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An atlas of transcribed human cardiac promoters and enhancers reveals an important role of regulatory elements in heart failure

Nature Cardiovascular Research volume 2, pages 58–75 (2023)Cite this article

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Abstract

A deeper knowledge of the dynamic transcriptional activity of promoters and enhancers is needed to improve mechanistic understanding of the pathogenesis of heart failure and heart diseases. In this study, we used cap analysis of gene expression (CAGE) to identify and quantify the activity of transcribed regulatory elements (TREs) in the four cardiac chambers of 21 healthy and ten failing adult human hearts. We identified 17,668 promoters and 14,920 enhancers associated with the expression of 14,519 genes. We showed how these regulatory elements are alternatively transcribed in different heart regions, in healthy versus failing hearts and in ischemic versus non-ischemic heart failure samples. Cardiac-disease-related single-nucleotide polymorphisms (SNPs) appeared to be enriched in TREs, potentially affecting the allele-specific transcription factor binding. To conclude, our open-source heart CAGE atlas will serve the cardiovascular community in improving the understanding of the role of the cardiac gene regulatory networks in cardiovascular disease and therapy.

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Fig. 1: Atlas of transcribed promoters and enhancers of healthy and failing human hearts.
Fig. 2: Differential expression analysis between healthy and failing hearts reveals chamber-specific differences.
Fig. 3: Differences in expression between healthy atria and ventricles.
Fig. 4: Failing atria exhibit two distinct expression programs.
Fig. 5: A comparison between NICM and ICM identifies disease-specific regulatory elements and pathways.
Fig. 6: SNP calling in CAGE data reveals functional variants in promoters.
Fig. 7: Proportions of GWAS SNPs in TREs.

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Data availability

Heart CAGE data results were integrated using ZENBU (https://fantom.gsc.riken.jp/zenbu/reports/#Atlas%20of%20cardiac%20promoters%20and%20enhancers)116. Raw data are available on the NCBI GEO portal (GSE150736). All other data supporting the analyses presented in this study are provided in the manuscript and supplementary files.

Code availability

Unidirectional TRE annotation to genes was performed with https://github.com/Deviatiiarov/CAGE_peak_annotation. Differential expression analysis code can be accessed at https://github.com/Deviatiiarov/heart-CAGE-DE.

References

  1. Liu, Y. et al. RNA-seq identifies novel myocardial gene expression signatures of heart failure. Genomics 105, 83–89 (2015).

    Article  CAS  Google Scholar 

  2. Anene-Nzelu, C. G., Lee, M. C. J., Tan, W. L. W., Dashi, A. & Foo, R. S. Y. Genomic enhancers in cardiac development and disease. Nat. Rev. Cardiol. 19, 7–25 (2021).

  3. Gasperini, M., Tome, J. M. & Shendure, J. Towards a comprehensive catalogue of validated and target-linked human enhancers. Nat. Rev. Genet. 21, 292–310 (2020).

    Article  CAS  Google Scholar 

  4. Carullo, N. V. N. & Day, J. J. Genomic enhancers in brain health and disease. Genes (Basel) 10, 43 (2019).

    Article  Google Scholar 

  5. Arnold, P. R., Wells, A. D. & Li, X. C. Diversity and emerging roles of enhancer RNA in regulation of gene expression and cell fate. Front. Cell Dev. Biol. 7, 377 (2019).

    Article  Google Scholar 

  6. van der Lee, R., Correard, S. & Wasserman, W. W. Deregulated regulators: disease-causing cis variants in transcription factor genes. Trends Genet. 36, 523–539 (2020).

    Article  Google Scholar 

  7. Nepal, C. et al. Dual-initiation promoters with intertwined canonical and TCT/TOP transcription start sites diversify transcript processing. Nat. Commun. 11, 168 (2020).

    Article  CAS  Google Scholar 

  8. Nepal, C. et al. Dynamic regulation of the transcription initiation landscape at single nucleotide resolution during vertebrate embryogenesis. Genome Res. 23, 1938–1950 (2013).

    Article  CAS  Google Scholar 

  9. Sartorelli, V. & Lauberth, S. M. Enhancer RNAs are an important regulatory layer of the epigenome. Nat. Struct. Mol. Biol. 27, 521–528 (2020).

    Article  CAS  Google Scholar 

  10. Thomas, A. M. et al. Differentially expressed genes for atrial fibrillation identified by RNA sequencing from paired human left and right atrial appendages. Physiol. Genomics 51, 323–332 (2019).

    Article  CAS  Google Scholar 

  11. Yamaguchi, T. et al. Cardiac dopamine D1 receptor triggers ventricular arrhythmia in chronic heart failure. Nat. Commun. 11, 4364 (2020).

    Article  Google Scholar 

  12. Wang, Y. et al. The 3D Genome Browser: a web-based browser for visualizing 3D genome organization and long-range chromatin interactions. Genome Biol. 19, 151 (2018).

    Article  Google Scholar 

  13. 3D Genome Browser. http://3dgenome.fsm.northwestern.edu/publications.html.

  14. Wang, Q. S. et al. Leveraging supervised learning for functionally informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs. Nat. Commun. 12, 3394 (2021).

    Article  CAS  Google Scholar 

  15. Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).

    Article  CAS  Google Scholar 

  16. Yan, Q. et al. KLF9 aggravates ischemic injury in cardiomyocytes through augmenting oxidative stress. Life Sci. 233, 116641 (2019).

    Article  CAS  Google Scholar 

  17. Khan, A. et al. JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework. Nucleic Acids Res. 46, D260–D266 (2018).

    Article  CAS  Google Scholar 

  18. Roadmap Epigenomics Consortium. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

  19. Dickel, D. E. et al. Genome-wide compendium and functional assessment of in vivo heart enhancers. Nat. Commun. 7, 12923 (2016).

    Article  CAS  Google Scholar 

  20. Lee, D. et al. Human cardiac cis-regulatory elements, their cognate transcription factors, and regulatory DNA sequence variants. Genome Res. 28, 1577–1588 (2018).

    Article  CAS  Google Scholar 

  21. FANTOM Consortium and the RIKEN PMI and CLST (DGT). A promoter-level mammalian expression atlas. Nature 507, 462–470 (2014).

    Article  Google Scholar 

  22. Gacita, A. M. et al. Altered enhancer and promoter usage leads to differential gene expression in the normal and failed human heart. Circ. Heart. Fail. 13, e006926 (2020).

    Article  Google Scholar 

  23. Gilsbach, R. et al. Distinct epigenetic programs regulate cardiac myocyte development and disease in the human heart in vivo. Nat. Commun. 9, 391 (2018).

    Article  Google Scholar 

  24. van Ouwerkerk, A. F. et al. Identification of atrial fibrillation associated genes and functional non-coding variants. Nat. Commun. 10, 4755 (2019).

    Article  Google Scholar 

  25. Chen, L. et al. The landscape of isoform switches in sepsis: a multicenter cohort study. Sci. Rep. 12, 10276 (2022).

    Article  CAS  Google Scholar 

  26. Dixon, R. D. S. et al. Palladin is an actin cross-linking protein that uses immunoglobulin-like domains to bind filamentous actin. J. Biol. Chem. 283, 6222–6231 (2008).

    Article  CAS  Google Scholar 

  27. Litviňuková, M. et al. Cells of the adult human heart. Nature 588, 466–472 (2020).

    Article  Google Scholar 

  28. Jordan, E. et al. Evidence-based assessment of genes in dilated cardiomyopathy. Circulation 144, 7–19 (2021).

    Article  CAS  Google Scholar 

  29. Santulli, G. Angiopoietin-like proteins: a comprehensive look. Front. Endocrinol. (Lausanne) 5, 4 (2014).

  30. DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

    Article  CAS  Google Scholar 

  31. The GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).

    Article  Google Scholar 

  32. Abramov, S. et al. Landscape of allele-specific transcription factor binding in the human genome. Nat. Commun. 12, 2751 (2021).

    Article  CAS  Google Scholar 

  33. Liu, Z., Dong, X. & Li, Y. A genome-wide study of allele-specific expression in colorectal cancer. Front. Genet. 9, 570 (2018).

    Article  CAS  Google Scholar 

  34. Vierstra, J. et al. Global reference mapping of human transcription factor footprints. Nature 583, 729–736 (2020).

    Article  CAS  Google Scholar 

  35. ENCODE Project Consortium. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature 583, 699–710 (2020).

    Article  Google Scholar 

  36. Ernst, J. & Kellis, M. Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues. Nat. Biotechnol. 33, 364–376 (2015).

    Article  CAS  Google Scholar 

  37. Sweet, M. E. et al. Transcriptome analysis of human heart failure reveals dysregulated cell adhesion in dilated cardiomyopathy and activated immune pathways in ischemic heart failure. BMC Genomics 19, 812 (2018).

    Article  CAS  Google Scholar 

  38. Kubota, Y. et al. Cooperative interaction of angiopoietin-like proteins 1 and 2 in zebrafish vascular development. Proc. Natl Acad. Sci. USA 102, 13502–13507 (2005).

    Article  CAS  Google Scholar 

  39. Dhanabal, M., Jeffers, M., LaRochelle, W. J. & Lichenstein, H. S. Angioarrestin: a unique angiopoietin-related protein with anti-angiogenic properties. Biochem. Biophys. Res. Commun. 333, 308–315 (2005).

    Article  CAS  Google Scholar 

  40. Carbone, C. et al. Angiopoietin-like proteins in angiogenesis, inflammation and cancer. Int. J. Mol. Sci. 19, 431 (2018).

  41. Carpenito, M. et al. The central role of left atrium in heart failure. Front. Cardiovasc. Med. 8, 704762 (2021).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  43. Potter, L. R., Abbey-Hosch, S. & Dickey, D. M. Natriuretic peptides, their receptors, and cyclic guanosine monophosphate-dependent signaling functions. Endocr. Rev. 27, 47–72 (2006).

    Article  CAS  Google Scholar 

  44. Luo, X.-L. et al. Myosin light chain 2 marks differentiating ventricular cardiomyocytes derived from human embryonic stem cells. Pflugers Arch. 473, 991–1007 (2021).

    Article  CAS  Google Scholar 

  45. Barth, A. S. et al. Reprogramming of the human atrial transcriptome in permanent atrial fibrillation: expression of a ventricular-like genomic signature. Circ. Res. 96, 1022–1029 (2005).

    Article  CAS  Google Scholar 

  46. Zhang, R. et al. In vivo cardiac reprogramming contributes to zebrafish heart regeneration. Nature 498, 497–501 (2013).

    Article  CAS  Google Scholar 

  47. Yan, S. F. et al. Egr-1, a master switch coordinating upregulation of divergent gene families underlying ischemic stress. Nat. Med. 6, 1355–1361 (2000).

    Article  CAS  Google Scholar 

  48. Blouin, J.-M. et al. Identification of novel UROS mutations in a patient with congenital erythropoietic porphyria and efficient treatment by phlebotomy. Mol. Genet. Metab. Rep. 27, 100722 (2021).

    Article  Google Scholar 

  49. Fu, R., Shen, Y. & Zheng, J. Association between common genetic variants in ESR1 and stroke risk: a systematic review and meta-analysis. J. Stroke Cerebrovasc. Dis. 28, 104355 (2019).

    Article  Google Scholar 

  50. Kunnas, T. et al. ESR1 genetic variants, haplotypes and the risk of coronary heart disease and ischemic stroke in the Finnish population: a prospective follow-up study. Atherosclerosis 211, 200–202 (2010).

    Article  CAS  Google Scholar 

  51. Timón-Gómez, A., Garlich, J., Stuart, R. A., Ugalde, C. & Barrientos, A. Distinct roles of mitochondrial HIGD1A and HIGD2A in respiratory complex and supercomplex biogenesis. Cell Rep. 31, 107607 (2020).

    Article  Google Scholar 

  52. Fatima, L. A. et al. Estrogen receptor 1 (ESR1) regulates VEGFA in adipose tissue. Sci. Rep. 7, 16716 (2017).

  53. House, S. L. et al. Fibroblast growth factor 2 mediates isoproterenol-induced cardiac hypertrophy through activation of the extracellular regulated kinase. Mol. Cell. Pharmacol. 2, 143–154 (2010).

    CAS  Google Scholar 

  54. Zhao, Y. & Rahmouni, K. BBSome: a new player in hypertension and other cardiovascular risks. Hypertension 79, 303–313 (2022).

    Article  CAS  Google Scholar 

  55. Elbedour, K., Zucker, N., Zalzstein, E., Barki, Y. & Carmi, R. Cardiac abnormalities in the Bardet–Biedl syndrome: echocardiographic studies of 22 patients. Am. J. Med. Genet. 52, 164–169 (1994).

    Article  CAS  Google Scholar 

  56. Murata, M. et al. Detecting expressed genes using CAGE. Methods Mol. Biol. 1164, 67–85 (2014).

    Article  Google Scholar 

  57. Takahashi, H., Nishiyori-Sueki, H. & Carninci, P. Low quantity single strand CAGE protocol. https://doi.org/10.17504/protocols.io.bbwkipcw.

  58. Labsquare Team. FastQt 0.2.3: a quality control tool for high throughput sequence data. https://doi.org/10.5281/zenodo.824550 (2017).

  59. FASTX-Toolkit. http://hannonlab.cshl.edu/fastx_toolkit/.

  60. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Article  CAS  Google Scholar 

  61. Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).

    Article  Google Scholar 

  62. Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907–915 (2019).

    Article  CAS  Google Scholar 

  63. Haberle, V., Forrest, A. R. R., Hayashizaki, Y., Carninci, P. & Lenhard, B. CAGEr: precise TSS data retrieval and high-resolution promoterome mining for integrative analyses. Nucleic Acids Res. 43, e51 (2015).

    Article  Google Scholar 

  64. Kouno, T. et al. C1 CAGE detects transcription start sites and enhancer activity at single-cell resolution. Nat. Commun. 10, 360 (2019).

    Article  CAS  Google Scholar 

  65. Noguchi, S. et al. FANTOM5 CAGE profiles of human and mouse samples. Sci. Data 4, 170112 (2017).

    Article  CAS  Google Scholar 

  66. Decomposition-based peak identification. https://github.com/hkawaji/dpi1.

  67. TagDust. http://tometools.sourceforge.net/.

  68. Dreos, R., Ambrosini, G., Périer, R. C. & Bucher, P. The Eukaryotic Promoter Database: expansion of EPDnew and new promoter analysis tools. Nucleic Acids Res. 43, D92–D96 (2015).

    Article  CAS  Google Scholar 

  69. Abugessaisa, I. et al. RefTSS: a reference data set for human and mouse transcription start sites. J. Mol. Biol. 431, 2407–2422 (2019).

    Article  CAS  Google Scholar 

  70. Yu, G., Wang, L.-G. & He, Q.-Y. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31, 2382–2383 (2015).

    Article  CAS  Google Scholar 

  71. Stanke, M. & Morgenstern, B. AUGUSTUS: a web server for gene prediction in eukaryotes that allows user-defined constraints. Nucleic Acids Res. 33, W465–W467 (2005).

    Article  CAS  Google Scholar 

  72. Burge, C. B. Modeling dependencies in pre-mRNA splicing signals. New Compr. Biochem. 32, 129–164 (1998).

  73. O’Leary, N. A. et al. Reference Sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 44, D733–D745 (2016).

    Article  Google Scholar 

  74. Frankish, A. et al. GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res. 47, D766–D773 (2019).

    Article  CAS  Google Scholar 

  75. Finucane Lab. Data. https://www.finucanelab.org/data.

  76. Nasser, J. et al. Genome-wide enhancer maps link risk variants to disease genes. Nature 593, 238–243 (2021).

    Article  CAS  Google Scholar 

  77. Jiang, S. & Mortazavi, A. Integrating ChIP-seq with other functional genomics data. Brief. Funct. Genomics 17, 104–115 (2018).

    Article  CAS  Google Scholar 

  78. sra-tools. https://github.com/ncbi/sra-tools.

  79. Langdon, W. B. Performance of genetic programming optimised Bowtie2 on genome comparison and analytic testing (GCAT) benchmarks. BioData Min. 8, 1 (2015).

    Article  CAS  Google Scholar 

  80. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  Google Scholar 

  81. Picard. https://broadinstitute.github.io/picard/.

  82. Ramírez, F., Dündar, F., Diehl, S., Grüning, B. A. & Manke, T. deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 42, W187–W191 (2014).

    Article  Google Scholar 

  83. Gaspar, J. M. Improved peak-calling with MACS2. Preprint at https://www.biorxiv.org/content/10.1101/496521v1 (2018).

  84. Trapnell, C. et al. Erratum: Corrigendum: differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 9, 2513 (2014).

  85. Visel, A., Minovitsky, S., Dubchak, I. & Pennacchio, L. A. VISTA Enhancer Browser—a database of tissue-specific human enhancers. Nucleic Acids Res. 35, D88–D92 (2007).

    Article  CAS  Google Scholar 

  86. Khan, A. & Zhang, X. dbSUPER: a database of super-enhancers in mouse and human genome. Nucleic Acids Res. 44, D164–D171 (2016).

    Article  CAS  Google Scholar 

  87. Fishilevich, S. et al. GeneHancer: genome-wide integration of enhancers and target genes in GeneCards. Database (Oxford) 2017, bax028 (2017).

  88. deepTools: tools for exploring deep sequencing data. https://deeptools.readthedocs.io/en/develop/index.html.

  89. Siepel, A. et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 15, 1034–1050 (2005).

    Article  CAS  Google Scholar 

  90. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  92. Troukhan, M., Tatarinova, T., Bouck, J., Flavell, R. B. & Alexandrov, N. N. Genome-wide discovery of cis-elements in promoter sequences using gene expression. OMICS 13, 139–151 (2009).

    Article  CAS  Google Scholar 

  93. Triska, M., Grocutt, D., Southern, J., Murphy, D. J. & Tatarinova, T. cisExpress: motif detection in DNA sequences. Bioinformatics 29, 2203–2205 (2013).

    Article  CAS  Google Scholar 

  94. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  Google Scholar 

  95. Young, M. D., Wakefield, M. J., Smyth, G. K. & Oshlack, A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 11, R14 (2010).

    Article  Google Scholar 

  96. De Preter, K., Barriot, R., Speleman, F., Vandesompele, J. & Moreau, Y. Positional gene enrichment analysis of gene sets for high-resolution identification of overrepresented chromosomal regions. Nucleic Acids Res. 36, e43 (2008).

    Article  Google Scholar 

  97. Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation (Camb.) 2, 100141 (2021).

  98. Ge, S. X., Jung, D. & Yao, R. ShinyGO: a graphical gene-set enrichment tool for animals and plants. Bioinformatics 36, 2628–2629 (2020).

    Article  CAS  Google Scholar 

  99. Yu, G. Chapter 15. Visualization of functional enrichment result. https://yulab-smu.top/biomedical-knowledge-mining-book/enrichplot.html.

  100. Bailey, T. L. et al. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 37, W202–W208 (2009).

    Article  CAS  Google Scholar 

  101. Madsen, J. G. S. et al. Integrated analysis of motif activity and gene expression changes of transcription factors. Genome Res. 28, 243–255 (2018).

    Article  CAS  Google Scholar 

  102. Kel, A. E. MATCHTM: a tool for searching transcription factor binding sites in DNA sequences. Nucleic Acids Res. 31, 3576–3579 (2003).

    Article  CAS  Google Scholar 

  103. Barkas, N. et al. Joint analysis of heterogeneous single-cell RNA-seq dataset collections. Nat. Methods 16, 695–698 (2019).

    Article  CAS  Google Scholar 

  104. Pagoda2. https://github.com/kharchenkolab/pagoda2.

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

    Article  CAS  Google Scholar 

  106. Wang, X., Park, J., Susztak, K., Zhang, N. R. & Li, M. Bulk tissue cell type deconvolution with multi-subject single-cell expression reference. Nat. Commun. 10, 380 (2019).

    Article  CAS  Google Scholar 

  107. Egorov, A. A. et al. Svist4get: a simple visualization tool for genomic tracks from sequencing experiments. BMC Bioinformatics 20, 113 (2019).

    Article  Google Scholar 

  108. Buniello, A. et al. The NHGRI–EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47, D1005–D1012 (2019).

    Article  CAS  Google Scholar 

  109. Coetzee, S. G., Coetzee, G. A. & Hazelett, D. J. motifbreakR: an R/Bioconductor package for predicting variant effects at transcription factor binding sites. Bioinformatics 31, 3847–3849 (2015).

    CAS  Google Scholar 

  110. Harrow, J. et al. GENCODE: producing a reference annotation for ENCODE. Genome Biol. 7, S4.1–9 (2006).

    Article  Google Scholar 

  111. Sherry, S. T. et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29, 308–311 (2001).

    Article  CAS  Google Scholar 

  112. Beta negative binomial mixture model facilitates identification of allele-specific gene regulation in high-throughput sequencing data. In The Thirteenth International Multiconference (Institute of Cytology and Genetics, the Siberian Branch of the Russian Academy of Sciences, 2022).

  113. Abramov, S., Boytsov, A. & Meshcheryakov, G. autosome-ru/MixALime: MiXALime v 1.0.4. https://doi.org/10.5281/ZENODO.6977216 (2022).

  114. George, E. O. & Mudholkar, G. S. On the convolution of logistic random variables. Metrika 30, 1–13 (1983).

    Article  Google Scholar 

  115. Benjamini, Y. & Yekutieli, D. The control of the false discovery rate in multiple testing under dependency. Ann. Stat. 29, 1165–1188 (2001).

    Article  Google Scholar 

  116. Severin, J. et al. Interactive visualization and analysis of large-scale sequencing datasets using ZENBU. Nat. Biotechnol. 32, 217–219 (2014).

    Article  CAS  Google Scholar 

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Acknowledgements

This study was funded by the Leducq Foundation (RHYTHM to I.R.E. and A.G.), the National Institutes of Health (3OT2OD023848, R01HL126802 and U01HL141074 to I.R.E.), Russian Foundation for Basic Research grant 192904111 (to R. Syunyaev) and the University of La Verne Faculty Development fund (to T.V.T.). O.G. and I.V.K. were supported by the Ministry of Science and Higher Education of the Russian Federation (grant no. 075152021601).

Author information

Author notes

  1. These authors contributed equally: Ruslan M. Deviatiiarov, Anna Gams.

Authors and Affiliations

  1. Laboratory of Regulatory Genomics, Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia

    Ruslan M. Deviatiiarov, Ivan V. Kulakovskiy, Andrey Buyan & Oleg Gusev

  2. Department of Biomedical Engineering, The George Washington University, Washington, DC, USA

    Anna Gams, Roman Syunyaev, Palak Shah & Igor R. Efimov

  3. Institute of Protein Research, Russian Academy of Sciences, Pushchino, Russia

    Ivan V. Kulakovskiy, Andrey Buyan & Georgy Meshcheryakov

  4. Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia

    Ivan V. Kulakovskiy & Tatiana V. Tatarinova

  5. I.M. Sechenov First Moscow State Medical University, Moscow, Russia

    Roman Syunyaev

  6. Inova Heart and Vascular Institute, Falls Church, VA, USA

    Ramesh Singh & Palak Shah

  7. Department of Biology, University of La Verne, La Verne, CA, USA

    Tatiana V. Tatarinova

  8. Graduate School of Medicine, Juntendo University, Tokyo, Japan

    Oleg Gusev

  9. RIKEN Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan

    Oleg Gusev

  10. Endocrinology Research Center, Moscow, Russia

    Oleg Gusev

  11. Department of Biomedical Engineering, Northwestern University, Chicago, IL, USA

    Igor R. Efimov

  12. Department of Medicine, Northwestern University, Chicago, IL, USA

    Igor R. Efimov

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Contributions

A.G., R. Singh, P.S. and I.R.E. collected samples. O.G. performed sequencing. R.M.D., A.G., I.V.K., A.B., G.M. and T.V.T. conducted data analysis. R.M.D., A.G., R. Syunyaev, T.V.T., O.G., I.V.K., A.B., G.M. and I.R.E. wrote and critically revised the manuscript.

Corresponding authors

Correspondence to Tatiana V. Tatarinova, Oleg Gusev or Igor R. Efimov.

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The authors declare no competing interests.

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Nature Cardiovascular Research thanks Vincent Christoffels, Albin Sandelin 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 Pipeline overview and general statistics of clustering and classifiers.

a. CAGE reads were aligned to human genome assembly v38 with BWA (unspliced) and unmapped reads were realigned with HISAT2 (spliced reads). Alignments were converted to CTSS (CAGE transcription start sites). b. CTSS were clustered using two methods: decomposition peak identification (DPI) and bidirectional enhancer pipelines to identify unidirectional and bidirectional TREs respectively. c. TREs were submitted to the TSSClassifier as 2 kb sequences: overlap with the EPD/FANTOM/ENCODE - training set, no overlap - test set. d. All TREs were connected to proximal GENCODE transcripts. Unidirectional TREs were associated with GENCODE genes if located within ±500-bp of the gene model start site. Bidirectional TREs were associated with genes if located in the same topologically associated domain (TAD) and supported by a correlation test FDR < 0.05 or located within the same TAD and supported by Expression Modifier Score (EMS) data. e. Classified TREs were extended into 400-bp regions. If 400-bp regions of the same class overlap, they were combined into one consensus region. For each gene, unidirectional TREs with the highest expression level represents the main TSS (highest CTSS peak position), and remaining TREs comprised an alternative TSS. For bidirectional TREs, the Mid position of the consensus region with the highest expression is the main Mid, and others are alternative Mid. f. External ChIP-seq data from Gilsbach et al. and ENCODE for ChIP-seq overlap-based classifier and ATAC-seq, DNase-seq, RAMPAGE, and RNA-seq data comparisons as supportive classifiers (Supplementary Table 2). g. Diagram explaining where each type of CAGE data was used. h. Trimming and mapping statistics. i. Genomic locations of all unidirectional TREs (top panel) and bidirectional TREs (bottom panel). j. Receiver operating characteristic (ROC) (left) and a number of classified peaks (right) of CAGE peak classification with a machine learning algorithm, TSSClassifier. The classifier was trained on regions from ENCODE (PLS - promoter-like sequences, pELS/dELS - proximal/distal enhancer-like sequences), FANTOM5 (refTSS), and EPD-eukaryotic promoter database). k. ChIP-seq based classification of TREs. l. TREs overlaps with databases of human heart (Supplementary Table 2). m. Bidirectional TRE overlaps with enhancer databases. n. Dinucleotide frequency in CTSS (highest peak position in unidirectional TREs).

Extended Data Fig. 2 Comparison of TREs of this study with other databases.

a. Upset plot highlights the intersection of TREs with different databases. To note, the FANTOM5 database includes the entire set of promoter peaks, while other resources are human heart-specific. b. TREs show higher specificity of epigenetic signals in comparison to other available databases of human heart promoters and enhancers. Data for ChIP-seq (H3K4me3, H3K27ac, PolR2A), ATAC-seq, and DNase-seq were obtained from ENCODE. The Y-axis shows the fraction of tags. CpG and phastCons values for 100-way genomic alignment were obtained from UCSC. The Y-axis shows fractions of CpG coverage or phastCons scores, respectively.

Extended Data Fig. 3 Comparison of failing heart differentially expressed genes defined by CAGE and ChIP-seq data.

a. Differential affinity analysis for Gilsbach et al. ChIP-seq active marks H3K4me3, H3K27ac. The number of defined differential TREs (FDR < 0.05) enriched in failing samples is much lower than in healthy samples, which is consistent with our observation of differential expression analysis (Fig. 2b,c). b. Differentially expressed TREs of failing samples have higher coverage by ChIP-seq active marks compared to healthy samples (likelihood ratio test, multiple testing corrected with Benjamini-Hochberg). Atrial and ventricular differentially expressed TREs have similar coverage. Example genes with consistent profiles in both CAGE and ChIP-seq data are shown as boxplots (CAGE healthy (n = 76) and failing (n = 33); H3K4me3 healthy (n = 3) and failing (n = 3); H3K27ac healthy (n = 2) and failing (n = 3)). The center line of the boxplot shows median, box bounds are first and third quartiles, whiskers indicate minima and maxima, and points show outliers. c. Coverage (CPM normalized) of CAGE TRE by ChIP-seq active marks (H3K4me3, H3K27ac) from Gilsbach et al. To note, there are two samples with a relatively low numbers of reads, SRR6426245 H3K27ac and SRR6426272 H3K4me3, and they appear as outliers on the heatmaps and principal component analysis plots on panel a. RLE - relative log expression, TPM - tags per million, CPM - counts per million.

Extended Data Fig. 4 Comparison of TREs with the FANTOM5 database.

a. Overlap of unidirectional TREs and bidirectional TREs with the FANTOM5 database. b. Aggregation plots for unidirectional TREs and bidirectional TREs using different epigenetic markers and scores.

Extended Data Fig. 5 Deconvolution analysis for heart CAGE data.

a. Single-cell RNA-seq data of adult human hearts were recalculated for cell deconvolution (www.heartcellatlas.org). b. Deconvolution results of heart CAGE data. Statistical differences between cell proportions in chambers were calculated using two-sided Student’s t-test with Bonferroni correction (n = 30 for LA, n = 29 for LV, n = 21 for RA, and n = 23 for RV samples). Only significant cases are labeled (adjusted p-value < 0.05). The center line of the boxplot shows median, box bounds are first and third quartiles, whiskers indicate minima and maxima, and points show outliers. LA - left atrium, LV - left ventricle, RA - right atrium, RV - right ventricle.

Extended Data Fig. 6 Differences in cell proportions between healthy and failing samples after deconvolution analysis.

a. Atrial CAGE libraries (n = 6 libraries for each chamber). b. Ventricular CAGE libraries (n = 76 for healthy and n = 33 for failing samples). Two-sided Student’s t-test with Bonferroni correction was applied. Only significant cases are labeled (adjusted p-value < 0.05). The center line of the boxplot shows median, box bounds are first and third quartiles, whiskers indicate minima and maxima, and points show outliers.

Extended Data Fig. 7 Specific GO terms for differentially expressed genes within selected groups.

Differentially expressed genes were defined as FDR < 0.05 and |log2(FC) | > 1 and submitted to the GO over-representation test. Significant GO terms (FDR < 0.05) were selected for each group, and log2(FDR) values were used for score calculation. Each cluster color (hierarchical clustering to the right of the heatmap) is numbered (number in a circle). Pathways corresponding to each cluster number are listed. Full clustering information (numbers in circles based on hierarchical clustering) is available in Source Data Extended Data Fig. 7 (column “cluster”).

Source data

Extended Data Fig. 8 KEGG pathway analysis of differentially expressed genes within selected groups.

KEGG enrichment analysis was applied to differentially expressed genes (similar to Extended Data Fig. 7), log2(FDR) was used for score calculation and hierarchical clustering. Each cluster color (hierarchical clustering to the right of the heatmap) is numbered (number in a circle). Pathways corresponding to each cluster number are listed. Detailed clustering data are available in Source Data Extended Data Fig. 8. KEGG - Kyoto Encyclopedia of Genes and Genomes, FDR- false discovery rate.

Source data

Extended Data Fig. 9 TFBS enrichment analysis for selected comparison groups.

Sequences of differentially expressed TREs were submitted to MEME Suite 5.3.3 AME with default parameters and Jaspar 2018 CORE non-redundant vertebrate motifs. Sequences of non-differentially expressed TREs were used as background. Bonferroni corrected p-values were transferred to log scale and used for score calculation and clustering. Each cluster color (hierarchical clustering to the right of the heatmap) is numbered (number in a circle). Motifs corresponding to each cluster number are listed. Clustering data are available in Source Data Extended Data Fig. 9. TFBS- transcription factor binding site.

Source data

Extended Data Fig. 10 Genomic variants associated with heart diseases and their potential to affect the regulatory network.

a. Enriched NHGRI-EBI GWAS SNPs (top) and TFBS (bottom) in differentially expressed CAGE TREs between healthy and failing heart samples. Numbers of TREs and representative examples are labeled. More detailed terms are available in Source Data Extended Data Fig. 10. b. An example of an SNP is rs186540595 (familial hypertrophic cardiomyopathy) which affects TFBS structure. Other cases are available on the Zenbu report platform. c. Visualization of rs186540595 SNP shows its proximity to differentially expressed CTSS of TNNI3.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–6

Reporting Summary

Supplementary Table 1

Donor demographics and sample statistics.

Supplementary Table 2

Libraries obtained from ENCODE and used in the analysis.

Supplementary Table 3

Libraries obtained from Gilsbach et al.23 and used in the analysis.

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Deviatiiarov, R.M., Gams, A., Kulakovskiy, I.V. et al. An atlas of transcribed human cardiac promoters and enhancers reveals an important role of regulatory elements in heart failure. Nat Cardiovasc Res 2, 58–75 (2023). https://doi.org/10.1038/s44161-022-00182-x

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