Transcriptional enhancers function as docking platforms for combinations of transcription factors (TFs) to control gene expression. How enhancer sequences determine nucleosome occupancy, TF recruitment and transcriptional activation in vivo remains unclear. Using ATAC–seq across a panel of Drosophila inbred strains, we found that SNPs affecting binding sites of the TF Grainy head (Grh) causally determine the accessibility of epithelial enhancers. We show that deletion and ectopic expression of Grh cause loss and gain of DNA accessibility, respectively. However, although Grh binding is necessary for enhancer accessibility, it is insufficient to activate enhancers. Finally, we show that human Grh homologs—GRHL1, GRHL2 and GRHL3—function similarly. We conclude that Grh binding is necessary and sufficient for the opening of epithelial enhancers but not for their activation. Our data support a model positing that complex spatiotemporal expression patterns are controlled by regulatory hierarchies in which pioneer factors, such as Grh, establish tissue-specific accessible chromatin landscapes upon which other factors can act.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Additional information

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


  1. 1.

    Bernstein, B. E. et al. The NIH Roadmap Epigenomics Mapping Consortium. Nat. Biotechnol. 28, 1045–1048 (2010).

  2. 2.

    ENCODE Project Consortium. The ENCODE (ENCyclopedia Of DNA Elements) Project. Science 306, 636–640 (2004).

  3. 3.

    Yue, F. et al. A comparative encyclopedia of DNA elements in the mouse genome. Nature 515, 355–364 (2014).

  4. 4.

    Robertson, G. et al. Genome-wide profiles of STAT1–DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nat. Methods 4, 651–657 (2007).

  5. 5.

    Song, L. & Crawford, G. E. DNase-seq: a high-resolution technique for mapping active gene regulatory elements across the genome from mammalian cells. Cold Spring Harb. Protoc. 2010, pdb.prot5384 (2010).

  6. 6.

    Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

  7. 7.

    Li, Y. & Tollefsbol, T. O. DNA methylation detection: bisulfite genomic sequencing analysis. Methods Mol. Biol. 791, 11–21 (2011).

  8. 8.

    Davidson, E. H. Emerging properties of animal gene regulatory networks. Nature 468, 911–920 (2010).

  9. 9.

    Spitz, F. & Furlong, E. E. M. Transcription factors: from enhancer binding to developmental control. Nat. Rev. Genet. 13, 613–626 (2012).

  10. 10.

    Arnold, C. D. et al. Genome-wide quantitative enhancer activity maps identified by STARR-seq. Science 339, 1074–1077 (2013).

  11. 11.

    Melnikov, A. et al. Systematic dissection and optimization of inducible enhancers in human cells using a massively parallel reporter assay. Nat. Biotechnol. 30, 271–277 (2012).

  12. 12.

    Patwardhan, R. P. et al. Massively parallel functional dissection of mammalian enhancers in vivo. Nat. Biotechnol. 30, 265–270 (2012).

  13. 13.

    Kvon, E. Z. et al. Genome-scale functional characterization of Drosophila developmental enhancers in vivo. Nature 512, 91–95 (2014).

  14. 14.

    Pfeiffer, B. D. et al. Tools for neuroanatomy and neurogenetics in Drosophila. Proc. Natl. Acad. Sci. USA 105, 9715–9720 (2008).

  15. 15.

    Arnosti, D. N. & Kulkarni, M. M. Transcriptional enhancers: intelligent enhanceosomes or flexible billboards? J. Cell. Biochem. 94, 890–898 (2005).

  16. 16.

    Reiter, F., Wienerroither, S. & Stark, A. Combinatorial function of transcription factors and cofactors. Curr. Opin. Genet. Dev. 43, 73–81 (2017).

  17. 17.

    Shlyueva, D., Stampfel, G. & Stark, A. Transcriptional enhancers: from properties to genome-wide predictions. Nat. Rev. Genet. 15, 272–286 (2014).

  18. 18.

    Soufi, A. et al. Pioneer transcription factors target partial DNA motifs on nucleosomes to initiate reprogramming. Cell 161, 555–568 (2015).

  19. 19.

    Iwafuchi-Doi, M. & Zaret, K. S. Pioneer transcription factors in cell reprogramming. Genes Dev. 28, 2679–2692 (2014).

  20. 20.

    Zaret, K. S. & Carroll, J. S. Pioneer transcription factors: establishing competence for gene expression. Genes Dev. 25, 2227–2241 (2011).

  21. 21.

    Barozzi, I. et al. Co-regulation of transcription factor binding and nucleosome occupancy through DNA features of mammalian enhancers. Mol. Cell 54, 844–857 (2014).

  22. 22.

    Younger, S. T. & Rinn, J. L. p53 regulates enhancer accessibility and activity in response to DNA damage. Nucleic Acids Res. 45, 9889–9900 (2017).

  23. 23.

    Zhang, S. & Cui, W. SOX2, a key factor in the regulation of pluripotency and neural differentiation. World J. Stem Cells 6, 305–311 (2014).

  24. 24.

    Verfaillie, A. et al. Multiplex enhancer–reporter assays uncover unsophisticated TP53 enhancer logic. Genome Res. 26, 882–895 (2016).

  25. 25.

    Boyer, L. A. et al. Core transcriptional regulatory circuitry in human embryonic stem cells. Cell 122, 947–956 (2005).

  26. 26.

    Liang, H.-L. et al. The zinc-finger protein Zelda is a key activator of the early zygotic genome in Drosophila. Nature 456, 400–403 (2008).

  27. 27.

    Foo, S. M. et al. Zelda potentiates morphogen activity by increasing chromatin accessibility. Curr. Biol. 24, 1341–1346 (2014).

  28. 28.

    Mackay, T. F. C. et al. The Drosophila melanogaster Genetic Reference Panel. Nature 482, 173–178 (2012).

  29. 29.

    Huang, W. et al. Natural variation in genome architecture among 205 Drosophila melanogaster Genetic Reference Panel lines. Genome Res. 24, 1193–1208 (2014).

  30. 30.

    Chen, X., Rahman, R., Guo, F. & Rosbash, M. Genome-wide identification of neuronal-activity-regulated genes in Drosophila. eLife 5, e19942 (2016).

  31. 31.

    Degner, J. F. et al. DNase I sensitivity QTLs are a major determinant of human expression variation. Nature 482, 390–394 (2012).

  32. 32.

    Venkatesan, K., McManus, H. R., Mello, C. C., Smith, T. F. & Hansen, U. Functional conservation between members of an ancient duplicated transcription factor family, LSF (grainy head). Nucleic Acids Res. 31, 4304–4316 (2003).

  33. 33.

    Paré, A., Kim, M., Juarez, M. T., Brody, S. & McGinnis, W. The functions of grainy-head-like proteins in animals and fungi and the evolution of apical extracellular barriers. PLoS One 7, e36254 (2012).

  34. 34.

    Narasimha, M., Uv, A., Krejci, A., Brown, N. H. & Bray, S. J. Grainy head promotes expression of septate junction proteins and influences epithelial morphogenesis. J. Cell Sci. 121, 747–752 (2008).

  35. 35.

    Nevil, M., Bondra, E. R., Schulz, K. N., Kaplan, T. & Harrison, M. M. Stable binding of the conserved transcription factor grainy head to its target genes throughout Drosophila melanogaster development. Genetics 205, 605–620 (2017).

  36. 36.

    Varma, S. et al. The transcription factors Grainyhead-like 2 and NK2-homeobox 1 form a regulatory loop that coordinates lung epithelial cell morphogenesis and differentiation. J. Biol. Chem. 287, 37282–37295 (2012).

  37. 37.

    Lyne, R. et al. FlyMine: an integrated database for Drosophila and Anopheles genomics. Genome Biol. 8, R129 (2007).

  38. 38.

    Schmidl, C., Rendeiro, A. F., Sheffield, N. C. & Bock, C. ChIPmentation: fast, robust, low-input ChIP-seq for histones and transcription factors. Nat. Methods 12, 963–965 (2015).

  39. 39.

    modENCODE Consortium. Identification of functional elements and regulatory circuits by Drosophila modENCODE. Science 330, 1787–1797 (2010).

  40. 40.

    Potier, D. et al. Mapping gene regulatory networks in Drosophila eye development by large-scale transcriptome perturbations and motif inference. Cell Rep. 9, 2290–2303 (2014).

  41. 41.

    Herrmann, C., Van de Sande, B., Potier, D. & Aerts, S. i-cisTarget: an integrative genomics method for the prediction of regulatory features and cis-regulatory modules. Nucleic Acids Res. 40, e114 (2012).

  42. 42.

    Imrichová, H., Hulselmans, G., Atak, Z. K., Potier, D. & Aerts, S. i-cisTarget 2015 update: generalized cis-regulatory enrichment analysis in human, mouse and fly. Nucleic Acids Res. 43, W57–W64 (2015).

  43. 43.

    Mace, K. A., Pearson, J. C. & McGinnis, W. An epidermal barrier wound repair pathway in Drosophila is mediated by grainy head. Science 308, 381–385 (2005).

  44. 44.

    Wang, S. et al. The tyrosine kinase Stitcher activates grainy head and epidermal wound healing in Drosophila. Nat. Cell Biol. 11, 890–895 (2009).

  45. 45.

    Boyle, A. P. et al. High-resolution genome-wide in vivo footprinting of diverse transcription factors in human cells. Genome Res. 21, 456–464 (2011).

  46. 46.

    Aerts, S. et al. Robust target gene discovery through transcriptome perturbations and genome-wide enhancer predictions in Drosophila uncovers a regulatory basis for sensory specification. PLoS Biol. 8, e1000435 (2010).

  47. 47.

    Li, X. Y. et al. Transcription factors bind thousands of active and inactive regions in the Drosophila blastoderm. PLoS Biol. 6, e27 (2008).

  48. 48.

    Ostrin, E. J. et al. Genome-wide identification of direct targets of the Drosophila retinal determination protein Eyeless. Genome Res. 16, 466–476 (2006).

  49. 49.

    Stark, A. et al. Discovery of functional elements in 12 Drosophila genomes using evolutionary signatures. Nature 450, 219–232 (2007).

  50. 50.

    Nüsslein-Volhard, C., Wieschaus, E. & Kluding, H. Mutations affecting the pattern of the larval cuticle in Drosophila melanogaster: I. zygotic loci on the second chromosome. Wilehm Roux Arch. Dev. Biol. 193, 267–282 (1984).

  51. 51.

    Luo, L., Liao, Y. J., Jan, L. Y. & Jan, Y. N. Distinct morphogenetic functions of similar small GTPases: Drosophila Drac1 is involved in axonal outgrowth and myoblast fusion. Genes Dev. 8, 1787–1802 (1994).

  52. 52.

    Svetlichnyy, D., Imrichova, H., Fiers, M., Kalender Atak, Z. & Aerts, S. Identification of high-impact cis-regulatory mutations using transcription-factor-specific random forest models. PLoS Comput. Biol. 11, e1004590 (2015).

  53. 53.

    el Hassan, M. A. & Calladine, C. R. Propeller-twisting of base-pairs and the conformational mobility of dinucleotide steps in DNA. J. Mol. Biol. 259, 95–103 (1996).

  54. 54.

    Struhl, K. & Segal, E. Determinants of nucleosome positioning. Nat. Struct. Mol. Biol. 20, 267–273 (2013).

  55. 55.

    Kaplan, N. et al. The DNA-encoded nucleosome organization of a eukaryotic genome. Nature 458, 362–366 (2009).

  56. 56.

    Cirillo, L. A. & Zaret, K. S. An early developmental transcription factor complex that is more stable on nucleosome core particles than on free DNA. Mol. Cell 4, 961–969 (1999).

  57. 57.

    Wilanowski, T. et al. A highly conserved novel family of mammalian developmental transcription factors related to Drosophila grainy head. Mech. Dev. 114, 37–50 (2002).

  58. 58.

    Gao, X. et al. Evidence for multiple roles for Grainyhead-like 2 in the establishment and maintenance of human muco-ciliary airway epithelium. Proc. Natl. Acad. Sci. USA 110, 9356–9361 (2013).

  59. 59.

    Chung, V. Y. et al. GRHL2–miR-200–ZEB1 maintains the epithelial status of ovarian cancer through transcriptional regulation and histone modification. Sci. Rep. 6, 19943 (2016).

  60. 60.

    Frisch, S. M., Farris, J. C. & Pifer, P. M. Roles of Grainyhead-like transcription factors in cancer. Oncogene 36, 6067–6073 (2017).

  61. 61.

    Ming, Q. et al. Structural basis of gene regulation by the grainy head (CP2) transcription factor family. Nucleic Acids Res. 46, 2082–2095 (2018).

  62. 62.

    ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

  63. 63.

    Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

  64. 64.

    Kumasaka, N., Knights, A. J. & Gaffney, D. J. Fine-mapping cellular QTLs with RASQUAL and ATAC-seq. Nat. Genet. 48, 206–213 (2016).

  65. 65.

    Goltsev, Y., Hsiong, W., Lanzaro, G. & Levine, M. Different combinations of gap repressors for common stripes in Anopheles and Drosophila embryos. Dev. Biol. 275, 435–446 (2004).

  66. 66.

    Varma, S. et al. Grainyhead-like 2 (GRHL2) distribution reveals novel pathophysiological differences between human idiopathic pulmonary fibrosis and mouse models of pulmonary fibrosis. Am. J. Physiol. Lung Cell. Mol. Physiol. 306, L405–L419 (2014).

  67. 67.

    Carpinelli, M. R., de Vries, M. E., Jane, S. M. & Dworkin, S. Grainyhead-like transcription factors in craniofacial development. J. Dent. Res. 96, 1200–1209 (2017).

  68. 68.

    Harrison, M. M., Botchan, M. R. & Cline, T. W. Grainy head and Zelda compete for binding to the promoters of the earliest-expressed Drosophila genes. Dev. Biol. 345, 248–255 (2010).

  69. 69.

    Ye, T. et al. seqMINER: an integrated ChIP-seq data interpretation platform. Nucleic Acids Res. 39, e35 (2011).

  70. 70.

    Davie, K. et al. Discovery of transcription factors and regulatory regions driving in vivo tumor development by ATAC-seq and FAIRE-seq open-chromatin profiling. PLoS Genet. 11, e1004994 (2015).

  71. 71.

    Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).

  72. 72.

    Gramates, L. S. et al. FlyBase at 25: looking to the future. Nucleic Acids Res. 45, D663–D671 (2017).

  73. 73.

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

  74. 74.

    Li, H. Seqtk: toolkit for processing sequences in FASTA/Q formats. https://github.com/lh3/seqtk (2017).

  75. 75.

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

  76. 76.

    Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).

  77. 77.

    Zhang, Y. et al. Model-based analysis of ChIP-seq (MACS). Genome Biol. 9, R137 (2008).

  78. 78.

    Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general-purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

  79. 79.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

  80. 80.

    Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

  81. 81.

    Thomas-Chollier, M. et al. RSAT peak-motifs: motif analysis in full-size ChIP-seq datasets. Nucleic Acids Res. 40, e31 (2012).

  82. 82.

    Schep, A. N., Wu, B., Buenrostro, J. D. & Greenleaf, W. J. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat. Methods 14, 975–978 (2017).

  83. 83.

    Wei, T. et al. corrplot: visualization of a correlation matrix. https://github.com/taiyun/corrplot (2017).

  84. 84.

    Tarailo-Graovac, M. & Chen, N. Using RepeatMasker to identify repetitive elements in genomic sequences. Curr. Protoc. Bioinformatics Chapter 4, Unit 4.10 (2009).

  85. 85.

    Quinlan, A. R. BEDTools: the Swiss-army tool for genome feature analysis. Curr. Protoc. Bioinformatics 47, 11.12.1–11.12.34 (2014).

  86. 86.

    Fisher, R. A. The logic of inductive inference. J. R. Stat. Soc. 98, 39–82 (1935).

  87. 87.

    Weirauch, M. T. et al. Evaluation of methods for modeling transcription factor sequence specificity. Nat. Biotechnol. 31, 126–134 (2013).

  88. 88.

    Robin, X. et al. pROC: an open-source package for R and S + to analyze and compare ROC curves. BMC Bioinformatics 12, 77 (2011).

  89. 89.

    Frith, M. C., Li, M. C. & Weng, Z. Cluster-Buster: finding dense clusters of motifs in DNA sequences. Nucleic Acids Res. 31, 3666–3668 (2003).

  90. 90.

    Pedregosa, F. et al. Scikit-learn: machine-learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

  91. 91.

    Frith, M. C., Hansen, U. & Weng, Z. Detection of cis-element clusters in higher-eukaryotic DNA. Bioinformatics 17, 878–889 (2001).

  92. 92.

    Rice, P., Longden, I. & Bleasby, A. EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet. 16, 276–277 (2000).

  93. 93.

    Mahony, S. & Benos, P. V. STAMP: a web tool for exploring DNA-binding motif similarities. Nucleic Acids Res. 35, W253–W258 (2007).

  94. 94.

    van Bergeijk, P., Heimiller, J., Uyetake, L. & Su, T. T. Genome-wide expression analysis identifies a modulator of ionizing-radiation-induced p53-independent apoptosis in Drosophila melanogaster. PLoS One 7, e36539 (2012).

  95. 95.

    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).

  96. 96.

    Corces, M. R. et al. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat. Methods 14, 959–962 (2017).

Download references


We would like to thank F. Casares for helpful discussions, L. Vanden Broeck (KU Leuven) for sharing the DGRP lines with us, S. Bray (University of Cambridge) for sharing the UAS::grhN line and for insightful discussions, and M. Harrison (UW School of Medicine and Public Health) for sharing antibody to the C terminus of Grh. Stocks obtained from the Bloomington Drosophila Stock Center (NIH grant no. P40OD018537) were used in this study. Computing was performed at the Flemish Supercomputer Center (VSC). This work was supported by funding from FWO project grants (grant no. G.0640.13 (S. Aerts), G.0791.14 (S. Aerts), G.0C04.17 (S. Aerts) and G.0954.16 N (G. Halder)), Special Research Fund (BOF) KU Leuven grants (grant no. PF/10/016 and OT/13/103; to S. Aerts), the Foundation Against Cancer (grant no. 2012-F2 and 2016-070; to S. Aerts), an ERC CoG grant (724226_cis-CONTROL; S. Aerts), PhD fellowships from the Flemish Agency for Innovation by Science and Technology (J.J., K.D. and H.I.) and a postdoctoral research fellowship from Kom op tegen Kanker (Stand up to Cancer), the Flemish Cancer Society (J.W.).

Author information


  1. VIB Center for Brain and Disease Research, Laboratory of Computational Biology, Leuven, Belgium

    • Jelle Jacobs
    • , Kristofer Davie
    • , Hana Imrichova
    • , Valerie Christiaens
    • , Gert Hulselmans
    • , Delphine Potier
    • , Jasper Wouters
    • , Carmen B. González-Blas
    • , Duygu Koldere
    • , Sara Aibar
    •  & Stein Aerts
  2. KU Leuven, Department of Human Genetics, Leuven, Belgium

    • Jelle Jacobs
    • , Kristofer Davie
    • , Hana Imrichova
    • , Valerie Christiaens
    • , Gert Hulselmans
    • , Delphine Potier
    • , Jasper Wouters
    • , Carmen B. González-Blas
    • , Duygu Koldere
    • , Sara Aibar
    •  & Stein Aerts
  3. VIB Center for Cancer Biology, Leuven, Belgium

    • Mardelle Atkins
    • , Lucia Romanelli
    •  & Georg Halder
  4. KU Leuven, Department of Oncology, Leuven, Belgium

    • Mardelle Atkins
    • , Lucia Romanelli
    •  & Georg Halder
  5. Bogazici University, Molecular Biology and Genetics, Istanbul, Turkey

    • Ibrahim I. Taskiran
  6. Politecnico di Torino, Automatics and Informatics, Turin, Italy

    • Giulia Paciello


  1. Search for Jelle Jacobs in:

  2. Search for Mardelle Atkins in:

  3. Search for Kristofer Davie in:

  4. Search for Hana Imrichova in:

  5. Search for Lucia Romanelli in:

  6. Search for Valerie Christiaens in:

  7. Search for Gert Hulselmans in:

  8. Search for Delphine Potier in:

  9. Search for Jasper Wouters in:

  10. Search for Ibrahim I. Taskiran in:

  11. Search for Giulia Paciello in:

  12. Search for Carmen B. González-Blas in:

  13. Search for Duygu Koldere in:

  14. Search for Sara Aibar in:

  15. Search for Georg Halder in:

  16. Search for Stein Aerts in:


J.J. and S. Aerts conceived and designed the experiments; J.J. and V.C. performed all of the bulk ATAC–seq analyses and generated flies; K.D. and V.C. performed single-cell ATAC–seq and sorted ATAC–seq; D.P. and V.C. performed ATAC–seq on the Drosophila species; V.C. performed Grh-specific ChIPmentation; L.R., M.A., V.C., D.K. and J.J. performed imaginal disc dissections, staining and imaging; J.J. analyzed the data, with assistance from G. Hulselmans on the evolution part and BLS, assistance from I.I.T. and G.P. on the random forest analyses, assistance from C.B.G.-B. and K.D. on the single-cell analysis and assistance from S. Aibar on DNA footprinting; J.W. performed omni-ATAC–seq on MCF-7 cells and GRHL knockdown; H.I. analyzed the human GRHL data; and J.J. and S. Aerts wrote the manuscript, with insightful feedback from M.A. and G. Halder.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Stein Aerts.

Integrated Supplementary Information

  1. Supplementary Figure 1 Correlation of ATAC–seq peaks between eye–antennal discs (DGRP) and adult brain tissues.

    Spearman correlation between the accessible regions (normalized ATAC–seq reads) of 30 eye–antennal disc samples (DGRP) and 2 adult brain samples. a, Pairwise correlation matrix of all 32 samples. be, Correlation plots visualizing normalized (RPM) ATAC–seq reads for all 34,768 accessible regions (over the 30 EA discs and 2 brain samples). b, Correlation (Spearman’s ρ = 0.95) between irradiated and control tissue. c, Correlation (Spearman’s ρ = 0.90) between different DGRP lines. d, Correlation (Spearman’s ρ = 0.96) between the two adult brain samples. e, Low correlation (Spearman’s ρ = 0.27) between the accessible regions of eye–antennal discs (DGRP 25,199) and adult brain tissues.

  2. Figure Supplementary 2 Single-cell ATAC–seq.

    Comparing single-cell with bulk ATAC–seq. a, Correlation plot between bulk and aggregate single-cell ATAC–seq signal on all 30,774 accessible regions in eye–antennal discs (Spearman’s ρ = 0.83). b, Track visualizing the aggregate ATAC–seq signal of 68 individual single cells and bulk ATAC–seq signal (1 of 30 similar eye–antennal disc samples is shown) around the grh TSS.

  3. Supplementary Figure 3 Atonal motifs are co-conserved in accessible Grainy head enhancers.

    a, Heat map of Ato motif CRM scores across the conserved Grh enhancers, zoomed in on the 92 regions with a conserved Ato motif (BLS over 3 for each region). b, GSEA visualizing enrichment for gain in expression in the Ato gain-of-function mutants versus Ato loss of function (x axis; 13,087 log2 (FC)–ranked genes (GSE16713)), of the 63 genes that are near the 92 conserved Ato–Grh regions (black bars). FDR was calculated by GSEA by randomly permuting the ranked gene list 10,000 times. c, Visualization of Ato- and Grh-binding sites on 20 previously characterized Ato enhancers using TOUCANjs. Six enhancers that have clear Grh binding (ChIP + ATAC + motif) are marked by a red square.

  4. Supplementary Figure 4 Grainy head–mutant phenotypes in eye–antennal discs.

    a, Cross scheme to generate grhIM mutant clones in eye–antennal discs from wandering third-instar larvae. b,b′, Confocal images of eye–antennal discs with grhIM mutant clones. Wild-type cells are marked with GFP and have Grh protein in their nuclei (red, b′), while the grh-mutant clones do not have GFP or detectable Grh protein (reproducible results for four discs each). Scale bars, 100 μm. c,d, Confocal images of eye–antennal discs; nuclei are stained with DAPI (blue) and wild-type cells are stained with GFP (green) and Dcp1, a marker of apoptosis (red, or white in the bottom panels) (reproducible results for three discs each). c,c′, Control with genotype ey3.5-flp/ey3.5-flp; Ubi-GFP/CyO. A regular but sparse pattern of apoptotic cells is observed across the disc (white, c′). d, Disc with Grh-mutant clones, with genotype ey3.5-flp/ey3.5-flp; FRT42 grhIM/FRT42 Ubi-GFP. The non-GFP clones are homozygous mutant for grh (grhIM). Loss of Grh in these clones is associated with increased levels of apoptotic cells, as visualized by Dcp1 expression (d′). e, Cross schemes for the control and largely grhIM mutant eye–antennal discs that were used for ATAC–seq. The control discs have ubiquitous expression of Grh protein (red + blue) across the disc; in the grh-mutant disc, most cells do not have Grh protein (red) anymore (blue only) (reproducible results for five discs). f, Photographs of pupae originating from the grhIM/cell-lethal cross; the top two pupae are controls, which appear normal and were alive or enclosed (n = 111). Pupae from animals with the grhIM mutant discs are shown on the bottom row (zoomed-in view in f′). These grh-mutant animals have lethal defects during pupation. From the initial cross, one-third of animals are expected to be homozygous mutant for Grh; out of 203 pupae, 71 were dead and 132 were alive.

  5. Supplementary Figure 5 Grh motifs used in the random forest classifier and random forest precision-recall curves.

    a, Logos and origin of the five Grh motifs that were used as features in the random forest classifier. b, Precision-recall curves assessing the performance of a random forest classifier to discriminate between all 1,300 bound and all 4,000 unbound Grh motifs. Shown are curves with random order (gray; AUPRC 0.248), random forest with one Grh motif (brown; AUPRC 0.345), random forest using the five Grh motifs (red; AUPRC 0.550), random forest using the five Grh motifs and positive repeats (green; AUPRC 0.634), random forest using the five Grh motifs and GC content (purple; AUPRC 0.674), and a combination of the five Grh motifs, repeats and GC content (blue; AUPRC 0.736) (Methods).

  6. Supplementary Figure 6 DNA-binding domain conservation between Drosophila Grh and the human GRHLs.

    The amino acids that directly interact with specific DNA bases are marked in green; other amino acids that interact with the DNA backbone through hydrogen bonds or Coulomb interactions are marked in yellow. Interactions for GRHL1 were obtained from Ming et al. (Nucleic Acids Res. https://dx.doi.org/10.1093/nar/gkx1299, 2018).

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–6, Supplementary Tables 1–6 and Supplementary Notes 1–3

  2. Reporting Summary

About this article

Publication history