Skip to main content
SpringerLink
Log in

Using GRO-Seq to Measure Circadian Transcription and Discover Circadian Enhancers

  • Protocol
  • First Online:
Circadian Clocks

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2130))

Abstract

Circadian gene transcription transmits timing information and drives cyclic physiological processes across various tissues. Recent studies indicate that oscillating enhancer activity is a major driving force of rhythmic gene transcription. Functional circadian enhancers can be identified in an unbiased manner by correlation with the rhythms of nearby gene transcription.

Global run-on sequencing (GRO-seq) measures nascent transcription of both pre-mRNAs and enhancer RNAs (eRNAs) at a genome-wide level, making it a unique tool for unraveling complex gene regulation mechanisms in vivo. Here, we describe a comprehensive protocol, ranging from wet lab to in silico analysis, for detecting and quantifying circadian transcription of genes and eRNAs. Moreover, using gene-eRNA correlation, we detail the steps necessary to identify functional enhancers and transcription factors (TFs) that control circadian gene expression in vivo. While we use mouse liver as an example, this protocol is applicable for multiple tissues.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 149.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bass J, Lazar MA (2016) Circadian time signatures of fitness and disease. Science 354(6315):994–999. https://doi.org/10.1126/science.aah4965

    Article  CAS  PubMed  Google Scholar 

  2. Zhang EE, Kay SA (2010) Clocks not winding down: unravelling circadian networks. Nat Rev Mol Cell Biol 11(11):764–776. https://doi.org/10.1038/nrm2995

    Article  CAS  PubMed  Google Scholar 

  3. Feng D, Liu T, Sun Z, Bugge A, Mullican SE, Alenghat T, Liu XS, Lazar MA (2011) A circadian rhythm orchestrated by histone deacetylase 3 controls hepatic lipid metabolism. Science 331(6022):1315–1319. https://doi.org/10.1126/science

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Feng D, Lazar MA (2012) Clocks, metabolism, and the epigenome. Mol Cell 47(2):158–167. https://doi.org/10.1016/j.molcel.2012.06.026

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Geertz M, Maerkl SJ (2010) Experimental strategies for studying transcription factor-DNA binding specificities. Brief Funct Genomics 9(5-6):362–373. https://doi.org/10.1093/bfgp/elq023

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Berger MF, Philippakis AA, Qureshi AM, He FS, Estep PW 3rd, Bulyk ML (2006) Compact, universal DNA microarrays to comprehensively determine transcription-factor binding site specificities. Nat Biotechnol 24(11):1429–1435. https://doi.org/10.1038/nbt1246

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Fang B, Mane-Padros D, Bolotin E, Jiang T, Sladek FM (2012) Identification of a binding motif specific to HNF4 by comparative analysis of multiple nuclear receptors. Nucleic Acids Res 40(12):5343–5356. https://doi.org/10.1093/nar/gks190

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Slattery M, Zhou T, Yang L, Dantas Machado AC, Gordan R, Rohs R (2014) Absence of a simple code: how transcription factors read the genome. Trends Biochem Sci 39(9):381–399. https://doi.org/10.1016/j.tibs.2014.07.002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Buenrostro JD, Wu B, Chang HY, Greenleaf WJ (2015) ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr Protoc Mol Biol 109(29):21–29. https://doi.org/10.1002/0471142727.mb2129s109

    Article  PubMed  PubMed Central  Google Scholar 

  10. Valouev A, Johnson DS, Sundquist A, Medina C, Anton E, Batzoglou S, Myers RM, Sidow A (2008) Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data. Nat Methods 5(9):829–834. https://doi.org/10.1038/nmeth.1246

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Boyle AP, Davis S, Shulha HP, Meltzer P, Margulies EH, Weng Z, Furey TS, Crawford GE (2008) High- resolution mapping and characterization of open chromatin across the genome. Cell 132(2):311–322. https://doi.org/10.1016/j.cell.2007.12.014

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Calo E, Wysocka J (2013) Modification of enhancer chromatin: what, how, and why? Mol Cell 49(5):825–837. https://doi.org/10.1016/j.molcel.2013.01.038

    Article  CAS  PubMed  Google Scholar 

  13. Hon GC, Hawkins RD, Ren B (2009) Predictive chromatin signatures in the mammalian genome. Hum Mol Genet 18(R2):R195–R201. https://doi.org/10.1093/hmg/ddp409

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Dostie J, Richmond TA, Arnaout RA, Selzer RR, Lee WL, Honan TA, Rubio ED, Krumm A, Lamb J, Nusbaum C, Green RD, Dekker J (2006) Chromosome conformation capture carbon copy (5C): a massively parallel solution for mapping interactions between genomic elements. Genome Res 16(10):1299–1309. https://doi.org/10.1101/gr.5571506

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Lieberman-Aiden E, van Berkum NL, Williams L, Imakaev M, Ragoczy T, Telling A, Amit I, Lajoie BR, Sabo PJ, Dorschner MO, Sandstrom R, Bernstein B, Bender MA, Groudine M, Gnirke A, Stamatoyannopoulos J, Mirny LA, Lander ES, Dekker J (2009) Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326(5950):289–293. https://doi.org/10.1126/science.1181369

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Hakim O, Misteli T (2012) SnapShot: Chromosome confirmation capture. Cell 148(5):1068. https://doi.org/10.1016/j.cell.2012.02.019

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Step SE, Lim HW, Marinis JM, Prokesch A, Steger DJ, You SH, Won KJ, Lazar MA (2014) Anti-diabetic rosiglitazone remodels the adipocyte transcriptome by redistributing transcription to PPARgamma- driven enhancers. Genes Dev 28(9):1018–1028. https://doi.org/10.1101/gad.237628.114

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, Genome Project Data Processing S (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25(16):2078–2079. https://doi.org/10.1093/bioinformatics/btp352

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Kim TK, Hemberg M, Gray JM, Costa AM, Bear DM, Wu J, Harmin DA, Laptewicz M, Barbara-Haley K, Kuersten S, Markenscoff-Papadimitriou E, Kuhl D, Bito H, Worley PF, Kreiman G, Greenberg ME (2010) Widespread transcription at neuronal activity-regulated enhancers. Nature 465(7295):182–187. https://doi.org/10.1038/nature09033

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hah N, Murakami S, Nagari A, Danko CG, Kraus WL (2013) Enhancer transcripts mark active estrogen receptor binding sites. Genome Res 23(8):1210–1223. https://doi.org/10.1101/gr.152306.112

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Li W, Notani D, Rosenfeld MG (2016) Enhancers as non-coding RNA transcription units: recent insights and future perspectives. Nat Rev Genet 17(4):207–223. https://doi.org/10.1038/nrg.2016.4

    Article  CAS  PubMed  Google Scholar 

  22. Fang B, Everett LJ, Jager J, Briggs E, Armour SM, Feng D, Roy A, Gerhart-Hines Z, Sun Z, Lazar MA (2014) Circadian enhancers coordinate multiple phases of rhythmic gene transcription in vivo. Cell 159(5):1140–1152. https://doi.org/10.1016/j.cell.2014.10.022

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Langmead B, Trapnell C, Pop M, Salzberg SL (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10(3):R25. https://doi.org/10.1186/gb-2009-10-3-r25

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Quinlan AR, Hall IM (2010) BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26(6):841–842. https://doi.org/10.1093/bioinformatics/btq033

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, Cheng JX, Murre C, Singh H, Glass CK (2010) Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell 38(4):576–589. https://doi.org/10.1016/j.molcel.2010.05.004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Hughes ME, Hogenesch JB, Kornacker K (2010) JTK_CYCLE: an efficient nonparametric algorithm for detecting rhythmic components in genome-scale data sets. J Biol Rhythm 25(5):372–380. https://doi.org/10.1177/0748730410379711

    Article  Google Scholar 

  27. Robinson JT, Thorvaldsdottir H, Winckler W, Guttman M, Lander ES, Getz G, Mesirov JP (2011) Integrative genomics viewer. Nat Biotechnol 29(1):24–26. https://doi.org/10.1038/nbt.1754

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Nagari A, Murakami S, Malladi VS, Kraus WL (2017) Computational approaches for mining GRO-Seq data to identify and characterize active enhancers. Methods Mol Biol 1468:121–138. https://doi.org/10.1007/978-1-4939-4035-6_10

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Menet JS, Rodriguez J, Abruzzi KC, Rosbash M (2012) Nascent-Seq reveals novel features of mouse circadian transcriptional regulation. Elife 1:e00011. https://doi.org/10.7554/eLife.00011

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Anders S, Pyl PT, Huber W (2015) HTSeq--a python framework to work with high-throughput sequencing data. Bioinformatics 31(2):166–169. https://doi.org/10.1093/bioinformatics/btu638

    Article  CAS  PubMed  Google Scholar 

  31. Vollmers C, Schmitz RJ, Nathanson J, Yeo G, Ecker JR, Panda S (2012) Circadian oscillations of protein- coding and regulatory RNAs in a highly dynamic mammalian liver epigenome. Cell Metab 16(6):833–845. https://doi.org/10.1016/j.cmet.2012.11.004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Koike N, Yoo SH, Huang HC, Kumar V, Lee C, Kim TK, Takahashi JS (2012) Transcriptional architecture and chromatin landscape of the core circadian clock in mammals. Science 338(6105):349–354. https://doi.org/10.1126/science.1226339

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Fullwood MJ, Liu MH, Pan YF, Liu J, Xu H, Mohamed YB, Orlov YL, Velkov S, Ho A, Mei PH, Chew EG, Huang PY, Welboren WJ, Han Y, Ooi HS, Ariyaratne PN, Vega VB, Luo Y, Tan PY, Choy PY, Wansa KD, Zhao B, Lim KS, Leow SC, Yow JS, Joseph R, Li H, Desai KV, Thomsen JS, Lee YK, Karuturi RK, Herve T, Bourque G, Stunnenberg HG, Ruan X, Cacheux-Rataboul V, Sung WK, Liu ET, Wei CL, Cheung E, Ruan Y (2009) An oestrogen-receptor-alpha-bound human chromatin interactome. Nature 462(7269):58–64. https://doi.org/10.1038/nature08497

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Heinz S, Romanoski CE, Benner C, Glass CK (2015) The selection and function of cell type-specific enhancers. Nat Rev Mol Cell Biol 16(3):144–154. https://doi.org/10.1038/nrm3949

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Jager J, Wang F, Fang B, Lim HW, Peed LC, Steger DJ, Won KJ, Kharitonenkov A, Adams AC, Lazar MA (2016) The nuclear receptor rev-erbalpha regulates adipose tissue-specific FGF21 signaling. J Biol Chem 291(20):10867–10875. https://doi.org/10.1074/jbc.M116.719120

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Hong S, Zhou W, Fang B, Lu W, Loro E, Damle M, Ding G, Jager J, Zhang S, Zhang Y, Feng D, Chu Q, Dill BD, Molina H, Khurana TS, Rabinowitz JD, Lazar MA, Sun Z (2017) Dissociation of muscle insulin sensitivity from exercise endurance in mice by HDAC3 depletion. Nat Med 23(2):223–234. https://doi.org/10.1038/nm.4245

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

We thank Romeo Papazyan for careful reading of the manuscript. Work on circadian rhythms and GRO-seq in the Lazar lab is funded by NIH grants DK45586 and DK43806, as well as by the JPB Foundation.

Author information

Author notes

  1. Bin Fang

    Present address: Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA

  2. Bin Fang and Dongyin Guan contributed equally to this work.

Authors and Affiliations

  1. Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

    Bin Fang, Dongyin Guan & Mitchell A. Lazar

  2. The Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

    Bin Fang & Mitchell A. Lazar

Authors
  1. Bin Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dongyin Guan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mitchell A. Lazar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mitchell A. Lazar .

Editor information

Editors and Affiliations

  1. Chronobiology and Sleep Research Group, Institute of Pharmacology and Toxicology, University of Zurich, Zürich, Switzerland

    Steven A. Brown

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Fang, B., Guan, D., Lazar, M.A. (2021). Using GRO-Seq to Measure Circadian Transcription and Discover Circadian Enhancers. In: Brown, S.A. (eds) Circadian Clocks. Methods in Molecular Biology, vol 2130. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0381-9_10

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-0381-9_10

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0380-2

  • Online ISBN: 978-1-0716-0381-9

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation