Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Tbx5 maintains atrial identity in postnatal cardiomyocytes by regulating an atrial-specific enhancer network

Nature Cardiovascular Research volume 2, pages 881–898 (2023)Cite this article

Subjects

An Author Correction to this article was published on 31 October 2023

This article has been updated

Abstract

Understanding how the atrial and ventricular heart chambers maintain distinct identities is a prerequisite for treating chamber-specific diseases. In this study, we selectively knocked out the transcription factor Tbx5 in the atrial working myocardium to evaluate its requirement for atrial identity. Atrial Tbx5 inactivation downregulated atrial cardiomyocyte (aCM)-selective gene expression. Using concurrent single-nucleus transcriptome and open chromatin profiling, genomic accessibility differences were identified between control and Tbx5 knockout aCMs, revealing that 69% of the control-enriched ATAC regions were bound by TBX5. Genes associated with these regions were downregulated in knockout aCMs, suggesting that they function as TBX5-dependent enhancers. Comparing enhancer chromatin looping using H3K27ac HiChIP identified 510 chromatin loops sensitive to TBX5 dosage, and 74.8% of control-enriched loops contained anchors in control-enriched ATAC regions. Together, these data demonstrate that TBX5 maintains the atrial gene expression program by binding to and preserving the tissue-specific chromatin architecture of atrial enhancers.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Inactivation of Tbx5 in aCMs results in atrial remodeling.
Fig. 2: Inactivating Tbx5 alters the expression of aCM-selective genes.
Fig. 3: Concurrent scRNA-seq and scATAC-seq analysis of cell states in control and Tbx5AKO atria.
Fig. 4: TBX5 is required to promote the expression of aCM genes.
Fig. 5: Multiomics reveals a set of Tbx5-dependent CREs that promote aCM gene expression.
Fig. 6: TBX5 maintains local chromatin structure to regulate gene expression.
Fig. 7: TBX5 regulates enhancer activity, accessibility and looping at aCM-selective genes Myl7 and Bmp10.
Fig. 8: TBX5 promotes atrial identity.

Similar content being viewed by others

Data availability

High-throughput data used in this manuscript are available from the Gene Expression Omnibus, accession number GSE222970. Other datasets used were obtained from GSE215065 (ref. 3), GSE129503 (ref. 17) and GSE195905 (ref. 52). All other data supporting the findings in this study are included in the main article and associated files. Source data are provided with this manuscript.

Code availability

Analysis was performed using standard R packages. No custom analysis software was created to perform these analyses. Analyses were performed using published R packages that are cited within the text and the Methods section.

Change history

References

  1. Mulder, B. J. M. & van der Wall, E. E. Size and function of the atria. Int. J. Cardiovasc. Imaging 24, 713–716 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Chung Mina, K. et al. Atrial fibrillation. J. Am. Coll. Cardiol. 75, 1689–1713 (2020).

    Article  CAS  PubMed  Google Scholar 

  3. Cao, Y. et al. In vivo dissection of chamber selective enhancers reveals estrogen-related receptor as a regulator of ventricular cardiomyocyte identity. Circulation. 147, 881–896 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Funakoshi, S. et al. Generation of mature compact ventricular cardiomyocytes from human pluripotent stem cells. Nat. Commun. 12, 3155 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Devalla, H. D. et al. Atrial-like cardiomyocytes from human pluripotent stem cells are a robust preclinical model for assessing atrial-selective pharmacology. EMBO Mol. Med. 7, 394–410 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Pradhan, A. et al. FGF signaling enforces cardiac chamber identity in the developing ventricle. Development 144, 1328–1338 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Targoff, K. L. et al. Nkx genes are essential for maintenance of ventricular identity. Development 140, 4203–4213 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Bao, Z. Z., Bruneau, B. G., Seidman, J. G., Seidman, C. E. & Cepko, C. L. Regulation of chamber-specific gene expression in the developing heart by Irx4. Science 283, 1161–1164 (1999).

    Article  CAS  PubMed  Google Scholar 

  9. Bruneau, B. G. et al. Cardiomyopathy in Irx4-deficient mice is preceded by abnormal ventricular gene expression. Mol. Cell. Biol. 21, 1730–1736 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Koibuchi, N. & Chin, M. T. CHF1/Hey2 plays a pivotal role in left ventricular maturation through suppression of ectopic atrial gene expression. Circ. Res. 100, 850–855 (2007).

    Article  CAS  PubMed  Google Scholar 

  11. Xin, M. et al. Essential roles of the bHLH transcription factor Hrt2 in repression of atrial gene expression and maintenance of postnatal cardiac function. Proc. Natl. Acad. Sci. USA. 104, 7975–7980 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Wu, S.-P. et al. Atrial identity is determined by a COUP-TFII regulatory network. Dev. Cell 25, 417–426 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Niederreither, K. et al. Embryonic retinoic acid synthesis is essential for heart morphogenesis in the mouse. Development 128, 1019–1031 (2001).

    Article  CAS  PubMed  Google Scholar 

  14. Lee, J. H., Protze, S. I., Laksman, Z., Backx, P. H. & Keller, G. M. Human pluripotent stem cell-derived atrial and ventricular cardiomyocytes develop from distinct mesoderm populations. Cell Stem Cell 21, 179–194 (2017).

    Article  CAS  PubMed  Google Scholar 

  15. Bruneau, B. G. et al. A murine model of Holt–Oram syndrome defines roles of the T-box transcription factor Tbx5 in cardiogenesis and disease. Cell 106, 709–721 (2001).

    Article  CAS  PubMed  Google Scholar 

  16. van Ouwerkerk, A. F. et al. Patient-specific TBX5-G125R variant induces profound transcriptional deregulation and atrial dysfunction. Circulation 145, 606–619 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Nadadur, R. D. et al. Pitx2modulates a Tbx5-dependent gene regulatory network to maintain atrial rhythm. Sci. Transl. Med. 8, 354ra115 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Dai, W. et al. A calcium transport mechanism for atrial fibrillation in Tbx5-mutant mice. eLife 8, e41814 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Laforest, B. et al. Atrial fibrillation risk loci interact to modulate Ca2+-dependent atrial rhythm homeostasis. J. Clin. Invest. 129, 4937–4950 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Kathiriya, I. S. et al. Modeling human TBX5 haploinsufficiency predicts regulatory networks for congenital heart disease. Dev. Cell 56, 292–309 (2021).

    Article  CAS  PubMed  Google Scholar 

  21. Steimle, J. D. & Moskowitz, I. P. TBX5: a key regulator of heart development. In Current Topics in Developmental Biology (ed Frasch, M.) Vol. 122, 195–221 (Academic Press, 2017).

  22. Ni, L. et al. Atrial-specific gene delivery using an adeno-associated viral vector. Circ. Res. 124, 256–262 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Hulsurkar, M. M. et al. Atrial-specific LKB1 knockdown represents a novel mouse model of atrial cardiomyopathy with spontaneous atrial fibrillation. Circulation 144, 909–912 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Horsthuis, T. et al. Distinct regulation of developmental and heart disease-induced atrial natriuretic factor expression by two separate distal sequences. Circ. Res. 102, 849–859 (2008).

    Article  CAS  PubMed  Google Scholar 

  25. Lu, F. et al. CMYA5 establishes cardiac dyad architecture and positioning. Nat. Commun. 13, 2185 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Takeuchi, J. K. et al. Chromatin remodelling complex dosage modulates transcription factor function in heart development. Nat. Commun. 2, 187 (2011).

    Article  PubMed  Google Scholar 

  27. Georges, R., Nemer, G., Morin, M., Lefebvre, C. & Nemer, M. Distinct expression and function of alternatively spliced Tbx5 isoforms in cell growth and differentiation. Mol. Cell. Biol. 28, 4052–4067 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Stuart, T., Srivastava, A., Madad, S., Lareau, C. A. & Satija, R. Single-cell chromatin state analysis with Signac. Nat. Methods 18, 1333–1341 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Yang, X. H. et al. Transcription-factor-dependent enhancer transcription defines a gene regulatory network for cardiac rhythm. Elife 6, e31683 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Arnolds, D. E. et al. TBX5 drives Scn5aexpression to regulate cardiac conduction system function. J. Clin. Invest. 122, 2509–2518 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. van Gorp, P. R. R. et al. Sbk2, a newly discovered atrium-enriched regulator of sarcomere integrity. Circ. Res. 131, 24–41 (2022).

    Article  PubMed  Google Scholar 

  35. Ackerman, M. J. & Mohler, P. J. Defining a new paradigm for human arrhythmia syndromes: phenotypic manifestations of gene mutations in ion channel- and transporter-associated proteins. Circ. Res. 107, 457–465 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Bosada, F. M. et al. An atrial fibrillation-associated regulatory region modulates cardiac Tbx5 levels and arrhythmia susceptibility. eLife 12, e80317 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Hnisz, D. et al. Super-enhancers in the control of cell identity and disease. Cell 155, 934–947 (2013).

    Article  CAS  PubMed  Google Scholar 

  38. Leblanc, F. J. A. et al. Transcriptomic profiling of canine atrial fibrillation models after one week of sustained arrhythmia. Circ. Arrhythm. Electrophysiol. 14, e009887 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Ma, S. et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell 183, 1103–1116 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Nakajima, H. et al. Atrial but not ventricular fibrosis in mice expressing a mutant transforming growth factor-β1 transgene in the heart. Circ. Res. 86, 571–579 (2000).

    Article  CAS  PubMed  Google Scholar 

  41. Cui, M. et al. Dynamic transcriptional responses to injury of regenerative and non-regenerative cardiomyocytes revealed by single-nucleus RNA sequencing. Dev. Cell 53, 102–116 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Roselli, C. et al. Multi-ethnic genome-wide association study for atrial fibrillation. Nat. Genet. 50, 1225–1233 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Schoenfelder, S. et al. The pluripotent regulatory circuitry connecting promoters to their long-range interacting elements. Genome Res. 25, 582–597 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Mumbach, M. R. et al. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat. Methods 13, 919–922 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Servant, N. et al. HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol. 16, 259 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Lareau, C. A. & Aryee, M. J. hichipper: a preprocessing pipeline for calling DNA loops from HiChIP data. Nat. Methods 15, 155–156 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Durand, N. C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst 3, 95–98 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Man, J. C. K. et al. Genetic dissection of a super enhancer controlling the Nppa-Nppbcluster in the heart. Circ. Res. 128, 115–129 (2021).

    Article  CAS  PubMed  Google Scholar 

  49. Chen, H. et al. BMP10 is essential for maintaining cardiac growth during murine cardiogenesis. Development 131, 2219–2231 (2004).

    Article  CAS  PubMed  Google Scholar 

  50. Xia, B. et al. Machine learning uncovers cell identity regulator by histone code. Nat. Commun. 11, 2696 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Shim, W. J. et al. Conserved epigenetic regulatory logic infers genes governing cell identity. Cell Syst 11, 625–639 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Zhou, P. et al. Dynamic changes in P300 enhancers and enhancer-promoter contacts control mouse cardiomyocyte maturation. Dev. Cell 58, 898–914 (2023).

    Article  CAS  PubMed  Google Scholar 

  53. Yuan, S., Norgard, R. J. & Stanger, B. Z. Cellular plasticity in cancer. Cancer Discov. 9, 837–851 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. LeBleu, V. S. & Neilson, E. G. Origin and functional heterogeneity of fibroblasts. FASEB J. 34, 3519–3536 (2020).

    Article  CAS  PubMed  Google Scholar 

  55. Lickert, H. et al. Baf60c is essential for function of BAF chromatin remodelling complexes in heart development. Nature 432, 107–112 (2004).

    Article  CAS  PubMed  Google Scholar 

  56. Robbe, Z. L. et al. CHD4 is recruited by GATA4 and NKX2-5 to repress noncardiac gene programs in the developing heart. Genes Dev. 36, 468–482 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Panigrahi, A. & O’Malley, B. W. Mechanisms of enhancer action: the known and the unknown. Genome Biol. 22, 108 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Sinner, M. F. et al. Integrating genetic, transcriptional, and functional analyses to identify 5 novel genes for atrial fibrillation. Circulation 130, 1225–1235 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Fu, J.-D. D. et al. Direct reprogramming of human fibroblasts toward a cardiomyocyte-like state. Stem Cell Rep. 1, 235–247 (2013).

    Article  CAS  Google Scholar 

  60. Holm, H. et al. Several common variants modulate heart rate, PR interval and QRS duration. Nat. Genet. 42, 117–122 (2010).

    Article  CAS  PubMed  Google Scholar 

  61. Hoesl, E. et al. Tamoxifen-inducible gene deletion in the cardiac conduction system. J. Mol. Cell. Cardiol. 45, 62–69 (2008).

    Article  CAS  PubMed  Google Scholar 

  62. Muzumdar, M. D., Tasic, B., Miyamichi, K., Li, L. & Luo, L. A global double-fluorescent Cre reporter mouse. Genesis 45, 593–605 (2007).

    Article  CAS  PubMed  Google Scholar 

  63. Peron, S. P., Freeman, J., Iyer, V., Guo, C. & Svoboda, K. A cellular resolution map of barrel cortex activity during tactile behavior. Neuron 86, 783–799 (2015).

    Article  CAS  PubMed  Google Scholar 

  64. Agah, R. et al. Gene recombination in postmitotic cells. Targeted expression of Cre recombinase provokes cardiac-restricted, site-specific rearrangement in adult ventricular muscle in vivo. J. Clin. Invest. 100, 169–179 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Wang, S., Guo, Y. & Pu, W. T. AAV gene transfer to the heart. Methods Mol. Biol. 2158, 269–280 (2021).

    Article  CAS  PubMed  Google Scholar 

  66. Jiang, J., Wakimoto, H., Seidman, J. G. & Seidman, C. E. Allele-specific silencing of mutant Myh6transcripts in mice suppresses hypertrophic cardiomyopathy. Science 342, 111–114 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Nadelmann, E. R. et al. Isolation of nuclei from mammalian cells and tissues for single-nucleus molecular profiling. Curr Protoc 1, e132 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. McGinnis, C. S., Murrow, L. M. & Gartner, Z. J. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 8, 329–337(2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA. 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14, 128 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  72. McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  74. Cui, M. & Olson, E. N. Protocol for single-nucleus transcriptomics of diploid and tetraploid cardiomyocytes in murine hearts. STAR Protoc 1, 100049 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Phanstiel, D. H., Boyle, A. P., Heidari, N. & Snyder, M. P. Mango: a bias-correcting ChIA-PET analysis pipeline. Bioinformatics 31, 3092–3098 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Lareau, C. A. & Aryee, M. J. diffloop: a computational framework for identifying and analyzing differential DNA loops from sequencing data. Bioinformatics 34, 672–674 (2018).

    Article  CAS  PubMed  Google Scholar 

  77. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Song, F., Xu, J., Dixon, J. & Yue, F. Analysis of Hi-C data for discovery of structural variations in cancer. In Hi-C Data Analysis: Methods and Protocols (eds Bicciato, S. & Ferrari, F.) 143–161 (Springer, 2022).

  79. Durand, N. C. et al. Juicebox provides a visualization system for Hi-C contact maps with unlimited zoom. Cell Syst. 3, 99–101 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Chen, K. et al. DANPOS: dynamic analysis of nucleosome position and occupancy by sequencing. Genome Res. 23, 341–351 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

M.E.S. and M.T. were supported by T32HL007572 and F32 F32HL163877. W.T.P. was supported by R01HL156503. Y.C., F.L. and P.W. were supported by AHA Postdoctoral Fellowships. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Mason E. Sweat, Yangpo Cao.

Authors and Affiliations

  1. Department of Cardiology, Boston Children’s Hospital, Boston, MA, USA

    Mason E. Sweat, Yangpo Cao, Xiaoran Zhang, Arulsamy Kulandaisamy, Fujian Lu, Erin M. Keating, Brynn N. Akerberg, Qing Ma, Lauren D. Hill, Mi Kyoung Song, Michael A. Trembley, Peizhe Wang, Matteo Gianeselli, Maksymilian Prondzynski, Raul H. Bortolin, Vassilios J. Bezzerides, Kaifu Chen & William T. Pu

  2. Department of Pharmacology, School of Medicine, Southern University of Science and Technology, Shenzhen, China

    Yangpo Cao

  3. Department of Pediatrics, Pathology, and Human Genetics, University of Chicago, Chicago, IL, USA

    Ozanna Burnicka-Turek, Carlos Perez-Cervantes & Ivan P. Moskowitz

  4. Department of Genetics, Harvard Medical School, Boston, MA, USA

    Hiroko Wakimoto, Joshua M. Gorham, Jonathan G. Seidman & Christine E. Seidman

  5. Department of Pediatrics, Seoul National University College of Medicine, Seoul, Korea

    Mi Kyoung Song

Authors
  1. Mason E. Sweat
    View author publications

    Search author on:PubMed Google Scholar

  2. Yangpo Cao
    View author publications

    Search author on:PubMed Google Scholar

  3. Xiaoran Zhang
    View author publications

    Search author on:PubMed Google Scholar

  4. Ozanna Burnicka-Turek
    View author publications

    Search author on:PubMed Google Scholar

  5. Carlos Perez-Cervantes
    View author publications

    Search author on:PubMed Google Scholar

  6. Arulsamy Kulandaisamy
    View author publications

    Search author on:PubMed Google Scholar

  7. Fujian Lu
    View author publications

    Search author on:PubMed Google Scholar

  8. Erin M. Keating
    View author publications

    Search author on:PubMed Google Scholar

  9. Brynn N. Akerberg
    View author publications

    Search author on:PubMed Google Scholar

  10. Qing Ma
    View author publications

    Search author on:PubMed Google Scholar

  11. Hiroko Wakimoto
    View author publications

    Search author on:PubMed Google Scholar

  12. Joshua M. Gorham
    View author publications

    Search author on:PubMed Google Scholar

  13. Lauren D. Hill
    View author publications

    Search author on:PubMed Google Scholar

  14. Mi Kyoung Song
    View author publications

    Search author on:PubMed Google Scholar

  15. Michael A. Trembley
    View author publications

    Search author on:PubMed Google Scholar

  16. Peizhe Wang
    View author publications

    Search author on:PubMed Google Scholar

  17. Matteo Gianeselli
    View author publications

    Search author on:PubMed Google Scholar

  18. Maksymilian Prondzynski
    View author publications

    Search author on:PubMed Google Scholar

  19. Raul H. Bortolin
    View author publications

    Search author on:PubMed Google Scholar

  20. Vassilios J. Bezzerides
    View author publications

    Search author on:PubMed Google Scholar

  21. Kaifu Chen
    View author publications

    Search author on:PubMed Google Scholar

  22. Jonathan G. Seidman
    View author publications

    Search author on:PubMed Google Scholar

  23. Christine E. Seidman
    View author publications

    Search author on:PubMed Google Scholar

  24. Ivan P. Moskowitz
    View author publications

    Search author on:PubMed Google Scholar

  25. William T. Pu
    View author publications

    Search author on:PubMed Google Scholar

Contributions

M.E.S. and Y.C. contributed equally to this study. M.E.S. and Y.C. conceived of the study; designed and performed the experiments; and analyzed the data. X.Z., C.P.-C. and K.A. contributed to the data analysis. O.B.-T., B.N.A., Q.M., E.K., H.W., J.M.G., L.H., M.K.S., M.A.T., P.W., F.L., M.G., M.P. and R.H.B. contributed data, reagents and analyses. V.B., K.C., J.G.S., C.E.S., I.P.M. and W.T.P. oversaw the project and provided resources. M.E.S. and W.T.P. wrote the manuscript, with input from Y.C. and the other authors.

Corresponding author

Correspondence to William T. Pu.

Ethics declarations

Competing interests

The authors have no competing interests to declare.

Peer review

Peer review information

Nature Cardiovascular Research thanks Vincent Christoffels and the other, anonymous, reviewer(s), for their contribution to the peer review of this work. Primary Handling Editor: Vesna Todorovic, in collaboration with the Nature Cardiovascular Research team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended Data

Extended Data Fig. 1 Characterization of the cardiac expression domain of AAV9:Nppa-EGFP.

a-b, Hcn4CreERT2/+; Rosa26LSL-Tomato pups were injected with 2 X 1011 viral genomes per gram bodyweight (VG/g) AAV9:Nppa-EGFP at P8 and injected with tamoxifen at P21 and P22 to activate tomato expression in the conduction system including the sinoatrial and atrioventricular nodes. Hearts were harvested in PBS and brightfield, GFP, and Tomato fluorescent images were acquired. Mutually exclusive GFP and tomato signals were observed in the atrium and the SA node, respectively. A similar result was observed with administration at P2. c, Hearts were sectioned and stained with WGA. Tomato signal marked the sinoatrial node (SAN) and atrioventricular node (AVN) but not the working myocardium. GFP was restricted to the working myocardium. A similar staining pattern was observed in 4 other littermates.

Extended Data Fig. 2 Phenotypic characterization of Tbx5AKO mice.

a, Trichrome staining of Tbx5Flox/Flox and Tbx5AKO hearts. b, RT-qPCR quantification of Postn (periostin) and Col1a1 (collagen type 1 alpha 1 chain), two indicators of fibrosis (n = 3 control and 4 KO mice). Unpaired two-sided t-test. Error bars represent mean values +/-SEM. c-d, Immunostaining for sarcomeric α-actinin (SAA) or FSD2, markers of the Z line and the junctional Sarcoplasmic reticulum, respectively, in the left atrium of the indicated genotypes. Localization of both proteins is disrupted in Tbx5AKO atria. e, Pattern of SAA signal intensity. SAA intensity along the long axis of cardiomyocytes demonstrated a periodic signal in control, consistent with regular position of sarcomere Z-lines, and loss of periodicity in Tbx5AKO. f, Preserved ventricular function of Tbx5AKO mice. Tbx5Flox/Flox mice were treated with AAV:Nppa-EGFP (control) or AAV:Nppa-Cre (Tbx5AKO) at P2. Echocardiography was performed at P20. LVID;d, left ventricular internal diameter at end diastole. EF, ejection fraction. n = 5 control and 4 KO mice. Graphs show mean ± SEM. g, Surface EKG recordings. Orange and black bars highlight successive RR intervals. h, Poincaré plots. The RR interval of greater than 1500 beats on EKG recordings is plotted versus the RR interval of the subsequent beat (RR[ + 1]). to visualize the dispersion of interbeat intervals in Tbx5AKO, consistent with atrial fibrillation. i, Standard deviation of the RR interval, a measure of heart rate irregularity, was calculated for at least 1500 beats for each group at P21. n = 4 control and 9 KO mice. Unpaired two-sided t-test: ***, P = 0.005. Graphs show mean ± SEM. j, Time course of heart rate irregularity. Serial EKGs were acquired from control or Tbx5AKO mice at the indicated time points. SDRR was measured and compared between groups at each time point using a two-way ANOVA with Sidek’s multiple comparison test. **, P < 0.01. ***, P < 0.001. For P14 and P17 timepoints, n = 3 control and 3 KO mice. For P8 and P21 timepoints, n = 5 control and 8 KO mice. Graphs show mean ± SEM. k, Simultaneous intracardiac and surface ECG recordings demonstrate normal synchronous atrial-ventricular rhythm in Tbx5Flox/+ mice injected with AAV-Nppa-Cre. l, In contrast, animals with complete atrial ablation of Tbx5 (AAV-Nppa-Cre + Tbx5flox/flox) demonstratee nearly continuous low-amplitude atrial activity and irregularly irregular ventricular response consistent with atrial fibrillation.

Extended Data Fig. 3 Tbx5 overexpression atrializes ventricular myocytes.

a, Strategy to generate TBX5-OE ventricles. A full length Tbx5 cDNA downstream of the ubiquitous CAG promoter was activated by the cardiomyocyte specific Myh6-Cre transgene. b-c, Volcano plot comparing the change in gene expression of mouse left ventricle overexpressing Tbx5 compared to control LV. aCM genes are marked in red in (a) and vCM genes are denoted in blue in (b). b-c, Wald’s test followed by Benjamini Hochberg correction. d-e, Fisher’s exact test (two tailed) was performed to determine if changes in chamber selective gene expression downstream of Tbx5 overexpression were significant. f-h, Staining for MYL4, MYL7, and MYL2 in atria and ventricles of control and TBX5-OE hearts. Staining for each marker was performed using 3 control hearts and 2 TBX5-OE hearts, and representative images are shown.

Extended Data Fig. 4 Single cell dataset metrics.

a, Parameters from single nucleus datasets. Mean values per nucleus: nCount_ATAC, number of ATAC fragments; nFeature_ATAC, number of ATAC peaks with at least one read; nCount_RNA, number of RNA fragments; and nFeature_RNA, number of genes. b, Transcription start site (TSS) aggregation plots for the scATAC multiome datasets showing the expected enrichment of ATAC fragments. c, WNN UMAP of the dataset split by original sample. Each of the KO and control replicates have a high degree of overlap, demonstrating high reproducibility of cell state changes in Tbx5AKO atria.

Extended Data Fig. 5 Differentially expressed genes between myocyte clusters.

a, Volcano plot of differentially expressed genes (DEGs) between early and late pseudotime clusters in the control trajectory (Myocyte_4 vs. Myocyte_1, left) and the KO trajectory (Myocyte_5 vs. Myocyte_6; right). Wilcoxon rank sum test, Bonferroni correction. b. MA plot of RNA-seq experiment comparing P0 and P28 aCMs. c-d. Volcano plots shown in (a) overlayed with genes enriched in P28 and P0 aCMs. Wilcoxon rank sum test, Bonferroni correction. e. The proportion of DEGs from comparisons in (a) that overlap genes selectively expressed in aCMs at P0 and P28. We did not observe enrichment of P0 or P28 selective aCM genes in early or late pseudotime clusters, respectively. This suggested that pseudotime trajectories did not correspond to chronological time. f-g. GO biological process terms enriched for DEGs for the comparisons shown in (a). The top 10 terms enriched by genes upregulated in the indicated cluster are shown. Functional terms related to cardiomyocyte function were enriched in the late pseudotime clusters, Myo_1 (control) and Myo_6 (AKO). Two-tailed Fisher’s exact test, Bonferroni correction. h, WNN UMAP plot colored by a ‘functional cardiac gene’ index, which was calculated based on the aggregate expression of the six indicated genes, which are required for the efficient pumping function of aCMs.

Extended Data Fig. 6 A comparison of TBX5 RNA-seq datasets.

We compared snRNAseq data from Tbx5AKO aCMs to three other bulk RNA-seq datasets involving Tbx5 gain- or loss- of function. Control_aCM indicates Myocyte_1 and KO_aCM indicates Myocyte_6. a, c, e: Scatter plot of fold-change in Tbx5AKO aCMs compared to the other three datasets. Left, points are colored by significance in each dataset (Padj < 0.05). Right, genes with significant differential expression in both datasets are colored by chamber selectivity. Pie charts summarize the proportion of each class of genes within each quadrant. b, d, f: Enrichment of aCM or vCM genes in the indicated quadrants of the scatter plots. Fisher’s exact test, two-tailed. a-b. Comparison to LA tissue with ubiquitous, adult-induced inactivation of Tbx5 (Tbx5iKO; Nadadur et al., 2016). c-d. Comparison to LA tissue with mild Tbx5 upregulation due to deletion of an intronic regulatory element (Tbx5Re(int)KO; Bosada et al., 2023). e-f. Comparison to LV tissue with Tbx5 overexpression in cardiomyocytes (Tbx5-OE; this study). g. Comparison of changes in expression of aCM- and vCM-selective genes across all four datasets. Fold-change was calculated between condition with higher Tbx5 (numerator) to condition with lower Tbx5 (denominator). Genes were ordered by ascending Log2(WT/TBX5iKO). Genes not detected (ND) for a given experiment are colored white. Normalized enrichment score (NES) and false discovery rate (FDR) values from GSEA using the aCM-selective or vCM-selective gene lists are shown. Negative NES indicates enrichment in the genes upregulated in the lower Tbx5 condition, whereas positive NES indicates enrichment in the genes upregulated in the higher Tbx5 condition.

Extended Data Fig. 7 Characterization of myocyte differentially accessible regions.

Heatmap (a) shows the patterns of differential accessibility between myocyte clusters. The rows contain the union of regions with differential accessibility in the four pairwise comparisons shown in Fig. 5a. ATAC signal in each region is shown for myocyte clusters 1 (control_aCM cluster), 4, 6 (KO_aCM cluster), and 5. The regions are grouped (groups a-f) by their pattern of accessibility change in the four pairwise comparisons. Please also refer to Supp. Table 4. Arrows denote significant enrichment in one cluster compared to another. These six groups fit two predominant patterns: those with and those without predominant accessibility in the control aCM cluster (Myocyte_1). Most regions with predominant accessibility in the control aCM cluster were occupied by TBX5 and had GO terms related to cardiac cell development or striated muscle contraction (b). In contrast, a minority of regions without predominant accessibility in the control aCM cluster were occupied by TBX5 and had GO terms that were atypical for cardiomyocytes (c). Groups with less than 200 regions are not shown in the heatmap.

Extended Data Fig. 8 Proximity and predicted gene linkages demonstrate the regulation of the atrial GRN by control peaks.

a, The different types of association of a genomic region with a gene: (1) Linkage (L). Region-to-gene linkages are predicted based on co-variance of accessibility and expression on a nucleus-by-nucleus basis in the multiome data. (2) Proximity (P). Region-to-gene relationships are inferred by proximity of the region to the gene’s transcriptional start site. (3) Proximity and linkage (P + L). A region-to-gene association can be supported by both proximity and linkage. b, Genes were associated with control and KO regions by linkage (n = 2 control and 2 KO multiome biological replicates). Ratio of gene expression between the control and KO aCM clusters was plotted and compared between groups by the two-sided Mann-Whitney test. P = 9.1E-48. c, Association of control and KO ATAC regions with aCM and vCM genes. Associations were made based on proximity, linkage, or both. d, aCM and vCM genes were interrogated for associations with control and KO ATAC regions by proximity, linkage, or both.

Extended Data Fig. 9 Motif analysis of control and KO ATAC regions.

a, Aggregation plots for H3K27Ac at control and KO regions. b, Heatmaps of the H3K27Ac signal at control and KO peaks. c, Top-enriched motifs identified in control regions. The TBOX motif was the most enriched, followed by MEF2. An extended table of non-redundant motifs showing the top 21 most enriched motifs in control regions. d, Transcription factor footprinting analysis demonstrates footprints at TBOX and MEF2 motifs in control clusters (Myocyte_1 and Myocyte_4) compared to KO clusters (Myocyte_5 and Myocyte_6). e, Top-enriched motifs identified in KO regions and extended table of the top non-redundant motifs. f. Occupancy of control and KO ATAC regions by cardiac TFs in aCMs. Occupancy data is from GEO GSE2150653.

Extended Data Fig. 10 Chromatin loops link TBX5 dependent enhancers with atrial genes.

a, Contact maps of Myl7 or Bmp10. Black boxed regions are loops called in each sample and blue boxed regions mark differential loops that are significantly stronger in control samples. b, Genes near control anchors grouped by adjacency to aCM-selective, vCM-selective, or non-chamber selective expression. Control anchors were present near 62 aCM-selective genes and of these, 46 were expressed at greater levels in control Myocyte_1 compared to KO Myocyte_6. Notable genes from previous figures include Nppa, Bmp10, Sbk2 and Myl7. 38 non-chamber selective genes were also upregulated in control samples and linked to enhancers by TBX5-dependent looping. These included Gja1 and Tead1. Only 5 vCM-selective genes were found near control anchors. c, 50 genes neighbored KO anchors. These genes included 5 aCM-selective genes and 3 vCM-selective genes. Most of the differentially expressed genes near KO anchors were more highly expressed in Myocyte_6 (KO) compared to Myocyte_1 (Ctrl).

Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Tables 1–5 and 7.

Supplementary Table

diffloop HiChIP results.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 1

Uncropped western gels.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 7

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sweat, M.E., Cao, Y., Zhang, X. et al. Tbx5 maintains atrial identity in postnatal cardiomyocytes by regulating an atrial-specific enhancer network. Nat Cardiovasc Res 2, 881–898 (2023). https://doi.org/10.1038/s44161-023-00334-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s44161-023-00334-7

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing