Marson, A. & Baldock, C. Fibrillin microfibrils. Adv. Protein Chem. 70, 405–436 (2005).

    Article  CAS  PubMed  Google Scholar 

  12. Horiguchi, M. et al. Fibulin-4 conducts proper elastogenesis via interaction with cross-linking enzyme lysyl oxidase. Proc. Natl Acad. Sci. USA 106, 19029–19034 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Sterner-Kock, A. et al. Disruption of the gene encoding the latent transforming growth factor-β binding protein 4 (LTBP-4) causes abnormal lung development, cardiomyopathy, and colorectal cancer. Genes Dev. 16, 2264–2273 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Goddard, L. M. et al. Hemodynamic forces sculpt developing heart valves through a KLF2–WNT9B paracrine signaling axis. Dev. Cell 43, 274–289 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Hogers, B., DeRuiter, M. C., Gittenberger-de Groot, A. C. & Poelmann, R. E. Extraembryonic venous obstructions lead to cardiovascular malformations and can be embryolethal. Cardiovasc. Res. 41, 87–99 (1999).

    Article  CAS  PubMed  Google Scholar 

  16. Hove, J. R. et al. Intracardiac fluid forces are an essential epigenetic factor for embryonic cardiogenesis. Nature 421, 172–177 (2003).

    Article  CAS  PubMed  Google Scholar 

  17. Vermot, J. et al. Reversing blood flows act through klf2a to ensure normal valvulogenesis in the developing heart. PLoS Biol. 7, e1000246 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Yalcin, H. C. et al. Two-photon microscopy-guided femtosecond-laser photoablation of avian cardiogenesis: noninvasive creation of localized heart defects. Am. J. Physiol. Heart Circ. Physiol. 299, H1728–H1735 (2010).

    Article  CAS  PubMed  Google Scholar 

  19. Cui, Y. et al. Single-cell transcriptome analysis maps the developmental track of the human heart. Cell Rep. 26, 1934–1950 (2019).

    Article  CAS  PubMed  Google Scholar 

  20. Leshem, R. S. et al. A cell atlas of the human outflow tract of the heart and its adult derivatives. Preprint at bioRxiv https://doi.org/10.1101/2023.04.05.535627 (2023).

  21. Hulin, A. et al. Maturation of heart valve cell populations during postnatal remodeling. Development 146, dev173047 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Huang, Y. & Mahley, R. W. Apolipoprotein E: structure and function in lipid metabolism, neurobiology, and Alzheimer’s diseases. Neurobiol. Dis. 72, 3–12 (2014).

    Article  CAS  PubMed  Google Scholar 

  23. Murakami, M., Sato, H., Miki, Y., Yamamoto, K. & Taketomi, Y. A new era of secreted phospholipase A2. J. Lipid Res. 56, 1248–1261 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Grande-Allen, K. J., Griffin, B. P., Ratliff, N. B., Cosgrove, D. M. & Vesely, I. Glycosaminoglycan profiles of myxomatous mitral leaflets and chordae parallel the severity of mechanical alterations. J. Am. Coll. Cardiol. 42, 271–277 (2003).

    Article  CAS  PubMed  Google Scholar 

  25. Kim, A. J. et al. Deficiency of circulating monocytes ameliorates the progression of myxomatous valve degeneration in Marfan syndrome. Circulation 141, 132–146 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Saef, J. M. & Ghobrial, J. Valvular heart disease in congenital heart disease: a narrative review. Cardiovasc. Diagn. Ther. 11, 818–839 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Escalon, J. G. et al. Congenital anomalies of the pulmonary arteries: an imaging overview. Br. J. Radiol. 92, 20180185 (2019).

    Article  PubMed  Google Scholar 

  28. Allen, W. M., Matloff, J. M. & Fishbein, M. C. Myxoid degeneration of the aortic valve and isolated severe aortic regurgitation. Am. J. Cardiol. 55, 439–444 (1985).

    Article  CAS  PubMed  Google Scholar 

  29. Aikawa, E. et al. Multimodality molecular imaging identifies proteolytic and osteogenic activities in early aortic valve disease. Circulation 115, 377–386 (2007).

    Article  CAS  PubMed  Google Scholar 

  30. Fondard, O. et al. Extracellular matrix remodelling in human aortic valve disease: the role of matrix metalloproteinases and their tissue inhibitors. Eur. Heart J. 26, 1333–1341 (2005).

    Article  CAS  PubMed  Google Scholar 

  31. Segura, A. M. et al. Immunohistochemistry of matrix metalloproteinases and their inhibitors in thoracic aortic aneurysms and aortic valves of patients with Marfan’s syndrome. Circulation 98, II331–II337 (1998).

    CAS  PubMed  Google Scholar 

  32. Wang, M., Kim, S. H., Monticone, R. E. & Lakatta, E. G. Matrix metalloproteinases promote arterial remodeling in aging, hypertension, and atherosclerosis. Hypertension 65, 698–703 (2015).

    Article  CAS  PubMed  Google Scholar 

  33. Wang, K., Meng, X. & Guo, Z. Elastin structure, synthesis, regulatory mechanism and relationship with cardiovascular diseases. Front. Cell Dev. Biol. 9, 596702 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Yoganathan, A. P., He, Z. & Casey Jones, S. Fluid mechanics of heart valves. Annu. Rev. Biomed. Eng. 6, 331–362 (2004).

    Article  CAS  PubMed  Google Scholar 

  35. Parmar, K. M. et al. Integration of flow-dependent endothelial phenotypes by Kruppel-like factor 2. J. Clin. Invest. 116, 49–58 (2006).

    Article  CAS  PubMed  Google Scholar 

  36. Bowers, S. L. K., Banerjee, I. & Baudino, T. A. The extracellular matrix: at the center of it all. J. Mol. Cell. Cardiol. 48, 474–482 (2010).

    Article  CAS  PubMed  Google Scholar 

  37. Wang, Y. et al. Endocardial to myocardial Notch–Wnt–Bmp axis regulates early heart valve development. PLoS ONE 8, e60244 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Hofmann, J. J. et al. Endothelial deletion of murine Jag1 leads to valve calcification and congenital heart defects associated with Alagille syndrome. Development 139, 4449–4460 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. MacGrogan, D. et al. Sequential ligand-dependent Notch signaling activation regulates valve primordium formation and morphogenesis. Circ. Res. 118, 1480–1497 (2016).

    Article  CAS  PubMed  Google Scholar 

  40. High, F. A. et al. Murine Jagged1/Notch signaling in the second heart field orchestrates Fgf8 expression and tissue–tissue interactions during outflow tract development. J. Clin. Invest. 119, 1986–1996 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Donovan, J., Kordylewska, A., Jan, Y. N. & Utset, M. F. Tetralogy of Fallot and other congenital heart defects in Hey2 mutant mice. Curr. Biol. 12, 1605–1610 (2002).

    Article  CAS  PubMed  Google Scholar 

  42. Zohorsky, K., Lin, S. & Mequanint, K. Immobilization of Jagged1 enhances vascular smooth muscle cells maturation by activating the Notch pathway. Cells 10, 2089 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Dong, Y. et al. NOTCH-mediated maintenance and expansion of human bone marrow stromal/stem cells: a technology designed for orthopedic regenerative medicine. Stem Cells Transl. Med. 3, 1456–1466 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Davies, P. F., Remuzzi, A., Gordon, E. J., Dewey, C. F. Jr. & Gimbrone, M. A. Jr. Turbulent fluid shear stress induces vascular endothelial cell turnover in vitro. Proc. Natl Acad. Sci. USA 83, 2114–2117 (1986).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Masumura, T., Yamamoto, K., Shimizu, N., Obi, S. & Ando, J. Shear stress increases expression of the arterial endothelial marker ephrinB2 in murine ES cells via the VEGF–Notch signaling pathways. Arterioscler. Thromb. Vasc. Biol. 29, 2125–2131 (2009).

    Article  CAS  PubMed  Google Scholar 

  46. Driessen, R. C. H. et al. Shear stress induces expression, intracellular reorganization and enhanced Notch activation potential of Jagged1. Integr. Biol. (Camb.) 10, 719–726 (2018).

    Article  CAS  PubMed  Google Scholar 

  47. Karimi, A. & Milewicz, D. M. Structure of the elastin-contractile units in the thoracic aorta and how genes that cause thoracic aortic aneurysms and dissections disrupt this structure. Can. J. Cardiol. 32, 26–34 (2016).

    Article  PubMed  Google Scholar 

  48. Soler-López, M., Zanzoni, A., Lluís, R., Stelzl, U. & Aloy, P. Interactome mapping suggests new mechanistic details underlying Alzheimer’s disease. Genome Res. 21, 364–376 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Fukushima, H. et al. NOTCH2 Hajdu–Cheney mutations escape SCFFBW7-dependent proteolysis to promote osteoporosis. Mol. Cell 68, 645–658 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Huang, Y. A., Zhou, B., Nabet, A. M., Wernig, M. & Sudhof, T. C. Differential signaling mediated by ApoE2, ApoE3, and ApoE4 in human neurons parallels Alzheimer’s disease risk. J. Neurosci. 39, 7408–7427 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Jayakar, S. K. et al. Apolipoprotein E promotes invasion in oral squamous cell carcinoma. Am. J. Pathol. 187, 2259–2272 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Mahley, R. W. & Rall, S. C. Jr. Apolipoprotein E: far more than a lipid transport protein. Annu. Rev. Genomics Hum. Genet. 1, 507–537 (2000).

    Article  CAS  PubMed  Google Scholar 

  53. Li, L. et al. Jagged-1/Notch3 signaling transduction pathway is involved in apelin-13-induced vascular smooth muscle cells proliferation. Acta Biochim. Biophys. Sin. (Shanghai) 45, 875–881 (2013).

    Article  CAS  PubMed  Google Scholar 

  54. Wang, H. et al. Inflammatory cytokines induce NOTCH signaling in nucleus pulposus cells: implications in intervertebral disc degeneration. J. Biol. Chem. 288, 16761–16774 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Turnpenny, P. D. & Ellard, S. Alagille syndrome: pathogenesis, diagnosis and management. Eur. J. Hum. Genet. 20, 251–257 (2012).

    Article  CAS  PubMed  Google Scholar 

  56. Henneman, P. et al. The expression of type III hyperlipoproteinemia: involvement of lipolysis genes. Eur. J. Hum. Genet. 17, 620–628 (2009).

    Article  CAS  PubMed  Google Scholar 

  57. Novaro, G. M., Sachar, R., Pearce, G. L., Sprecher, D. L. & Griffin, B. P. Association between apolipoprotein E alleles and calcific valvular heart disease. Circulation 108, 1804–1808 (2003).

    Article  CAS  PubMed  Google Scholar 

  58. Tanaka, K. et al. Age-associated aortic stenosis in apolipoprotein E-deficient mice. J. Am. Coll. Cardiol. 46, 134–141 (2005).

    Article  CAS  PubMed  Google Scholar 

  59. Yamazaki, Y., Zhao, N., Caulfield, T. R., Liu, C.-C. & Bu, G. Apolipoprotein E and Alzheimer disease: pathobiology and targeting strategies. Nat. Rev. Neurol. 15, 501–518 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Hoe, H.-S., Harris, D. C. & Rebeck, G. W. Multiple pathways of apolipoprotein E signaling in primary neurons. J. Neurochem. 93, 145–155 (2005).

    Article  CAS  PubMed  Google Scholar 

  61. Theendakara, V. et al. Direct transcriptional effects of apolipoprotein E. J. Neurosci. 36, 685–700 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Islam, S. et al. Presenilin is essential for ApoE secretion, a novel role of presenilin involved in Alzheimer’s disease pathogenesis. J. Neurosci. 42, 1574–1586 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Kim, W. S. et al. Analysis of apolipoprotein E nuclear localization using green fluorescent protein and biotinylation approaches. Biochem. J. 409, 701–709 (2008).

    Article  CAS  PubMed  Google Scholar 

  64. Quinn, C. M. et al. Induction of fibroblast apolipoprotein E expression during apoptosis, starvation-induced growth arrest and mitosis. Biochem. J. 378, 753–761 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Lambert, S. A. et al. The human transcription factors. Cell 172, 650–665 (2018).

    Article  CAS  PubMed  Google Scholar 

  66. Su, Y. et al. Study of FOXO1-interacting proteins using TurboID-based proximity labeling technology. BMC Genomics 24, 146 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. He, Z. et al. The role of FOXG1 in the postnatal development and survival of mouse cochlear hair cells. Neuropharmacology 144, 43–57 (2019).

    Article  CAS  PubMed  Google Scholar 

  68. Adesina, A. M. et al. FOXG1 expression shows correlation with neuronal differentiation in cerebellar development, aggressive phenotype in medulloblastomas, and survival in a xenograft model of medulloblastoma. Hum. Pathol. 46, 1859–1871 (2015).

    Article  CAS  PubMed  Google Scholar 

  69. Jeon, J. H., Suh, H. N., Kim, M. O., Ryu, J. M. & Han, H. J. Glucosamine-induced OGT activation mediates glucose production through cleaved Notch1 and FoxO1, which coordinately contributed to the regulation of maintenance of self-renewal in mouse embryonic stem cells. Stem Cells Dev. 23, 2067–2079 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Kitamura, T. et al. A Foxo/Notch pathway controls myogenic differentiation and fiber type specification. J. Clin. Invest. 117, 2477–2485 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Sen, A., Nelson, T. J. & Alkon, D. L. ApoE4 and Aβ oligomers reduce BDNF expression via HDAC nuclear translocation. J. Neurosci. 35, 7538–7551 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Ferrante, F. et al. HDAC3 functions as a positive regulator in Notch signal transduction. Nucleic Acids Res. 48, 3496–3512 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Lee, S.-I. et al. APOE4-carrying human astrocytes oversupply cholesterol to promote neuronal lipid raft expansion and Aβ generation. Stem Cell Reports 16, 2128–2137 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Obniski, R., Sieber, M. & Spradling, A. C. Dietary lipids modulate Notch signaling and influence adult intestinal development and metabolism in Drosophila. Dev. Cell 47, 98–111 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Bu, G. Apolipoprotein E and its receptors in Alzheimer’s disease: pathways, pathogenesis and therapy. Nat. Rev. Neurosci. 10, 333–344 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Langlois, B. et al. LRP-1 promotes cancer cell invasion by supporting ERK and inhibiting JNK signaling pathways. PLoS ONE 5, e11584 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Bian, W. et al. Low-density-lipoprotein-receptor-related protein 1 mediates Notch pathway activation. Dev. Cell 56, 2902–2919 (2021).

    Article  CAS  PubMed  Google Scholar 

  78. Delio, M. et al. Spectrum of elastin sequence variants and cardiovascular phenotypes in 49 patients with Williams–Beuren syndrome. Am. J. Med. Genet. A 161A, 527–533 (2013).

    Article  PubMed  Google Scholar 

  79. Min, S. et al. Genetic diagnosis and the severity of cardiovascular phenotype in patients with elastin arteriopathy. Circ. Genom. Precis. Med. 13, e002971 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Del Pasqua, A. et al. New findings concerning cardiovascular manifestations emerging from long-term follow-up of 150 patients with the Williams–Beuren–Beuren syndrome. Cardiol. Young 19, 563–567 (2009).

    Article  PubMed  Google Scholar 

  81. Callewaert, B. et al. Comprehensive clinical and molecular analysis of 12 families with type 1 recessive cutis laxa. Hum. Mutat. 34, 111–121 (2013).

    Article  CAS  PubMed  Google Scholar 

  82. Andiran, N., Sarikayalar, F., Saraçlar, M. & Cağlar, M. Autosomal recessive form of congenital cutis laxa: more than the clinical appearance. Pediatr. Dermatol. 19, 412–414 (2002).

    Article  PubMed  Google Scholar 

  83. Mauskar, A., Shanbag, P., Ahirrao, V. & Nagotkar, L. Congenital cutis laxa. Ann. Saudi Med. 30, 167–169 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Hinz, B. et al. Recent developments in myofibroblast biology: paradigms for connective tissue remodeling. Am. J. Pathol. 180, 1340–1355 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Hinz, B. The myofibroblast: paradigm for a mechanically active cell. J. Biomech. 43, 146–155 (2010).

    Article  PubMed  Google Scholar 

  86. Noseda, M. et al. Smooth muscle α-actin is a direct target of Notch/CSL. Circ. Res. 98, 1468–1470 (2006).

    Article  CAS  PubMed  Google Scholar 

  87. Klingberg, F., Hinz, B. & White, E. S. The myofibroblast matrix: implications for tissue repair and fibrosis. J. Pathol. 229, 298–309 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Neri, T. et al. Human pre-valvular endocardial cells derived from pluripotent stem cells recapitulate cardiac pathophysiological valvulogenesis. Nat. Commun. 10, 1929 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  89. Cheng, L. et al. Generation and characterization of cardiac valve endothelial-like cells from human pluripotent stem cells. Commun. Biol. 4, 1039 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Skoglund, K., Rosengren, A., Lappas, G., Fedchenko, M. & Mandalenakis, Z. Long-term survival in patients with isolated pulmonary valve stenosis: a not so benign disease? Open Heart 8, e001836 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  91. Warboys, C. M., Ghim, M. & Weinberg, P. D. Understanding mechanobiology in cultured endothelium: a review of the orbital shaker method. Atherosclerosis 285, 170–177 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Gu, M. et al. Patient-specific iPSC-derived endothelial cells uncover pathways that protect against pulmonary hypertension in BMPR2 mutation carriers. Cell Stem Cell 20, 490–504 (2017).

    Article  CAS  PubMed  Google Scholar 

  93. Miao, Y. et al. Cycloheximide (CHX) chase assay to examine protein half-life. Bio Protoc. 13, e4690 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Tran, H. T. N. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 21, 12 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Xie, C. et al. KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res. 39, W316–W322 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Jin, S. et al. Inference and analysis of cell–cell communication using CellChat. Nat. Commun. 12, 1088 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was supported by Additional Ventures (1019125), a CCRF Endowed Scholar Award (M. Gu) and the Chan Zuckerberg Initiative (CZF2019-002440 and CZF2021-237566) (J.R.S.). N.P. received the American Heart Association Predoctoral Fellowship grant 1013861. Z.Y. received the American Heart Association Predoctoral Fellowship grant 906513. We greatly appreciate M. Faust, L. Fist and O. Croweak from the Heart Institute Biorepository (HIBR), Cincinnati Children’s Hospital Medical Center (CCHMC) for collecting and providing human pulmonary valve samples; B. DiPasquale and J. Reuss from Pathology Core, the Discover Together Biobank, CCHMC for support to the study; M. Kofron from Confocal Imaging Core (CIC), CCHMC and J. Kitzmiller from the Division of Pulmonary Biology, CCHMC for providing access to and assistance with the confocal microscope and image processing; and J.C. Wu from Stanford University for providing guidance and support to the project.

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Author notes

  1. These authors contributed equally: Ziyi Liu, Yu Liu, Zhiyun Yu.

Authors and Affiliations

  1. Perinatal Institute, Division of Pulmonary Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Ziyi Liu, Zhiyun Yu, Cheng Tan, Nicole Pek, Minzhe Guo, Yifei Miao & Mingxia Gu

  2. Center for Stem Cell and Organoid Medicine (CuSTOM), Division of Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Ziyi Liu, Zhiyun Yu, Cheng Tan, Nicole Pek, Yifei Miao & Mingxia Gu

  3. Department of Pediatrics, University of Cincinnati School of Medicine, Cincinnati, OH, USA

    Ziyi Liu, Zhiyun Yu, Cheng Tan, Nicole Pek, Anna O’Donnell, Minzhe Guo, Katherine E. Yutzey, Yifei Miao & Mingxia Gu

  4. Cardiovascular Institute, Stanford School of Medicine, Stanford, CA, USA

    Yu Liu, Yifei Miao & Mingxia Gu

  5. Division of Cardiovascular Medicine, Department of Medicine, Stanford School of Medicine, Stanford, CA, USA

    Yu Liu

  6. The Heart Institute, Division of Molecular Cardiovascular Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Anna O’Donnell & Katherine E. Yutzey

  7. Department of Internal Medicine, Gastroenterology, University of Michigan Medical School, Ann Arbor, MI, USA

    Angeline Wu & Jason R. Spence

  8. Department of Pediatrics, Genetic Medicine, University of Washington, Seattle, WA, USA

    Ian Glass

  9. Cardiothoracic Surgery, Heart Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    David S. Winlaw

  10. Department of Surgery, University of Cincinnati School of Medicine, Cincinnati, OH, USA

    David S. Winlaw

  11. Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI, USA

    Jason R. Spence

  12. Department of Biomedical Engineering, University of Michigan College of Engineering, Ann Arbor, MI, USA

    Jason R. Spence

  13. Department of Cell, Developmental and Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Ya-Wen Chen

  14. Department of Otolaryngology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Ya-Wen Chen

  15. Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Ya-Wen Chen

  16. Institute for Airway Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Ya-Wen Chen

  17. Division of Pediatric Cardiology, Department of Pediatrics, Stanford School of Medicine, Stanford, CA, USA

    Yifei Miao & Mingxia Gu

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Contributions

Z.L., Z.Y., C.T., Y.M. and M. Gu conceived and designed experiments. Y.M., Z.Y., I.G., A.W. and J.R.S. collected and dissected human heart samples. Z.L. and Y.L. performed the bioinformatics analysis supervised by M. Guo. Y.-W.C. performed prenatal-based immunofluorescence analysis. Z.L. and Z.Y. performed postnatal valve immunofluorescence analysis supervised by A.O’D. and D.S.W. Z.Y., C.T. and Z.L. performed and analyzed cell-based experiments. K.E.Y. provided suggestions for the experimental design. Z.L., Z.Y., N.P., Y.M. and M. Gu wrote the manuscript with contributions from all other authors. M. Gu and Y.M. oversaw the project. All authors read and approved the manuscript.

Corresponding authors

Correspondence to Yifei Miao or Mingxia Gu.

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Nature Cardiovascular Research thanks Jae-Hoon Choi, Joy Lincoln 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 scRNA-seq of Normal Human Fetal Heart Valves.

a. Violin plot of doublet score, mitochondria percentage, number of counts, and number of features across all valves after quality control: nCount RNA < 1e5, nFeature RNA > = 1000, pMT < 10, Doublet score <0.3. A/P/M/T represent Aortic, Pulmonary, Mitral, Tricuspid valves respectively; b. UMAP visualization of cells from four different human fetal valves from two week-15 healthy hearts after integration by Canonical Correlation Analysis (CCA); c. Left: Cell number and proportion of each valve in each cell type. Right: Dot plot of representative marker genes within each valve cell type; d. Feature plot of representative marker genes in each valve cell type; e. UMAP visualization of four different human fetal valves within VICs; f. Left: Bar graph showing proportions of each valve within each VIC subtype. Right: Bar graph showing proportions of each VIC subtype within each valve; g. Feature plots for marker genes for each VIC subtype in VICs.

Source data

Extended Data Fig. 2 Re-analysis of Public Human Fetal and Mouse Postnatal Heart Valve scRNA-seq Datasets.

a. UMAP visualization of postnatal mouse valve cell types (Hulin et al., 2019); b. Feature plot of representative marker genes within each cell type within mouse valves; c. Development stage visualization (P7 and P30) and feature plots of representative marker genes for each VIC subtype; d. UMAP visualization showing mouse VIC subtypes similar to human GAG-VICs (Left) and Collagen-VICs (Right) through reverse projection; e. UMAP presentation of newly discovered Elastin-VICs, CLDN11-VICs, and Proliferative-VICs; f. UMAP visualization of human fetal valve in W22, W23, and W25 (Cui et al., 2019) integrated by CCA; g. Feature plot of representative marker genes of each cell type in Cui et al, 2019. h. UMAP visualization of VICs in W22, W23, and W25 (Cui et al., 2019).

Source data

Extended Data Fig. 3 APOE Expression and Functions in VICs During Elastogenesis.

a. Left: Representative immunofluorescence staining and RNA in situ hybridization of APOE in four human fetal valves. n = 3 different valves. White arrows represent unidirectional flow directions. Right: Violin plots of APOE and ELN expression within Elastin-VICs across each valve, A/P/M/T represent Aortic, Pulmonary, Mitral, Tricuspid valve respectively; b. Violin plots of elastogenesis-related genes (EMILIN1, LOXL1, FBN1, LTBP1) expression within Elastin-VICs across each valve; c. PCA analysis comparing four valves transcriptome in each VIC subtype; d. qPCR detection of VIC-related marker genes in cultured human fetal VICs, VECs, and HUVECs. Expression levels are normalized to HUVECs; e. Flow cytometry analysis of PECAM1 (VECs) and COL1A1 (VICs) expression in culture VICs; f. immunofluorescence staining of α-SMA (VICs) and PECAM1 (VECs) on cultured VICs; g. qPCR analysis APOE in VICs after APOE KD; h. Quantification of total elastin (both tropo-elastin(soluble) and mature elastin (insoluble)) in VICs after APOE KD. The elastin content was normalized to the total protein content; i. qPCR analysis of elastogenesis-related genes in VICs after APOE KD; j. Violin plot and feature plot of ACTA2 expression in VIC subtypes. FDR: Elastin vs. other VIC subclusters; k. Representative immunofluorescence staining of α-SMA and Elastin in four human fetal valves. White arrows represent unidirectional flow directions. Yellow arrowhead: Elastin-VIC positive for α-SMA. White arrowhead: VICs positive for α-SMA but not Elastin. n = 3; l. Contraction assay comparing scramble and APOE KD in VICs; m. qPCR analysis of contraction-related gene expressions in VICs comparing scramble and APOE KD. Data are shown as the mean ± SEM. n = 3 biological repeats. ns p > 0.05, *p < 0.05, **p < 0.01, ***p < 0.001. For g-i, l-m, scramble vs. APOE KD; d-e, VICs vs. VECs. Statistics in d-e, g-i, l-m: Unpaired 2-tailed t-test (2 groups). P values in d: COL1A1: p = 0.0121, ACTA2: p = 0.0437; in e: p < 0.0001; in g: p = 0.0023; in i: FBN2: p = 0.0265, LTBP2: p = 0.6162, LTBP4: p = 0.0422, FBLN1: p = 0.0327, FBLN2: p = 0.0526, LOX: p = 0.0003; in l: p = 0.0362; in m: ACTA2: p = 0.0011, MYH11: p = 0.0223, SMTN: p = 0.1664, CNN1: p = 0.0247. For j: Statistics adhered to a two-sided approach. Significance was determined by adjusted p-values (FDRs) < 0.05. FDR for j: FDR = 8.43E-203. FDR: False Discovery Rate. HUVECs: Human Umbilical Vein Endothelial Cells. KD: Knockdown.

Extended Data Fig. 4 Overview of Elastin-related Staining and scRNA-seq for PV with Pulmonary Stenosis.

a. Left: Verhoeff staining of pulmonary valves from the other two healthy control and age-matched Pulmonary Stenosis (PS) patients. The black staining within the dashed area indicated the elastin fiber. Right: Quantification of positive staining area of elastin fiber within elastin layer. Age of the sample were from 5-months to 10-years; b. Immunofluorescence staining of ELN, APOE, EMILIN1, and LOXL1 in pulmonary valves from control and PS. White dashed boxes corresponded to the zoom-in figures shown in Fig. 3b. n = 3 different valves; c. UMAP projection of valve cell types within pulmonary valves (PVs) from one healthy control and one patient with PS; d. Feature plots of representative marker genes within each cell type and subtype from c; e. Feature plots of representative marker genes of VIC subtypes comparing PVs from one control and one PS; f. Heatmap demonstrating the gene expression changes of elastase (MMPs) and elastase inhibitors (TIMPs) within each PV-VIC subcluster; g. qPCR of MMPs and TIMPs gene expressions in cultured human VICs comparing scramble vs. APOE KD. n = 3 biological repeats. Data shown as mean ± SEM. ns p > 0.05, *p < 0.05. For a: control vs. PS; For g: scramble vs. APOE KD. Statistics in a, g: Unpaired 2-tailed t-test (2 groups). P value in a: p = 0.0407; in g: MMP1: p = 0.2031, MMP2: p = 0.0649, MMP3: p = 0.1083, MMP9: p = 0.0635; MMP12: p = 0.1017, MMP13: p = 0.2405, MMP14: p = 0.2096, TIMP2: p = 0.0198, TIMP3: p = 0.0313, TIMP4: p = 0.8384.

Extended Data Fig. 5 Overview of VEC Populations from Human and Mouse scRNA-seq and Spatial Location.

a. UMAP demonstrating the distribution of four valves within VECs; b. Left: Bar graph showing proportions of each valve within each VEC subtype. Right: Bar graph showing proportions of each VEC subtype within each valve; c. Feature plots of marker genes for each VEC subtype; d. UMAP visualization of mouse VEC subtypes and timepoint distribution (Hulin et al., 2019); e. Feature plots of marker genes for each VEC subtype from mouse dataset; f-g. UMAP demonstration of the similarity of VEC subtypes between human and mouse VECs through reverse projection; h-i. Immunofluorescence staining of FOXC2 in four human fetal valves at W15 (h). Zoom out of immunofluorescence staining of FOXC2, CD55 and RNA in situ hybridization of PTGDS in four human fetal valves at W15 (i). White arrows represent unidirectional flow directions. n = 3 different hearts; j. UMAP visualization of each VEC subtype (Upper), and violin plot of CD55, PTGDS, FOXC2 expression among four valves (Lower); k. RNA in situ hybridization of PTGDS, APOE, PECAM1, and immunofluorescence staining of CD55, APOE, and PECAM1 in four human fetal valves at W15. n = 3 different hearts; l. Quantification of PTGDS+ VECs from Fig. 4d; m. PCA Analysis comparing four different valves in each VECs subtype. For l: Data shown as mean ± SEM. ***p < 0.001. SL vs. AV. Statistics: Unpaired 2-tailed t-test (two groups). P value in l: p = 0.0007. SL valves: Semilunar valves, A-V valves: Atrioventricular valves.

Source data

Extended Data Fig. 6 JAG1-NOTCH2 Mediated Notch Signal Between UDVECs and Elastin-VICs.

a. Major Extracellular Matrix (ECM)-related Ligand-Receptor (L-R) pairs between Elastin-VICs and unidirectional VECs (UDVECs) subtypes; b. Total L-R pair numbers between UDVECs and Elastin-VICs; c. Notch-related signaling patterns from UDVECs to Elastin-VICs; d. Gene Ontology (GO) enrichment analysis of differential expressed genes within each VIC subtype; e. Violin plot of DLL1 in VECs. FDR: PTGDS-VECs vs. other VEC subtypes; f. RNA in situ hybridization staining of PTGDS, JAG1, and NOTCH3 in four human fetal valves at W15, n = 3 different hearts; g. Violin plots of NOTCH1-3, HES1, HES4, and HEY2 in VICs. FDR: Elastin-VICs vs. other VIC subtypes; h. Immunofluorescence staining of NOTCH3, NOTCH1, HES1, and Elastin in four different valves; i. Violin plots of Notch receptors and downstream target gene expressions within Elastin-VICs (Left), and Notch ligands in UDVECs (Right) across each valve; j. qPCR analysis of Notch target genes in VICs with Jag1 (15 μg/ml) treatment; k. qPCR analysis of Notch receptors and target genes in cultured human VICs after combinational KD of NOTCH receptors. Jag1 (15ug/ml) was included; n = 3 biological repeats. Data shown as mean ± SEM. ns p > 0.05, *p < 0.05, **p < 0.01, ***p < 0.001. For g, i: Statistics adhered to a two-sided approach. Significance was determined by adjusted p-values (FDRs) < 0.05. For j, Control vs. Jag1 treatment. Unpaired 2-tailed t-test (two groups); P values for j: HEY2: p = 0.0006, HEYL: p = 0.0265, HES1: p = 0.2450. For k: one-way ANOVA followed by Tukey’s test, vs Scramble. P values for k: NOTCH2: Scramble vs. siN1: p > 0.9999, vs. siN2: p = 0.0008, vs. siN3: p = 0.1982, vs. siN1 + N2: p = 0.0024, vs. siN1 + N3: p > 0.9999, vs. siN2 + N3: p = 0.0008, vs. siN1 + N2 + N3: p = 0.0004, NOTCH1: Scramble vs. siN1: p < 0.0001, vs. siN2: p = 0.0025, vs. siN3: p = 0.0095, vs. siN1 + N2: p < 0.0001, vs. siN1 + N3: p < 0.0001, vs. siN2 + N3: p = 0.0206, vs. siN1 + N2 + N3: p < 0.0001, NOTCH3: Scramble vs. siN1: p = 0.3875, vs. siN2: p = 0.0053, vs. siN3: p < 0.0001, vs. siN1 + N2: p = 0.0038, vs. siN1 + N3: p < 0.0001, vs. siN2 + N3: p < 0.0001, vs. siN1 + N2 + N3: p < 0.0001, HEY2: Scramble vs. siN1: p = 0.6668, vs. siN2: p = 0.0413, vs. siN3: p = 0.9996, vs. siN1 + N2: p = 0.0510, vs. siN1 + N3: p = 0.9210, vs. siN2 + N3: p = 0.0238, vs. siN1 + N2 + N3: p = 0.0122, HEYL: Scramble vs. siN1: p = 0.2549, vs. siN2: p = 0.0001, vs. siN3: p = 0.0745, vs. siN1 + N2: p < 0.0001, vs. siN1 + N3: p = 0.0014, vs. siN2 + N3: p = 0.0001, vs. siN1 + N2 + N3: p < 0.0001. ECM: Extracellular Matrix, L-R: Ligand-Receptors.

Source data

Extended Data Fig. 7 Flow-induced JAG1-NOTCH2 Activation Between VICs and VECs in vitro.

a. qPCR detection of VEC-related genes in cultured VECs, VICs, and HUVECs. Expression levels are normalized to HUVECs; b. Flow cytometry analysis of PECAM1 (VECs) and COL1A1 (VICs) expression in culture VECs; c. Immunostaining of PECAM1 (VECs) and α-SMA (VICs) on cultured VECs; d. Representative bright field imaging of VECs under static, disturbed, and unidirectional/laminar flow conditions for 48 hours; e. Western blot and statistical analysis of JAG1 and GAPDH in VECs under different flow conditions. Unidirectional shear stress: 8.52 dynes/cm2; f. qPCR analysis of flow responsive genes and JAG1 expression in VECs under static or unidirectional laminar flow; g. Demonstration figure of VECs-VICs co-cultured in no-contacting condition under laminar flow; h. Immunofluorescence staining of α-SMA, NOTCH2, HES1, and EMILIN1 in VICs co-cultured with non-contacting VECs; i. qPCR detection of HEYL and HES1 in VICs co-cultured with VECs under flow conditions in partial-contact manner; j. Demonstration figure of VICs co-cultured with VEC under JAG1 KD in partial-contacting condition under laminar flow; k. qPCR detection of JAG1 in VECs under scramble or JAG1 KD; l-m. Immunofluorescence staining of α-SMA,NOTCH2 (l), and HES1 (m) in VICs co-cultured with partial-contacting VECs with JAG1 KD; n. Demonstration figure of VICs co-cultured with VECs after JAG1 overexpression (OE) in partial-contacting conditions under static flow; o. Western blot and statistical analysis of JAG1 in VECs with empty vector or JAG1 OE; p. Immunofluorescence staining of NOTCH2, NOTCH3, HES1, α-SMA, and PECAM1 in VICs co-cultured with VECs with JAG1 OE under static flow. n = 3 biological repeats. Data shown as ± SEM. ns p > 0.05, *p < 0.05, **p < 0.01, ***p < 0.001. For a-b: VICs vs. VECs; f, i: Static vs. Laminar flow; P values in a: PECAM1: p < 0.0001, CDH5: p = 0.0002; in b: p < 0.0001; k: scramble vs. JAG1 KD; o: empty vector vs. JAG1 OE. Statistics in a-b, f, i, k, o: Unpaired 2-tailed t-test (two groups), in e: one-way ANOVA followed by Tukey’s test, vs. Static flow. P values in e: Static vs. Disturbed flow: p = 0.6939, Static vs. Unidirectional flow: p = 0.0038; in f: JAG1: p = 0.0331, KLF2: p = 0.0065, KLF4: p = 0.0183, NOS3: p = 0.0129; in i: HEYL: p = 0.0412, HES1: p = 0.0818; in k: p < 0.0001; in o: p = 0.0224. Panels g, j and n created with BioRender.com.

Source data

Extended Data Fig. 8 APOE Controlled Jag1-induced Elastogenesis and Contraction in VICs.

a. qPCR analysis of elastogenesis-related genes in VICs with Jag1 treatment; b. Contraction assay of VICs with Jag1 treatment; c. qPCR analysis of contraction-related genes in VICs with Jag1 treatment. Jag1: 15 μg/ml; d. qPCR analysis of FBN2, LTBP1, FBLN2 and EMILIN1 expressions in each NOTCH receptor KD condition; e. Contraction assay of VICs with Jag1 treatment and APOE KD; f. qPCR analysis of contraction-related genes in VICs with Jag1 treatment and APOE KD. Jag1: 15 μg/ml. n = 3 biological repeats in each panel. Data shown as ± SEM. ns p > 0.05, *p < 0.05, **p < 0.01, ***p < 0.001. For a-c, control vs. Jag1 treatment. unpaired 2-tailed t-test (two groups). P value in a: ELN: p = 0.4570, FBN1: p = 0.0902, LTBP1: p = 0.0023, EMILIN1: p = 0.0060, LOXL1: p = 0.0083, FBN2: p = 0.0384, LTBP2: p = 0.7548, LTBP4: 0.0451, FBLN1: p = 0.4267, FBLN2: p = 0.0126, LOX: p = 0.0217; in c: ACTA2: p = 0.0185, MYH11: p = 0.0101, SMTN: p = 0.4497, CNN1: p = 0.0974, TAGLN: p = 0.0096. In d-f: one-way ANOVA followed by Tukey’s test, vs. Scramble. P values in d: FBN2: Scramble vs. siN1: p > 0.9999, vs. siN2: p = 0.0319, vs. siN3: p = 0.9288, vs. siN1 + N2: p = 0.0937, vs. siN1 + N3: p > 0.9999, vs. siN2 + N3: p = 0.0525, vs. siN1 + N2 + N3: p = 0.0029, LTBP1: Scramble vs. siN1: p = 0.7849, vs. siN2: p = 0.0178, vs. siN3: p = 0.2424, vs. siN1 + N2: p = 0.0004, vs. siN1 + N3: p = 0.9567, vs. siN2 + N3: p = 0.0098, vs. siN1 + N2 + N3: p = 0.0035, FBLN2: Scramble vs. siN1: p > 0.9999, vs. siN2: p = 0.0147, vs. siN3: p = 0.7622, vs. siN1 + N2: p = 0.0084, vs. siN1 + N3: p = 0.6843, vs. siN2 + N3: p = 0.0968, vs. siN1 + N2 + N3: p = 0.0022, EMILIN1: Scramble vs. siN1: p = 0.9949, vs. siN2: p = 0.5744, vs. siN3: p = 0.9886, vs. siN1 + N2: p = 0.1343, vs. siN1 + N3: p = 0.9997, vs. siN2 + N3: p = 0.0356, vs. siN1 + N2 + N3: p = 0.0235; in e: Ctrl vs. Jag1: p = 0.3496, Jag1 vs. Jag1+APOE KD: p = 0.0060; in f: ACTA2: Ctrl vs. Jag1: p = 0.0125, Jag1 vs. Jag1+APOE KD: p = 0.0035, CNN1: Ctrl vs. Jag1: p = 0.0002, Jag1 vs. Jag1+APOE KD: p = 0.0006.

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Extended Data Fig. 9 Overview of VEC scRNA-seq from Pulmonary Valves with Pulmonary Stenosis.

a. Feature plots of markers genes for each VEC subtype combining valves from one control and one patient with PS; b. Violin plots comparing JAG1 expression between three VEC subtypes; c. Immunofluorescence staining and percentage of NOTCH1+ & NOTCH3+ VICs in Elastin layer VICs in pulmonary valve tissues from control vs. PS. n = 3 different samples in each group. Data shown as ± SEM. **p < 0.01, ***p < 0.001, control vs. PS. Statistics in c: Unpaired 2-tailed t-test (two groups). P value in c: NOTCH1: p = 0.0493, NOTCH3: p = 0.0002.

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Extended Data Fig. 10 APOE Showed No Effect on Notch Degradation or Akt Signaling.

a-b. Quantification of NOTCH2 nuclei positive signals per cell in Fig. 7b,e. c. Western blot of NOTCH2 and GAPDH in VICs treated with 20 μg/ml cycloheximide (CHX) and harvested at the indicated time points; d. Western blot of p-AKT and pan AKT in VICs with empty vector and APOE OE; e. Demonstration figure: On one side, unidirectional flow induced Jag1 production in VECs, which activated NOTCH2-mediated Notch signaling in contacting Elastin-VICs, and promoted downstream elastogenesis-related protein expressions. On the other side, APOE facilitated NOTCH2 and its downstream signaling and elastogenesis protein expressions through MAPK/ERK pathway. Insufficient APOE found in PS patients suppressed Notch signaling, leading to elastogenesis defects. n = 3 biological repeats in each panel. Data shown as ± SEM. ns p > 0.05, **p < 0.01. For a, Jag1 treatment (15 μg/ml) vs. Jag1 treatment and APOE KD. For b, d, Empty vector vs. APOE OE. In a, b, d, unpaired two-tailed t-test. P values in a: p = 0.0048; in b: p = 0.0035; In c, Scramble vs. APOE KD (upper), empty vectors vs. APOE OE (lower), unpaired 2-tailed t-test (two groups), comparing conditions within each time point. Panel e created with BioRender.com.

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Supplementary information

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Supplementary Tables 1 and 2 and Fig. 1.

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Liu, Z., Liu, Y., Yu, Z. et al. APOE–NOTCH axis governs elastogenesis during human cardiac valve remodeling. Nat Cardiovasc Res 3, 933–950 (2024). https://doi.org/10.1038/s44161-024-00510-3

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