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Integrative gene regulatory network analysis discloses key driver genes of fibromuscular dysplasia

Nature Cardiovascular Research volume 3, pages 1098–1122 (2024)Cite this article

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Abstract

Fibromuscular dysplasia (FMD) is a poorly understood disease affecting 3–5% of adult females. The pathobiology of FMD involves arterial lesions of stenosis, dissection, tortuosity, dilation and aneurysm, which can lead to hypertension, stroke, myocardial infarction and even death. Currently, there are no animal models for FMD and few insights as to its pathobiology. In this study, by integrating DNA genotype and RNA sequence data from primary fibroblasts of 83 patients with FMD and 71 matched healthy controls, we inferred 18 gene regulatory co-expression networks, four of which were found to act together as an FMD-associated supernetwork in the arterial wall. After in vivo perturbation of this co-expression supernetwork by selective knockout of a top network key driver, mice developed arterial dilation, a hallmark of FMD. Molecular studies indicated that this supernetwork governs multiple aspects of vascular cell physiology and functionality, including collagen/matrix production. These studies illuminate the complex causal mechanisms of FMD and suggest a potential therapeutic avenue for this challenging disease.

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Fig. 1: SN-A is an important gene regulatory co-expression SN governing FMD.
Fig. 2: Visual representation of SN-A, its GO terms and green and cyan networks.
Fig. 3: UBR4 is a key driver of SN-A and shows robust expression in SMCs of adult human arteries.
Fig. 4: In vivo perturbation of SN-A by SMC-specific Ubr4 knockout in female mice (Sm22α-Ubr4KO) recapitulates the arterial dilation phenotype of FMD.
Fig. 5: In vivo perturbation of SN-A by SMC-specific Ubr4 knockout in male SMMHC-Ubr4KO mice validates an arterial dilation phenotype.
Fig. 6: scRNA-seq of arterial tissues from tdT-Sm22α-Ubr4KO and control mice confirms that SMC-specific Ubr4 knockout leads to changes in extracellular collagen/matrix and also in specific SMC clusters.

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Data availability

scRNA-seq data are available at the National Center for Biotechnology Informationʼs Gene Expression Omnibus database under accession number GSE242708. Mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifiers PXD049359 (UBR4 knockdown studies in both HASMCs and Bj-5ta fibroblasts) and PXD051750 (mouse aortas). Bulk RNA-seq data for primary human fibroblasts from study patients, and also both HASMCs and Bj-5ta fibroblasts, have been deposited in the database of Genotypes and Phenotypes (dbGAP) under accession number phs003674.v1.p1. With respect to the bulk RNA-seq data for primary human fibroblasts, of the 154 human subjects in this analysis, 62 did not provide consent to have their genomic data made publicly available. Therefore, the human subject data available at the dbGAP under accession phs003674.v1.p1 are from the 92 study participants who provided written informed consent to share their de-identified genomic data publicly. Source data are provided with this paper.

Code availability

No original code was used or created for this study.

References

  1. Sottiurai, V. S., Fry, W. J. & Stanley, J. C. Ultrastructure of medial smooth muscle and myofibroblasts in human arterial dysplasia. Arch. Surg. 113, 1280–1288 (1978).

    Article  CAS  PubMed  Google Scholar 

  2. Olin, J. W. et al. The United States Registry for Fibromuscular Dysplasia: results in the first 447 patients. Circulation 125, 3182–3190 (2012).

    Article  PubMed  Google Scholar 

  3. Pappaccogli, M. et al. The European/International Fibromuscular Dysplasia Registry and Initiative (FEIRI)—clinical phenotypes and their predictors based on a cohort of 1000 patients. Cardiovasc. Res. 117, 950–959 (2021).

    Article  CAS  PubMed  Google Scholar 

  4. Gornik, H. L. et al. First International Consensus on the diagnosis and management of fibromuscular dysplasia. Vasc. Med. 24, 164–189 (2019).

    Article  PubMed  Google Scholar 

  5. Hendricks, N. J. et al. Is fibromuscular dysplasia underdiagnosed? A comparison of the prevalence of FMD seen in CORAL trial participants versus a single institution population of renal donor candidates. Vasc. Med. 19, 363–367 (2014).

    Article  PubMed  Google Scholar 

  6. Plouin, P. F. et al. Fibromuscular dysplasia. Orphanet J. Rare Dis. 2, 28–35 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Leadbetter, W. F. & Burkland, C. R. Hypertension in unilateral renal disease. J. Urol. 39, 611–626 (1938).

    Article  Google Scholar 

  8. Olin, J. W. et al. Fibromuscular dysplasia: state of the science and critical unanswered questions: a scientific statement from the American Heart Association. Circulation 129, 1048–1078 (2014).

    Article  PubMed  Google Scholar 

  9. Georges, A. et al. Genetic investigation of fibromuscular dysplasia identifies risk loci and shared genetics with common cardiovascular diseases. Nat. Commun. 12, 6031 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Stanley, J. C., Gewertz, B. L., Bove, E. L., Sottiurai, V. & Fry, W. J. Arterial fibrodysplasia. Histopathologic character and current etiologic concepts. Arch. Surg. 110, 561–566 (1975).

    Article  CAS  PubMed  Google Scholar 

  11. Bruno, R. M. et al. Deep vascular phenotyping in patients with renal multifocal fibromuscular dysplasia. Hypertension 73, 371–378 (2019).

    Article  CAS  PubMed  Google Scholar 

  12. Kadian-Dodov, D. et al. Dissection and aneurysm in patients with fibromuscular dysplasia: findings from the U.S. Registry for FMD. J. Am. Coll. Cardiol. 68, 176–185 (2016).

    Article  PubMed  Google Scholar 

  13. Schwartz, A. M. et al. Aortic dimensions are larger in patients with fibromuscular dysplasia. J. Am. Heart Assoc. 11, e023858 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Katz, A. E. et al. Fibromuscular dysplasia and abdominal aortic aneurysms are dimorphic sex-specific diseases with shared complex genetic architecture. Circ. Genom. Precis. Med. 15, e003496 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Michelis, K. C., Olin, J. W., Kadian-Dodov, D., d’Escamard, V. & Kovacic, J. C. Coronary artery manifestations of fibromuscular dysplasia. J. Am. Coll. Cardiol. 64, 1033–1046 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Guill, C. K., Benavides, D. C., Rees, C., Fenves, A. Z. & Burton, E. C. Fatal mesenteric fibromuscular dysplasia: a case report and review of the literature. Arch. Intern. Med. 164, 1148–1153 (2004).

    Article  PubMed  Google Scholar 

  17. Kiando, S. R. et al. PHACTR1 is a genetic susceptibility locus for fibromuscular dysplasia supporting its complex genetic pattern of inheritance. PLoS Genet. 12, e1006367 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Georges, A. et al. Rare loss-of-function mutations of PTGIR are enriched in fibromuscular dysplasia. Cardiovasc. Res. 117, 1154–1165 (2021).

    Article  CAS  PubMed  Google Scholar 

  19. Olin, J. W. et al. A plasma proteogenomic signature for fibromuscular dysplasia. Cardiovasc. Res. 116, 63–77 (2020).

  20. Richer, J. et al. A novel recurrent COL5A1 genetic variant is associated with a dysplasia-associated arterial disease exhibiting dissections and fibromuscular dysplasia. Arterioscler. Thromb. Vasc. Biol. 40, 2686–2699 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Bjorkegren, J. L. M., Kovacic, J. C., Dudley, J. T. & Schadt, E. E. Genome-wide significant loci: how important are they? Systems genetics to understand heritability of coronary artery disease and other common complex disorders. J. Am. Coll. Cardiol. 65, 830–845 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Zeng, L. et al. Contribution of gene regulatory networks to heritability of coronary artery disease. J. Am. Coll. Cardiol. 73, 2946–2957 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Koplev, S. et al. A mechanistic framework for cardiometabolic and coronary artery diseases. Nat. Cardiovasc. Res. 1, 85–100 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Ma, L. et al. Multiple independent mechanisms link gene polymorphisms in the region of ZEB2 with risk of coronary artery disease. Atherosclerosis 311, 20–29 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Hao, K. et al. Integrative prioritization of causal genes for coronary artery disease. Circ. Genom. Precis. Med. 15, e003365 (2022).

    Article  CAS  PubMed  Google Scholar 

  26. Ma, L. et al. The HDAC9-associated risk locus promotes coronary artery disease by governing TWIST1. PLoS Genet. 18, e1010261 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Talukdar, H. A. et al. Cross-tissue regulatory gene networks in coronary artery disease. Cell Syst. 2, 196–208 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. GTEx Consortium. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

    Article  PubMed Central  Google Scholar 

  29. McCormack, L. J., Poutasse, E. F., Meaney, T. F., Noto, T. J. Jr & Dustan, H. P. A pathologic-arteriographic correlation of renal arterial disease. Am. Heart J. 72, 188–198 (1966).

  30. Uhlen, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015).

    Article  PubMed  Google Scholar 

  31. Heidt, S. T., Ganesh, S. K., Liu, P., Froehlich, J. B. & Kline-Rogers, E. Bilateral internal mammary artery fibromuscular dysplasia discovered upon evaluation for reconstructive breast surgery. Vasc. Med. 20, 487–488 (2015).

    Article  PubMed  Google Scholar 

  32. Nakaya, T. et al. p600 plays essential roles in fetal development. PLoS ONE 8, e66269 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Tasaki, T. et al. UBR box N-recognin-4 (UBR4), an N-recognin of the N-end rule pathway, and its role in yolk sac vascular development and autophagy. Proc. Natl Acad. Sci. USA 110, 3800–3805 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Nakatani, Y. et al. p600, a unique protein required for membrane morphogenesis and cell survival. Proc. Natl Acad. Sci. USA 102, 15093–15098 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Chakraborty, R. et al. Promoters to study vascular smooth muscle. Arterioscler. Thromb. Vasc. Biol. 39, 603–612 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. O’Brien, B. J., Martin, K. A. & Offermanns, S. ‘Cre’ating new tools for smooth muscle analysis. Arterioscler. Thromb. Vasc. Biol. 43, 212–214 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Deaton, R. A. et al. A new autosomal Myh11-CreERT2 smooth muscle cell lineage tracing and gene knockout mouse model—brief report. Arterioscler. Thromb. Vasc. Biol. 43, 203–211 (2023).

    Article  CAS  PubMed  Google Scholar 

  38. Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Schadt, E. E. & Bjorkegren, J. L. NEW: network-enabled wisdom in biology, medicine, and health care. Sci. Transl. Med. 4, 115rv1 (2012).

    Article  PubMed  Google Scholar 

  40. Barabasi, A. L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Ganesh, S. K. et al. Clinical and biochemical profiles suggest fibromuscular dysplasia is a systemic disease with altered TGF-β expression and connective tissue features. FASEB J. 28, 3313–3324 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Latosinska, A. et al. Increased collagen turnover is a feature of fibromuscular dysplasia and associated with hypertrophic radial remodeling: a pilot, urine proteomic study. Hypertension 79, 93–103 (2022).

    Article  CAS  PubMed  Google Scholar 

  43. Kim, S. T. et al. The N-recognin UBR4 of the N-end rule pathway is required for neurogenesis and homeostasis of cell surface proteins. PLoS ONE 13, e0202260 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Hunt, L. C. et al. Antagonistic control of myofiber size and muscle protein quality control by the ubiquitin ligase UBR4 during aging. Nat. Commun. 12, 1418 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Hunt, L. C. et al. A key role for the ubiquitin ligase UBR4 in myofiber hypertrophy in Drosophila and mice. Cell Rep. 28, 1268–1281 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Wang, Y. et al. Burden of rare genetic variants in spontaneous coronary artery dissection with high-risk features. JAMA Cardiol. 7, 1045–1055 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Adlam, D. et al. Genome-wide association meta-analysis of spontaneous coronary artery dissection identifies risk variants and genes related to artery integrity and tissue-mediated coagulation. Nat. Genet. 55, 964–972 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Tarr, I. et al. Exploring the genetic architecture of spontaneous coronary artery dissection using whole-genome sequencing. Circ. Genom. Precis. Med. 15, e003527 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Michelis, K. C. et al. CD90 identifies adventitial mesenchymal progenitor cells in adult human medium- and large-sized arteries. Stem Cell Rep. 11, 242–257 (2018).

    Article  CAS  Google Scholar 

  50. Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

    Article  CAS  PubMed  Google Scholar 

  51. Andrews, S. FastQC: a quality control tool for high throughput sequence data. Babraham Bioinformatics http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).

  52. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  53. Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E. & Storey, J. D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882–883 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Mi, H., Muruganujan, A., Ebert, D., Huang, X. & Thomas, P. D. PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. Nucleic Acids Res. 47, D419–D426 (2019).

    Article  CAS  PubMed  Google Scholar 

  58. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Huynh-Thu, V. A., Irrthum, A., Wehenkel, L. & Geurts, P. Inferring regulatory networks from expression data using tree-based methods. PLoS ONE 5, e12776 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Shu, L. et al. Mergeomics: multidimensional data integration to identify pathogenic perturbations to biological systems. BMC Genomics 17, 874 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Teufel, F. et al. SignalP 6.0 predicts all five types of signal peptides using protein language models. Nat. Biotechnol. 40, 1023–1025 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Shao, X. et al. MatrisomeDB 2.0: 2023 updates to the ECM-protein knowledge database. Nucleic Acids Res. 51, D1519–D1530 (2023).

    Article  PubMed  Google Scholar 

  64. Feil, S., Hofmann, F. & Feil, R. SM22α modulates vascular smooth muscle cell phenotype during atherogenesis. Circ. Res. 94, 863–865 (2004).

    Article  CAS  PubMed  Google Scholar 

  65. Kühbandner, S. et al. Temporally controlled somatic mutagenesis in smooth muscle. Genesis 28, 15–22 (2000).

    Article  PubMed  Google Scholar 

  66. Wirth, A. et al. G12-G13–LARG-mediated signaling in vascular smooth muscle is required for salt-induced hypertension. Nat. Med. 14, 64–68 (2008).

    Article  CAS  PubMed  Google Scholar 

  67. Madisen, L. et al. A robust and high-throughput Cre reporting and characterization system for the whole mouse brain. Nat. Neurosci. 13, 133–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  68. Evrard, S. M. et al. Endothelial to mesenchymal transition is common in atherosclerotic lesions and is associated with plaque instability. Nat. Commun. 7, 11853 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Evangelista, A. et al. Echocardiography in aortic diseases: EAE recommendations for clinical practice. Eur. J. Echocardiogr. 11, 645–658 (2010).

    Article  PubMed  Google Scholar 

  70. Gouma, E. et al. A simple procedure for estimation of total body surface area and determination of a new value of Meeh’s constant in rats. Lab Anim. 46, 40–45 (2012).

    Article  CAS  PubMed  Google Scholar 

  71. Lindsey, M. L., Kassiri, Z., Virag, J. A. I., de Castro Bras, L. E. & Scherrer-Crosbie, M. Guidelines for measuring cardiac physiology in mice. Am. J. Physiol. Heart Circ. Physiol. 314, H733–H752 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Morris, S. A. et al. Increased vertebral artery tortuosity index is associated with adverse outcomes in children and young adults with connective tissue disorders. Circulation 124, 388–396 (2011).

    Article  PubMed  Google Scholar 

  73. Smyth, G. K. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, Article3 (2004).

  74. Lu, S. et al. Smooth muscle-derived progenitor cell myofibroblast differentiation through KLF4 downregulation promotes arterial remodeling and fibrosis. JCI Insight 5, e139445 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Heumos, L. et al. Best practices for single-cell analysis across modalities. Nat. Rev. Genet. 24, 550–572 (2023).

    Article  CAS  PubMed  Google Scholar 

  76. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Kim, E. S. H., Saw, J., Kadian-Dodov, D., Wood, M. & Ganesh, S. K. FMD and SCAD: sex-biased arterial diseases with clinical and genetic pleiotropy. Circ. Res. 128, 1958–1972 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This study was funded by the US National Institutes of Health (R01HL148167 to J.C.K., D.K.-D., J.W.O., J.L.M.B., K.D.C. and M.C.M.W.-E.) and additional philanthropic support. J.C.K. also acknowledges research support from New South Wales health grant RG194194, the Bourne Foundation, Snow Medical and Agilent. J.L.M.B. acknowledges support from the Swedish Research Council (2018-02529 and 2022-00734), the Swedish Heart Lung Foundation (2017-0265 and 2020-0207), the Leducq Foundation AteroGen (22CVD04) and PlaqOmics (18CVD02) consortia; the National Institutes of Health National Heart, Lung, and Blood Institute (NIH/NHLBI, R01HL164577, R01HL148167, R01HL148239, R01HL166428 and R01HL168174); American Heart Association Transformational Project Award 19TPA34910021; and the CMD AMP fNIH program. J.L.M.B. also acknowledges the European Union’s Horizon Europe (European Innovation Council) program under grant agreement number 101115381. Y.X. is supported by the NYC Train KUHR Consortium (NIH grant TL1DK136048). We acknowledge the assistance and technical expertise of the Microscopy and Advanced Bioimaging Core, the BioMedical Engineering and Imaging Institute and the Center for Comparative Medicine and Surgery of the Icahn School of Medicine at Mount Sinai. University of Colorado Shared Resources are supported by the National Cancer Institute through Cancer Center Support Grant P30CA06934. We also acknowledge and thank all the participants in this study.

Author information

Authors and Affiliations

  1. Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Valentina d’Escamard, Lijiang Ma, Yang Xu, Bhargravi V′Gangula, Allison Thomas, Katherine C. Michelis, Rihab Bouchareb, Aya Nomura-Kitabayashi, Robert J. Wiener, Kevin D. Costa, Elena Chepurko, Vadim Chepurko, Manuel Mayr, Michael G. Katz & Jason C. Kovacic

  2. Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Daniella Kadian-Dodov, Annette King, Yu Zhou, Emir Bander, Michael G. Katz, Johan L. M. Björkegren, Jeffrey W. Olin & Jason C. Kovacic

  3. Department of Genetics & Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Lijiang Ma, Shouneng Peng, Ke Hao & Johan L. M. Björkegren

  4. Department of Medicine, Division of Renal Diseases and Hypertension, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

    Sizhao Lu & Mary C. M. Weiser-Evans

  5. School of Medicine, Consortium for Fibrosis Research and Translation, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

    Sizhao Lu & Mary C. M. Weiser-Evans

  6. INSERM, UMR970 Paris Cardiovascular Research Center (PARCC), Paris, France

    Adrien Georges & Nabila Bouatia-Naji

  7. Paris-Descartes University, Sorbonne Paris Cité, Paris, France

    Adrien Georges & Nabila Bouatia-Naji

  8. King’s British Heart Foundation Centre, King’s College London, London, UK

    Marika Fava, Temo Barwari & Manuel Mayr

  9. Department of Cardiovascular Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Anelechi Anyanwu & Farzan Filsoufi

  10. Recanati-Miller Transplantation Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Sander Florman

  11. Department of Internal Medicine II, Division of Cardiology, Medical University of Vienna, Vienna, Austria

    Lukas E. Schmidt

  12. Cardiovascular Pulmonary Research Program, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

    Mary C. M. Weiser-Evans

  13. Integrated Physiology PhD Program, Anschutz Medical Campus, Aurora, CO, USA

    Mary C. M. Weiser-Evans

  14. Department of Medicine, Karolinska Institutet, Karolinska Universitetssjukhuset, Huddinge, Sweden

    Johan L. M. Björkegren

  15. Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales, Australia

    Jason C. Kovacic

  16. St Vincent’s Clinical School, University of NSW, Sydney, New South Wales, Australia

    Jason C. Kovacic

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  1. Valentina d’Escamard
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V.d’.E. processed patient samples, performed most in vitro studies and coordinated in vivo studies. D.K.-D. and J.W.O. were responsible for coordination of clinical study enrollment and clinical aspects of this study. A.K. enrolled most of the participants into the study and was assisted by E.B. L.M. was the primary person responsible for conducting bioinformatics and network analyses, and initial network analyses were performed by S.P. K.H. and J.L.M.B. oversaw and supervised bioinformatics and network analyses. S.L. conducted scRNA-seq analyses, and M.C.M.W.-E. oversaw and planned scRNA-seq experiments. Y.X. assisted with immunofluorescence confocal microscopy and statistical analyses and created all figures. B.V. was responsible for dissecting mice and for mounting and staining most mouse samples, and A.N.-K., R.J.W. and K.D.C. also assisted with mouse protocols and studies. M.M. planned and oversaw proteomics studies, and M.F., T.B. and L.E.S. performed these experiments. A.T. performed most immunofluorescence confocal microscopy on human samples. K.C.M. established the separate human clinical protocol under which the surplus arterial samples analyzed in Fig. 3h–o were procured; A.A., F.F. and S.F. were the surgeons who obtained those samples; and R.B. assisted with sample collection49. N.B.-N. ran the case–control FMD analysis17 and provided access to summary data from that study (Table 2), and A.G. assisted with data interpretation and analyses related to that study. Y.Z., E.C. and V.C. performed mouse echocardiography and ultrasound studies, and M.G.K. oversaw these analyses and analyzed the data. J.C.K. conceived of this study, supervised the experiments and research group, wrote and approved the final version of the manuscript and was the primary person who secured funding. All authors contributed to drafting, editing and/or revising the manuscript.

Corresponding author

Correspondence to Jason C. Kovacic.

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Competing interests

J.C.K. is the recipient of an Agilent Thought Leader Award (January 2022), which includes funding for research that is unrelated to the current manuscript. K.D.C. (a co-investigator in this study) is the scientific co-founder of, receives financial compensation as the chief scientific officer for and holds equity in NovoHeart LTD (a biotechnology company that focuses on using human stem cells and engineered human cardiac tissues for drug development/screening applications). The other authors declare no competing interests.

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Nature Cardiovascular Research thanks Thomas Quertermous 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 Proliferation and contraction of UBR4-kd Bj-5ta fibroblasts.

a-c, Proliferation was evaluated for untreated control Bj-5ta fibroblasts, Bj-5ta fibroblasts treated with lentiviral vector containing scrambled shRNA (Scr), and UBR4-kd Bj-5ta fibroblasts. For these 3 cell types, proliferation was evaluated under 3 conditions: (a) culture with fetal bovine serum (FBS), (b) culture without FBS (starvation), and (c) culture with TGF-β1 at 20 ng ml−1. Scale bars: 100 µm. n=3 per group. d-f, Contraction was evaluated for the identical cell groups under the same conditions, respectively: (d) culture with FBS, (e) culture without FBS (starvation), and (f) culture with TGF-β1 at 20 ng ml−1. n=4 per group. *P≤0.05; **P≤0.01; ***P≤0.001; ****P≤0.0001. All analyses were performed using one-way ANOVA and Tukey’s post hoc analysis, except for Extended Data Fig. 1a for which the data were not normally distributed and a Kruskal-Wallis test with Dunn’s post hoc analysis were used. Additional statistical information regarding the analyses used in this Figure are provided in Supplementary Table 36.

Source data

Extended Data Fig. 2 Adhesion of UBR4-kd Bj-5ta fibroblasts.

a-c, Adhesion was evaluated for Bj-5ta fibroblasts treated with lentiviral vector containing scrambled shRNA (Scr) and UBR4-kd Bj-5ta fibroblasts. For these cell types, adhesion was evaluated under 3 conditions: (a) culture with FBS, (b) culture without FBS (starvation), and (c) culture with TGF-β1 at 20 ng ml−1. As per the Cell Biolabs CBA-070 adhesion assay used, adhesion was evaluated when cells were cultured on Bovine Serum Albumin (BSA), Fibronectin (FN), Collagen I (Col I), Collagen IV (Col IV), Laminin I (LM), Fibrinogen (FG). n=3 per group. Based on the distribution and variance of each group, analyses in a-c were performed using unpaired Student’s t test except Col I in b (culture with FBS) that was performed with Mann-Whitney test. d-f, As an alternate method to assess adhesion, the number of cells floating in the supernatant of 2 ml medium (‘Counts’) was evaluated after overnight culture for untreated control Bj-5ta fibroblasts, Bj-5ta fibroblasts treated with lentiviral vector containing scrambled shRNA (Scr), and UBR4-kd Bj-5ta fibroblasts. For these 3 cell types, ‘Counts’ were evaluated under 3 conditions: (d) culture with FBS, (e) culture without FBS (starvation), and (f) culture with TGF-β1 at 20 ng ml−1. n=4 per group. Analyses in d-f performed using one-way ANOVA and Tukey’s post hoc analysis. *P≤0.05; **P≤0.01; ****P≤0.0001. Additional statistical information regarding the analyses used in this Figure are provided in Supplementary Table 36.

Source data

Extended Data Fig. 3 Apoptosis and senescence of UBR4-kd Bj-5ta fibroblasts.

a-c, Apoptosis was evaluated for untreated control Bj-5ta fibroblasts, Bj-5ta fibroblasts treated with lentiviral vector containing scrambled shRNA (Scr), and UBR4-kd Bj-5ta fibroblasts. For these 3 cell types, apoptosis was evaluated under 3 conditions: (a) culture with FBS, (b) culture without FBS (starvation), and (c) culture with TGF-β1 at 20 ng ml−1. Scale bars: 100 µm. n=3 per group. d-f, Senescence was evaluated for the identical cell groups under the same conditions, respectively: (d) culture with FBS, (e) culture without FBS (starvation), and (f) culture with TGF-β1 at 20 ng ml−1. Scale bars: 100 µm. n=3 per group. All analyses were performed using Kruskal-Wallis test with Dunn’s post hoc analysis. There were no significant differences for any of these comparisons. Additional statistical information regarding the analyses used in this Figure are provided in Supplementary Table 36.

Source data

Extended Data Fig. 4 UBR4-kd Bj-5ta fibroblasts exhibit altered production of extra-cellular matrix-related proteins.

Culture supernatants of Bj-5ta fibroblasts treated with lentiviral vector containing scrambled shRNA (Scr) and UBR4-kd Bj-5ta fibroblasts were evaluated for production of extra-cellular matrix-related proteins. After extensive washing, cells were cultured for 24 hours with serum free medium. Supernatants were collected, frozen, and processed in a single batch by mass spectrometry. In total, 85 extra-cellular matrix and extra-cellular matrix-associated proteins were identified. Shown here is total ion current (TIC) normalized quantitation of extra-cellular matrix-related proteins that showed differential abundance (P≤0.05) in supernatant (that is, production by fibroblasts) when compared between Scr and UBR4-kd Bj-5ta fibroblasts. n=5 per group. Analyses performed using unpaired Student’s t test.

Extended Data Fig. 5 UBR4 knockdown (UBR4-kd) in immortalized HASMCs including RNAseq, GO terms and hypergeometric test.

a, In vitro gene expression levels of UBR4 by qRT-PCR in untreated control HASMCs (Ctrl), control HASMCs treated with lentiviral vector containing scrambled shRNA (Scr), and HASMCs with stable knockdown of UBR4 (UBR4-kd). n=4, all groups. *P≤0.05; **P≤0.01. Analysis performed using one-way ANOVA and Tukey’s post hoc analysis. b, Volcano plot of DGE between UBR4-kd and scramble control HASMCs based on bulk RNAseq. Selected genes have been labeled including UBR4 (full results in Supplementary Table 11). Blue and purple data points, as well as UBR4 in red, represent the transcripts that were significantly different after multiple comparison testing. n=6 per group. c, Top 10 GO terms (by Bonferroni P value) for genes showing upregulated DGE when comparing UBR4-kd and scramble control HASMCs (full results in Supplementary Table 12). d, Top 10 GO terms (by Bonferroni P value) for genes showing downregulated DGE when comparing UBR4-kd and scramble control HASMCs (full results in Supplementary Table 13). e, Hypergeometric test (the specific test to evaluate if a putative key driver governs a given gene network) comparing the ‘expected’ (dark blue column) versus ‘observed’ (light blue column) number of transcripts showing altered expression levels for genes in SN-A, based on knockdown of UBR4 in HASMCs (that is, based on RNAseq data shown in Supplementary Table 11). Knockdown of UBR4 in HASMCs resulted in a substantially greater ‘observed’ number of genes with altered expression in SN-A, further validating that UBR4 exerts powerful regulatory control over the genes in this supernetwork (P = 2.51 x 10−93). See also Fig. 3g showing the same analysis performed in Bj-5ta fibroblasts. Additional statistical information regarding the analyses used in this Figure are provided in Supplementary Table 36.

Source data

Extended Data Fig. 6 Proliferation, contraction and adhesion of UBR4-kd HASMCs.

a-c, Proliferation was evaluated for untreated control HASMCs, HASMCs treated with lentiviral vector containing scrambled shRNA (Scr), and UBR4-kd HASMCs. For these 3 cell types, proliferation was evaluated under 3 conditions: (a) culture with FBS, (b) culture without FBS (starvation), and (c) culture with TGF-β1 at 20 ng ml−1. Scale bars: 50 µm. n=4 per group. Analyses performed using one-way ANOVA and Tukey’s post hoc analysis. d, Contraction assay for the same HASMC groups grown in culture with FBS. n=4 per group. Analysis performed using one-way ANOVA and Tukey’s post hoc analysis. Note that unlike Bj-5ta fibroblasts, we observed that HASMCs grown without FBS (that is, conditions of either starvation or stimulation with TGF-β1) did not contract. Further assessment revealed that, presumably due to the need for extended culture without FBS, HASMCs in the contraction assay under conditions of either starvation or stimulation with TGF-β1 were no longer viable (see Methods). Therefore, only contraction data for HASMCs with FBS is presented. e-g, Adhesion was evaluated for HASMCs treated with lentiviral vector containing scrambled shRNA (Scr) and UBR4-kd HASMCs. For these cell types, adhesion was evaluated under 3 conditions: (e) culture with FBS, (f) culture without FBS (starvation), and (g) culture with TGF-β1 at 20 ng ml−1. As per the adhesion assay used, adhesion was evaluated when cells were cultured on Bovine Serum Albumin (BSA), Fibronectin (FN), Collagen I (Col I), Collagen IV (Col IV), Laminin I (LM), Fibrinogen (FG). n=3 per group. Based on the distribution and variance of each group, analyses in e-g were performed using unpaired Student’s t test except FN in e (culture with FBS) and LM in f (culture without FBS (starvation)) that were performed with Mann-Whitney test. *P≤0.05; **P≤0.01; ***P≤0.001; ****P≤0.0001. Additional statistical information regarding the analyses used in this Figure are provided in Supplementary Table 36.

Source data

Extended Data Fig. 7 Apoptosis and senescence of UBR4-kd HASMCs.

a-c, Apoptosis was evaluated for untreated control HASMCs, HASMCs treated with lentiviral vector containing scrambled shRNA (Scr), and UBR4-kd HASMCs. For these 3 cell types, apoptosis was evaluated under 3 conditions: (a) culture with FBS, (b) culture without FBS (starvation), and (c) culture with TGF-β1 at 20 ng ml−1. Scale bars: 50 µm. n=5-8 per group for a, n=4 per group for b and n=4-5 per group for c. d-f, Senescence was evaluated for the identical cell groups under the same conditions, respectively: (d) culture with FBS, (e) culture without FBS (starvation), and (f) culture with TGF-β1 at 20 ng ml−1. Scale bars: 100 µm. n=3 per group. All analyses performed using Kruskal-Wallis test with Dunn’s post hoc analysis except for Extended Data Fig. 7a where all values were 0. There were no significant differences for any of these comparisons. Additional statistical information regarding the analyses used in this Figure are provided in Supplementary Table 36.

Source data

Extended Data Fig. 8 UBR4-kd HASMCs exhibit altered production of extra-cellular matrix-related proteins.

Culture supernatants of HASMCs treated with lentiviral vector containing scrambled shRNA (Scr) and UBR4-kd HASMCs were evaluated for production of extra-cellular matrix-related proteins. a, Volcano plot showing proteins with differential abundance in Scr versus UBR4-kd HASMCs (full results in Supplementary Table 14). b, Top 10 GO terms (based on P value) of proteins showing increased abundance in supernatant from HASMCs with UBR4-kd versus Scr control HASMCs. Note that the top 10 GO terms were all GOCC terms, and all GO terms in this Figure showed positive fold enrichment (full results in Supplementary Table 15). c, Top 10 GO terms (based on P value) of proteins showing decreased abundance in supernatant from HASMCs with UBR4-kd versus Scr control SMCs. All GO terms in this Figure showed positive fold enrichment (full results in Supplementary Table 16). n=6 per group.

Extended Data Fig. 9 Single cell RNA sequencing (scRNA-seq) of arterial tissues from female tdT-Sm22α-Ubr4KO and control mice (Sm22α-CreERT2;tdTomato;Ubr4flox/flox and Sm22α-CreERT2;tdTomato mice, respectively) showing different cell clustering features.

a, Dotplot of the top 6 marker genes of each major cell type identified in the scRNA-seq data (EC, endothelial cells; SMC, smooth muscle cells; FB, fibroblasts; Prog, progenitor cells; Mac, macrophages; DC, dendritic cells; TC, T cells; Unk, unknown) grouped by different cell type clusters. b, Composition analysis of tdT+ samples. Experimental replicates are shown separately. c, Composition analysis of tdT- samples. The scRNA-seq experiment has duplicated samples (WT_1, WT_2, KO_1, KO_2). Each sample has pooled aortic and carotid artery tissues from n=2 mice. n=8 female mice were used for the entire analysis. KO, knockout mice (that is, tdT-Sm22α-Ubr4KO mice); WT, control mice.

Extended Data Fig. 10 Single cell RNA sequencing (scRNA-seq) of arterial tissues from female tdT-Sm22α-Ubr4KO and control mice showing GO terms and differential gene expression for specific cell types and cell clusters.

a, GOBP enrichment for upregulated (left) and downregulated (right) genes in SMC_1 compared to SMC_2-6. b, GOBP enrichment for upregulated (left) and downregulated (right) genes in SMC_7 compared to SMC_2-6. c, Volcano plot showing select DGE between tdT-Sm22α-Ubr4KO versus control mice in endothelial cells (for full results see Supplementary Table 26). d, Volcano plot showing select DGE between tdT-Sm22α-Ubr4KO versus control mice in macrophages (for full results see Supplementary Table 27). e, GOBP enrichment for downregulated genes in macrophages as compared between tdT-Sm22α-Ubr4KO versus control mice.

Supplementary information

Supplementary Information

Supplementary Figs. 1–15 and supplementary table legends.

Reporting Summary

Supplementary Tables 1–36

This single Excel file contains all 36 supplementary tables.

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d’Escamard, V., Kadian-Dodov, D., Ma, L. et al. Integrative gene regulatory network analysis discloses key driver genes of fibromuscular dysplasia. Nat Cardiovasc Res 3, 1098–1122 (2024). https://doi.org/10.1038/s44161-024-00533-w

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