Advertisement

Identification of Brassinosteroid Target Genes by Chromatin Immunoprecipitation Followed by High-Throughput Sequencing (ChIP-seq) and RNA-Sequencing

  • Trevor Nolan
  • Sanzhen Liu
  • Hongqing Guo
  • Lei Li
  • Patrick Schnable
  • Yanhai Yin
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1564)

Abstract

Brassinosteroids (BRs) play important roles in many growth and developmental processes. BRs signal to regulate BR-INSENSITIVE1-ETHYL METHANESULFONATE-SUPPRESSOR1 (BES1) and BRASSINAZOLE-RESISTANT1 (BZR1) transcription factors (TFs), which, in turn, regulate several hundreds of transcription factors (termed BES1/BZR1-targeted TFs or BTFs) and thousands of genes to mediate various BR responses. Chromatin Immunoprecipitation followed by high-throughput sequencing (ChIP-seq) with BES1/BZR1 and BTFs is an important approach to identify BR target genes. In combination with RNA-sequencing experiments, these genomic methods have become powerful tools to detect BR target genes and reveal transcriptional networks underlying BR-regulated processes.

Key words

Transcription factor Target genes ChIP-seq RNA-seq Gene expression 

Notes

Acknowledgments

We thank Iowa State University DNA Facility for allowing us to use the Diagenode Bioruptor and Mike Baker for information on ChIP-seq product amplification. The work is supported by grant from NSF (IOS-1257631) and by the Plant Science Institute at Iowa State University.

References

  1. 1.
    Clouse SD (2011) Brassinosteroid signal transduction: from receptor kinase activation to transcriptional networks regulating plant development. Plant Cell 23:1219–1230CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Li J, Chory J (1999) Brassinosteroid actions in plants. J Exp Bot 50:275–282Google Scholar
  3. 3.
    Guo H, Li L, Aluru M et al (2013) Mechanisms and networks for brassinosteroid regulated gene expression. Curr Opin Plant Biol 16:545–553CrossRefPubMedGoogle Scholar
  4. 4.
    Zhu J-Y, Sae-Seaw J, Wang Z-Y (2013) Brassinosteroid signalling. Development 140:1615–1620CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Jiang J, Zhang C, Wang X (2013) Ligand perception, activation, and early signaling of plant steroid receptor brassinosteroid insensitive 1. J Integr Plant Biol 55:1198–1211CrossRefPubMedGoogle Scholar
  6. 6.
    Vriet C, Russinova E, Reuzeau C (2013) From squalene to brassinolide: the steroid metabolic and signaling pathways across the plant kingdom. Mol Plant 6:1738–1757CrossRefPubMedGoogle Scholar
  7. 7.
    Yu X, Li L, Zola J et al (2011) A brassinosteroid transcriptional network revealed by genome-wide identification of BESI target genes in Arabidopsis thaliana. Plant J 65:634–646CrossRefPubMedGoogle Scholar
  8. 8.
    Sun Y, Fan X-Y, Cao D-M et al (2010) Integration of brassinosteroid signal transduction with the transcription network for plant growth regulation in Arabidopsis. Dev Cell 19:765–777CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Mundade R, Ozer HG, Wei H et al (2014) Role of ChIP-seq in the discovery of transcription factor binding sites, differential gene regulation mechanism, epigenetic marks and beyond. Cell Cycle 13:2847–2852CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Soon WW, Hariharan M, Snyder MP (2013) High-throughput sequencing for biology and medicine. Mol Syst Biol 9:640CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Wang X, Chen J, Xie Z et al (2014) Histone lysine methyltransferase SDG8 is involved in brassinosteroid-regulated gene expression in Arabidopsis thaliana. Mol Plant 7:1303–1315CrossRefPubMedGoogle Scholar
  12. 12.
    Li Y, Mukherjee I, Thum KE et al (2015) The histone methyltransferase SDG8 mediates the epigenetic modification of light and carbon responsive genes in plants. Genome Biol 16:79CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Para A, Li Y, Marshall-Colón A et al (2014) Hit-and-run transcriptional control by bZIP1 mediates rapid nutrient signaling in Arabidopsis. Proc Natl Acad Sci USA 111:10371–10376CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Lau OS, Bergmann DC (2015) MOBE-ChIP: a large-scale chromatin immunoprecipitation assay for cell type-specific studies. Plant J 84:443–450CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Nelson JD, Denisenko O, Sova P et al (2006) Fast chromatin immunoprecipitation assay. Nucleic Acids Res 34:e2CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Gendrel AV, Lippman Z, Yordan C et al (2002) Dependence of heterochromatic histone H3 methylation patterns on the Arabidopsis gene DDM1. Science 297:1871–1873CrossRefPubMedGoogle Scholar
  17. 17.
    Chen C, Khaleel SS, Huang H et al (2014) Software for pre-processing Illumina next-generation sequencing short read sequences. Source Code Biol Med 9:8CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30:2114–2120CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Wu TD, Nacu S (2010) Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics 26:873–881CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Dobin A, Davis CA, Schlesinger F et al (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29:15–21CrossRefPubMedGoogle Scholar
  21. 21.
    Trapnell C, Pachter L, Salzberg SL (2009) TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25:1105–1111CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25:1754–1760CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Langmead B (2010) Aligning short sequencing reads with Bowtie. Curr Protoc Bioinformatics 32 Unit 11.7:1–14Google Scholar
  24. 24.
    Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9:357–359CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Li H, Handsaker B, Wysoker A et al (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078–2079CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Robinson JT, Thorvaldsdóttir H, Winckler W, Guttman M, Lander ES, Getz G, Mesirov JP (2011) Integrative genomics viewer. Nat Biotechnol 29:24–26CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Anders S, Pyl PT, Huber W (2015) HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics 31:166–169CrossRefPubMedGoogle Scholar
  28. 28.
    Mortazavi A, Williams BA, McCue K et al (2008) Mapping and quantifying mammalian transcriptomes by RNA-seq. Nat Methods 5:621–628CrossRefPubMedGoogle Scholar
  29. 29.
    Trapnell C, Williams BA, Pertea G et al (2010) Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 28:511–515CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11:R106CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139–140CrossRefPubMedGoogle Scholar
  33. 33.
    Trapnell C, Hendrickson DG, Sauvageau M et al (2013) Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat Biotechnol 31:46–53CrossRefPubMedGoogle Scholar
  34. 34.
    Hardcastle TJ, Kelly KA (2010) baySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics 11:422CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B-Stat Methodol 57:289–300Google Scholar
  36. 36.
    Storey JD (2002) A direct approach to false discovery rates. J Roy Stat Soc Ser B-Stat Methodol 64:479–498CrossRefGoogle Scholar
  37. 37.
    Young MD, Wakefield MJ, Smyth GK et al (2010) Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol 11:R14CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Thimm O, Bläsing O, Gibon Y et al (2004) MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J 37:914–939CrossRefPubMedGoogle Scholar
  39. 39.
    Diaz A, Nellore A, Song JS (2012) CHANCE: comprehensive software for quality control and validation of ChIP-seq data. Genome Biol 13:R98CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Zhang Y, Liu T, Meyer CA et al (2008) Model-based analysis of ChIP-Seq (MACS). Genome Biol 9:R137CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Ji H, Jiang H, Ma W et al (2008) An integrated software system for analyzing ChIP-chip and ChIP-seq data. Nat Biotechnol 26:1293–1300CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Kharchenko PV, Tolstorukov MY, Park PJ (2008) Design and analysis of ChIP-seq experiments for DNA-binding proteins. Nat Biotechnol 26:1351–1359CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Muiño JM, Kaufmann K, van Ham RCHJ et al (2011) ChIP-seq analysis in R (CSAR): an R package for the statistical detection of protein-bound genomic regions. Plant Methods 7:11CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Rashid NU, Giresi PG, Ibrahim JG et al (2011) ZINBA integrates local covariates with DNA-seq data to identify broad and narrow regions of enrichment, even within amplified genomic regions. Genome Biol 12:R67CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Qin Z, Yu J, Shen J et al (2010) HPeak: an HMM-based algorithm for defining read-enriched regions in ChIP-Seq data. BMC Bioinformatics 11:369CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Spyrou C, Stark R, Lynch AG et al (2009) BayesPeak: Bayesian analysis of ChIP-seq data. BMC Bioinformatics 10:299CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Zang C, Schones DE, Zeng C et al (2009) A clustering approach for identification of enriched domains from histone modification ChIP-Seq data. Bioinformatics 25:1952–1958CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Xu H, Handoko L, Wei X et al (2010) A signal-noise model for significance analysis of ChIP-seq with negative control. Bioinformatics 26:1199–1204CrossRefPubMedGoogle Scholar
  49. 49.
    Song Q, Smith AD (2011) Identifying dispersed epigenomic domains from ChIP-Seq data. Bioinformatics 27:870–871CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    Bailey T, Krajewski P, Ladunga I et al (2013) Practical guidelines for the comprehensive analysis of ChIP-seq data. PLoS Comput Biol 9:e1003326. doi: 10.1371/journal.pcbi.1003326 CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Li Q, Brown JB, Huang H et al (2011) Measuring reproducibility of high-throughput experiments. Ann Appl Stat 5:1752–1779CrossRefGoogle Scholar
  52. 52.
    Furey TS (2012) ChIP-seq and Beyond: new and improved methodologies to detect and characterize protein-DNA interactions. Nat Rev Genet 13:840–852CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  • Trevor Nolan
    • 1
  • Sanzhen Liu
    • 2
  • Hongqing Guo
    • 1
  • Lei Li
    • 1
    • 3
  • Patrick Schnable
    • 4
  • Yanhai Yin
    • 1
  1. 1.Department of Genetics, Development and Cell BiologyIowa State UniversityAmesUSA
  2. 2.Department of Plant PathologyKansas State UniversityManhattanUSA
  3. 3.Harvard Medical SchoolHarvard UniversityBostonUSA
  4. 4.Department of AgronomyIowa State UniversityAmesUSA

Personalised recommendations