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Highly parallel assays of tissue-specific enhancers in whole Drosophila embryos

Abstract

Transcriptional enhancers are a primary mechanism by which tissue-specific gene expression is achieved. Despite the importance of these regulatory elements in development, responses to environmental stresses and disease, testing enhancer activity in animals remains tedious, with a minority of enhancers having been characterized. Here we describe 'enhancer-FACS-seq' (eFS) for highly parallel identification of active, tissue-specific enhancers in Drosophila melanogaster embryos. Analysis of enhancers identified by eFS as being active in mesodermal tissues revealed enriched DNA binding site motifs of known and putative, previously uncharacterized mesodermal transcription factors. Naive Bayes classifiers using transcription factor binding site motifs accurately predicted mesodermal enhancer activity. Application of eFS to other cell types and organisms should accelerate the cataloging of enhancers and understanding how transcriptional regulation is encoded in them.

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Figure 1: eFS methodology.
Figure 2: Active enhancers identified from eFS data.
Figure 3: Validations of enhancers identified as active (Padj < 0.1) by eFS.
Figure 4: Enrichment of various genomic marks among eFS-identified enhancers.
Figure 5: Computational motif analysis of eFS-identified active enhancers.

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References

  1. Bulyk, M.L. Computational prediction of transcription-factor binding site locations. Genome Biol. 5, 201 (2003).

    Article  Google Scholar 

  2. Davidson, E. Inside the cis-regulatory module: control logic, and how regulatory environment is transduced into spatial patterns of gene expression. in Genomic Regulatory Systems: Development and Evolution, chapter 2, 25–62 (Academic Press, 2001).

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

    Article  CAS  Google Scholar 

  4. Halfon, M.S., Gallo, S.M. & Bergman, C.M. REDfly 2.0: an integrated database of cis-regulatory modules and transcription factor binding sites in Drosophila. Nucleic Acids Res. 36, D594–D598 (2008).

    Article  CAS  Google Scholar 

  5. Sandmann, T. et al. A core transcriptional network for early mesoderm development in Drosophila melanogaster. Genes Dev. 21, 436–449 (2007).

    Article  CAS  Google Scholar 

  6. Zinzen, R.P., Girardot, C., Gagneur, J., Braun, M. & Furlong, E.E. Combinatorial binding predicts spatio-temporal cis-regulatory activity. Nature 462, 65–70 (2009).

    Article  CAS  Google Scholar 

  7. Jagalur, M., Pal, C., Learned-Miller, E., Zoeller, R.T. & Kulp, D. Analyzing in situ gene expression in the mouse brain with image registration, feature extraction and block clustering. BMC Bioinformatics 8 (suppl. 10), S5 (2007).

    Article  Google Scholar 

  8. Gertz, J., Siggia, E.D. & Cohen, B.A. Analysis of combinatorial cis-regulation in synthetic and genomic promoters. Nature 457, 215–218 (2009).

    Article  CAS  Google Scholar 

  9. Sharon, E. et al. Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters. Nat. Biotechnol. 30, 521–530 (2012).

    Article  CAS  Google Scholar 

  10. Nam, J., Dong, P., Tarpine, R., Istrail, S. & Davidson, E.H. Functional cis-regulatory genomics for systems biology. Proc. Natl. Acad. Sci. USA 107, 3930–3935 (2010).

    Article  CAS  Google Scholar 

  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).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  14. Dunin-Borkowski, O.M. & Brown, N.H. Mammalian CD2 is an effective heterologous marker of the cell surface in Drosophila. Dev. Biol. 168, 689–693 (1995).

    Article  CAS  Google Scholar 

  15. Bate, M. The mesoderm and its derivatives. in The development of Drosophila melanogaster (eds., Bate, M. & Martinez-Arias, A.) 1013–1090 (Cold Spring Harbor Laboratory, 1993).

  16. Roy, S. et al. Identification of functional elements and regulatory circuits by Drosophila modENCODE. Science 330, 1787–1797 (2010).

    Article  CAS  Google Scholar 

  17. Contrino, S. et al. modMine: flexible access to modENCODE data. Nucleic Acids Res. 40, D1082–D1088 (2012).

    Article  CAS  Google Scholar 

  18. Thomas, S. et al. Dynamic reprogramming of chromatin accessibility during Drosophila embryo development. Genome Biol. 12, R43 (2011).

    Article  CAS  Google Scholar 

  19. Warner, J. et al. Systematic identification of mammalian regulatory motifs' target genes and functions. Nat. Methods 5, 347–353 (2008).

    Article  CAS  Google Scholar 

  20. Groth, A.C., Fish, M., Nusse, R. & Calos, M.P. Construction of transgenic Drosophila by using the site-specific integrase from phage phiC31. Genetics 166, 1775–1782 (2004).

    Article  CAS  Google Scholar 

  21. Duan, H., Skeath, J.B. & Nguyen, H.T. Drosophila Lame duck, a novel member of the Gli superfamily, acts as a key regulator of myogenesis by controlling fusion-competent myoblast development. Development 128, 4489–4500 (2001).

    CAS  PubMed  Google Scholar 

  22. Guruharsha, K.G., Ruiz-Gomez, M., Ranganath, H.A., Siddharthan, R. & Vijayraghavan, K. The complex spatio-temporal regulation of the Drosophila myoblast attractant gene duf/kirre. PLoS ONE 4, e6960 (2009).

    Article  CAS  Google Scholar 

  23. Hoffmann, S. et al. Fast mapping of short sequences with mismatches, insertions and deletions using index structures. PLoS Comput. Biol. 5, e1000502 (2009).

    Article  Google Scholar 

  24. Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).

    Article  CAS  Google Scholar 

  25. Gallo, S.M. et al. REDfly v3.0: toward a comprehensive database of transcriptional regulatory elements in Drosophila. Nucleic Acids Res. 39, D118–D123 (2011).

    Article  CAS  Google Scholar 

  26. Bonn, S. et al. Tissue-specific analysis of chromatin state identifies temporal signatures of enhancer activity during embryonic development. Nat. Genet. 44, 148–156 (2012).

    Article  CAS  Google Scholar 

  27. Heintzman, N.D. et al. Histone modifications at human enhancers reflect global cell-type-specific gene expression. Nature 459, 108–112 (2009).

    Article  CAS  Google Scholar 

  28. Heintzman, N.D. et al. Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome. Nat. Genet. 39, 311–318 (2007).

    Article  CAS  Google Scholar 

  29. Pekowska, A. et al. H3K4 tri-methylation provides an epigenetic signature of active enhancers. EMBO J. 30, 4198–4210 (2011).

    Article  CAS  Google Scholar 

  30. Kharchenko, P.V. et al. Comprehensive analysis of the chromatin landscape in Drosophila melanogaster. Nature 471, 480–485 (2011).

    Article  CAS  Google Scholar 

  31. Ernst, J. et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473, 43–49 (2011).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  33. Azpiazu, N. & Frasch, M. tinman and bagpipe: two homeo box genes that determine cell fates in the dorsal mesoderm of Drosophila. Genes Dev. 7, 1325–1340 (1993).

    Article  CAS  Google Scholar 

  34. Cripps, R.M., Zhao, B. & Olson, E.N. Transcription of the myogenic regulatory gene Mef2 in cardiac, somatic, and visceral muscle cell lineages is regulated by a Tinman-dependent core enhancer. Dev. Biol. 215, 420–430 (1999).

    Article  CAS  Google Scholar 

  35. Busser, B.W. et al. Integrative analysis of the zinc finger transcription factor Lame duck in the Drosophila myogenic gene regulatory network. Proc. Natl. Acad. Sci. USA 109, 20768–20773 (2012).

    Article  CAS  Google Scholar 

  36. Zhu, L.J. et al. FlyFactorSurvey: a database of Drosophila transcription factor binding specificities determined using the bacterial one-hybrid system. Nucleic Acids Res. 39, D111–D117 (2011).

    Article  CAS  Google Scholar 

  37. Philippakis, A.A. et al. Expression-guided in silico evaluation of candidate cis regulatory codes for Drosophila muscle founder cells. PLoS Comput. Biol. 2, e53 (2006).

    Article  Google Scholar 

  38. Busser, B.W. et al. Molecular mechanism underlying the regulatory specificity of a Drosophila homeodomain protein that specifies myoblast identity. Development 139, 1164–1174 (2012).

    Article  CAS  Google Scholar 

  39. Thisse, B., el Messal, M. & Perrin-Schmitt, F. The twist gene: isolation of a Drosophila zygotic gene necessary for the establishment of dorsoventral pattern. Nucleic Acids Res. 15, 3439–3453 (1987).

    Article  CAS  Google Scholar 

  40. Furlong, E.E., Andersen, E.C., Null, B., White, K.P. & Scott, M.P. Patterns of gene expression during Drosophila mesoderm development. Science 293, 1629–1633 (2001).

    Article  CAS  Google Scholar 

  41. Grimaud, C., Negre, N. & Cavalli, G. From genetics to epigenetics: the tale of Polycomb group and trithorax group genes. Chromosome Res. 14, 363–375 (2006).

    Article  CAS  Google Scholar 

  42. 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).

    Article  CAS  Google Scholar 

  43. Yuan, Y., Guo, L., Shen, L. & Liu, J.S. Predicting gene expression from sequence: a reexamination. PLoS Comput. Biol. 3, e243 (2007).

    Article  Google Scholar 

  44. Busser, B.W. et al. A machine learning approach for identifying novel cell type-specific transcriptional regulators of myogenesis. PLoS Genet. 8, e1002531 (2012).

    Article  CAS  Google Scholar 

  45. Lister, J.A. Transgene excision in zebrafish using the phiC31 integrase. Genesis 48, 137–143 (2010).

    Article  CAS  Google Scholar 

  46. Thyagarajan, B., Olivares, E.C., Hollis, R.P., Ginsburg, D.S. & Calos, M.P. Site-specific genomic integration in mammalian cells mediated by phage phiC31 integrase. Mol. Cell Biol. 21, 3926–3934 (2001).

    Article  CAS  Google Scholar 

  47. Hollis, R.P. et al. Phage integrases for the construction and manipulation of transgenic mammals. Reprod. Biol. Endocrinol. 1, 79 (2003).

    Article  Google Scholar 

  48. Barolo, S., Carver, L.A. & Posakony, J.W. GFP and beta-galactosidase transformation vectors for promoter/enhancer analysis in Drosophila. Biotechniques 29, 726–732 (2000).

    Article  CAS  Google Scholar 

  49. Halfon, M.S. et al. Ras pathway specificity is determined by the integration of multiple signal-activated and tissue-restricted transcription factors. Cell 103, 63–74 (2000).

    Article  CAS  Google Scholar 

  50. Bischof, J., Maeda, R.K., Hediger, M., Karch, F. & Basler, K. An optimized transgenesis system for Drosophila using germ-line-specific phiC31 integrases. Proc. Natl. Acad. Sci. USA 104, 3312–3317 (2007).

    Article  CAS  Google Scholar 

  51. Markstein, M., Pitsouli, C., Villalta, C., Celniker, S.E. & Perrimon, N. Exploiting position effects and the gypsy retrovirus insulator to engineer precisely expressed transgenes. Nat. Genet. 40, 476–483 (2008).

    Article  CAS  Google Scholar 

  52. Estrada, B. et al. An integrated strategy for analyzing the unique developmental programs of different myoblast subtypes. PLoS Genet. 2, e16 (2006).

    Article  Google Scholar 

  53. Quail, M.A., Swerdlow, H. & Turner, D.J. Improved protocols for the illumina genome analyzer sequencing system. in Current Protocols in Human Genetics 18.2 (2009).

  54. Aboukhalil, A. & Bulyk, M.L. LOESS correction for length variation in gene set-based genomic sequence analysis. Bioinformatics 28, 1446–1454 (2012).

    Article  CAS  Google Scholar 

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

    Google Scholar 

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Acknowledgements

This project was supported in part by a US National Science Foundation Graduate Research Fellowship to L.A.B. and by grant R01 HG005287 from the US National Institutes of Health to M.L.B. We thank G. Losyev and C. Durkin for technical assistance, K.G. Guruharsha and K. VijayRaghavan for sharing coordinates of the duf enhancer before its publication, R.P. McCord, M. Markstein and O. Iartchouk for helpful discussion, and R. Gordân, M. Markstein and T. Siggers for critical reading of the manuscript.

Author information

Authors and Affiliations

Authors

Contributions

M.L.B. designed the study; S.S.G., P.W.E., A.M.M. and M.L.B. developed the eFS technology; S.S.G. and A.V. sorted flies; S.S.G., L.A.B., M.P. and A.A. performed computational data analysis; S.S.G., P.W.E., A.V., Y.K. and X.Z. performed PCRs; B.W.B., X.Z., A.S. and C.E.G. generated CD2 fly lines; S.S.G., A.V., A.P. and A.I. performed validation assays; S.S.G., L.A.B., M.P., A.A., B.W.B. and M.L.B. wrote Supplementary Note 2; S.S.G., L.A.B., M.P., A.A. and M.L.B. prepared figures and tables; and S.S.G. and M.L.B. wrote the manuscript.

Corresponding author

Correspondence to Martha L Bulyk.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Notes 1–3 and Supplementary Figures 1–9 (PDF 14222 kb)

Supplementary Table 1

Genomic coordinates, sequences and PCR primer sequences for cCRMs in library. (XLS 631 kb)

Supplementary Table 2

Numbers of cells in each collected cell population. (XLS 25 kb)

Supplementary Table 3

Number of sequencing reads obtained and mapped per sample. (XLS 28 kb)

Supplementary Table 4

List of all cCRMs detected by eFS and eFS results (enriched/depleted and P value), for all 6 cell populations, that is, GFP+CD2+ and GFP+CD2 for each of twi:CD2, ef2-IED5:CD2, and duf:CD2 sorts, along with overlap with ChIP-chip data, HOT regions, REDfly regions and results from traditional reporter assays in whole embryos. (XLS 371 kb)

Supplementary Table 5

Concordance of cCRM activity and expression pattern of adjacent genes. (XLS 59 kb)

Supplementary Table 6

Summary of traditional reporter assay results for all 6 cell populations, that is, GFP+CD2+ and GFP+CD2 cells for each of twi:CD2, and Mef2-I-ED5:CD2 and duf:CD2 sorts. (XLS 34 kb)

Supplementary Table 7

Lever results for motifs found to be significantly enriched (FDR ≤ 0.1) either individually or in pair-wise combination among eFS-enriched cCRMs. (XLS 1209 kb)

Supplementary Table 8

Summary of eFS outcomes of cCRMs selected for eFS testing according to various genomic features (for example, DHS, CBP ChIP binding signal and ChIP-CRM). (XLS 37 kb)

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Gisselbrecht, S., Barrera, L., Porsch, M. et al. Highly parallel assays of tissue-specific enhancers in whole Drosophila embryos. Nat Methods 10, 774–780 (2013). https://doi.org/10.1038/nmeth.2558

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