Identification of Direct Targets of Plant Transcription Factors Using the GR Fusion Technique
The glucocorticoid receptor-dependent activation of plant transcription factors has proven to be a powerful tool for the identification of their direct target genes. In the absence of the synthetic steroid hormone dexamethasone (dex), transcription factors fused to the hormone-binding domain of the glucocorticoid receptor (TF-GR) are held in an inactive state, due to their cytoplasmic localization. This requires physical interaction with the heat shock protein 90 (HSP90) complex. Hormone binding leads to disruption of the interaction between GR and HSP90 and allows TF-GR fusion proteins to enter the nucleus. Once inside the nucleus, they bind to specific DNA sequences and immediately activate or repress expression of their targets. This system is well suited for the identification of direct target genes of transcription factors in plants, as (A) there is little basal protein activity in the absence of dex, (B) steroid application leads to rapid transcription factor activation, (C) no side effects of dex treatment are observed on the physiology of the plant, and (D) secondary effects of transcription factor activity can be eliminated by simultaneous application of an inhibitor of protein biosynthesis, cycloheximide (cyc). In this chapter, we describe detailed protocols for the preparation of plant material, for dex and cyc treatment, for RNA extraction, and for the PCR-based or genome-wide identification of direct targets of transcription factors fused to GR.
Key wordsDexamethasone Direct target Glucocorticoid receptor Heat shock protein 90 Inducible system Transcription factor
This work was supported by IOS grant 1257111 to D.W, JSPS postdoctoral fellowships for research abroad to N.Y., NIH Developmental Biology Training Grant T32-HD007516 and NIH Ruth L. Kirschstein NRSA F32 Fellowship GM106690-01 to C.M.W., and Science Foundation Ireland to F.W.
- 1.Locker J (2001) Transcription factors. Academic, San DiegoGoogle Scholar
- 17.Schena M, Lloyd AM, Davis RW (1991) A steroid-inducible gene expression system for plant cells. Proc Natl Acad Sci U S A 101:1775–1780Google Scholar
- 32.Reinhart BJ, Liu T, Newell NR, Magnani E, Huang T, Kerstetter R, Michaels S, Barton MK (2013) Establishing a framework for the Ad/abaxial regulatory network of Arabidopsis: ascertaining targets of class III homeodomain leucine zipper and KANADI regulation. Plant Cell 25:3228–3249CrossRefPubMedCentralPubMedGoogle Scholar
- 33.Eklund M, Staldal V, Valsecchi I, Clerlik I, Eriksson C, Hiratsu K, Ohme-Takagi M, Sunstrom JF, Thelander M, Ezcurra I, Sundberg E (2010) The Arabidopsis thaliana STYLISH1 protein acts as a transcriptional activator regulating auxin biosynthesis. Plant Cell 22:349–363CrossRefPubMedCentralPubMedGoogle Scholar
- 42.Smyth G (2004) Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3:Article 3Google Scholar
- 43.Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B 57:289–300Google Scholar
- 44.Wagner D, Sablowski RW (2001) Glucocorticoid fusions for transcription factor. In: Weigel D, Glazebrook J (eds) Arabidopsis—a laboratory manual. Cold Spring Harbor, Cold Spring Harbor Laboratory PressGoogle Scholar
- 45.Bolstad BM, Collin F, Brettschneider J, Simpson K, Cope L, Irizarry RA, Speed TP (2005) Quality assessment of Affymetrix GeneChip data. In: Gentleman R, Carey V, Huber W, Irizarry R, Dudoit S (eds) Bioinformatics and computational biology solutions using R and bioconductor, statistics for biology and health. Springer, New York, pp 33–47CrossRefGoogle Scholar