Skip to main content
SpringerLink
Log in

High-Dimensional Data Approaches to Understanding Nuclear Hormone Receptor Signaling

  • Protocol
  • First Online:
Nuclear Receptors

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1966))

Abstract

Bioinformatics applies unbiased approaches to develop statistically robust insight into health and disease. At the global, or “20,000 ft” view bioinformatic analyses of NR signaling can measure how the NRs are implicated in human health and disease through the impact of genome-wide significant genetic variation, family-wide NR expression patterns or considering where NRs are significantly identified in other high-dimensional data analyses. With a more NR-centric, or “2000 ft” view, bioinformatic approaches can interrogate events downstream of a given NR. Integrative approaches aim to combine multiple NR-centric high-dimensional data both derived in cell models and primary human tissue to reveal how NR-transcriptional networks relate to human health and disease. Bioinformatic approaches to such high-dimensional data are central and require specialist statistical insight and computational skills, coupled with a dexterous understanding of the biological question. A current challenge is determining the optimal mechanism to share such bioinformatic approaches through the biological research community.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
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. Fodor SP et al (1993) Multiplexed biochemical assays with biological chips. Nature 364(6437):555–556

    Article  CAS  PubMed  Google Scholar 

  2. Welford SM et al (1998) Detection of differentially expressed genes in primary tumor tissues using representational differences analysis coupled to microarray hybridization. Nucleic Acids Res 26(12):3059–3065

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Brazma A et al (2001) Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet 29(4):365–371

    Article  CAS  PubMed  Google Scholar 

  4. R Core Team (2018) R: a language and environment for statistical computing. R4, 2018. Foundation for Statistical Computing. https://www.R-project.org/

  5. Gentleman RC et al (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5(10):R80

    Article  PubMed  PubMed Central  Google Scholar 

  6. Gentalen E, Chee M (1999) A novel method for determining linkage between DNA sequences: hybridization to paired probe arrays. Nucleic Acids Res 27(6):1485–1491

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Eads CA et al (2000) MethyLight: a high-throughput assay to measure DNA methylation. Nucleic Acids Res 28(8):E32

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Humphery-Smith I, Blackstock W (1997) Proteome analysis: genomics via the output rather than the input code. J Protein Chem 16(5):537–544

    Article  CAS  PubMed  Google Scholar 

  9. Tsugawa H (2018) Advances in computational metabolomics and databases deepen the understanding of metabolisms. Curr Opin Biotechnol 54:10–17

    Article  CAS  PubMed  Google Scholar 

  10. Orlando V, Paro R (1993) Mapping polycomb-repressed domains in the bithorax complex using in vivo formaldehyde cross-linked chromatin. Cell 75(6):1187–1198

    Article  CAS  PubMed  Google Scholar 

  11. Mockler TC et al (2005) Applications of DNA tiling arrays for whole-genome analysis. Genomics 85(1):1–15

    Article  CAS  PubMed  Google Scholar 

  12. Robertson G et al (2007) Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nat Methods 4(8):651–657

    Article  CAS  PubMed  Google Scholar 

  13. Paul J (1981) Sir George Beatson and the Royal Beatson Memorial Hospital. Med Hist 25(2):200–201

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Kerr JF, Wyllie AH, Currie AR (1972) Apoptosis: a basic biological phenomenon with wide-ranging implications in tissue kinetics. Br J Cancer 26(4):239–257

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Gorski J, Gannon F (1976) Current models of steroid hormone action: a critique. Annu Rev Physiol 38:425–450

    Article  CAS  PubMed  Google Scholar 

  16. Yamamoto K, Alberts B (1975) The interaction of estradiol-receptor protein with the genome: an argument for the existence of undetected specific sites. Cell 4(4):301–310

    Article  CAS  PubMed  Google Scholar 

  17. Weinberger C et al (1985) Identification of human glucocorticoid receptor complementary DNA clones by epitope selection. Science 228(4700):740–742

    Article  CAS  PubMed  Google Scholar 

  18. Green S et al (1986) Human oestrogen receptor cDNA: sequence, expression and homology to v-erb-A. Nature 320(6058):134–139

    Article  CAS  PubMed  Google Scholar 

  19. Weinberger C et al (1986) The c-erb-A gene encodes a thyroid hormone receptor. Nature 324(6098):641–646

    Article  CAS  PubMed  Google Scholar 

  20. McDonnell DP et al (1987) Molecular cloning of complementary DNA encoding the avian receptor for vitamin D. Science 235(4793):1214–1217

    Article  CAS  PubMed  Google Scholar 

  21. Petkovich M et al (1987) A human retinoic acid receptor which belongs to the family of nuclear receptors. Nature 330(6147):444–450

    Article  CAS  PubMed  Google Scholar 

  22. Giguere V et al (1987) Identification of a receptor for the morphogen retinoic acid. Nature 330(6149):624–629

    Article  CAS  PubMed  Google Scholar 

  23. Reichel RR, Jacob ST (1993) Control of gene expression by lipophilic hormones. FASEB J 7(5):427–436

    Article  CAS  PubMed  Google Scholar 

  24. Galon J et al (2002) Gene profiling reveals unknown enhancing and suppressive actions of glucocorticoids on immune cells. FASEB J 16(1):61–71

    Article  CAS  PubMed  Google Scholar 

  25. Lyakhovich A et al (2000) Vitamin D induced up-regulation of keratinocyte growth factor (FGF-7/KGF) in MCF-7 human breast cancer cells. Biochem Biophys Res Commun 273(2):675–680

    Article  CAS  PubMed  Google Scholar 

  26. Rusiniak ME et al (2000) Identification of B94 (TNFAIP2) as a potential retinoic acid target gene in acute promyelocytic leukemia. Cancer Res 60(7):1824–1829

    CAS  PubMed  Google Scholar 

  27. Elek J, Park KH, Narayanan R (2000) Microarray-based expression profiling in prostate tumors. In Vivo 14(1):173–182

    CAS  PubMed  Google Scholar 

  28. Yang GP et al (1999) Combining SSH and cDNA microarrays for rapid identification of differentially expressed genes. Nucleic Acids Res 27(6):1517–1523

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Chatterjee S et al (2016) Enhancer variants synergistically drive dysfunction of a gene regulatory network in Hirschsprung disease. Cell 167(2):355–368. e10

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Eckel-Mahan KL et al (2013) Reprogramming of the circadian clock by nutritional challenge. Cell 155(7):1464–1478

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Mikkelsen TS et al (2010) Comparative epigenomic analysis of murine and human adipogenesis. Cell 143(1):156–169

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Maret S et al (2005) Retinoic acid signaling affects cortical synchrony during sleep. Science 310(5745):111–113

    Article  CAS  PubMed  Google Scholar 

  33. Cancer Genome Atlas Research Network (2015) The molecular taxonomy of primary prostate cancer. Cell 163(4):1011–1025

    Article  CAS  Google Scholar 

  34. Ciriello G et al (2015) Comprehensive molecular portraits of invasive lobular breast cancer. Cell 163(2):506–519

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Sudlow C et al (2015) UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 12(3):e1001779

    Article  PubMed  PubMed Central  Google Scholar 

  36. Cancer Genome Atlas Research Network (2008) Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455(7216):1061–1068

    Article  CAS  Google Scholar 

  37. Cerami E et al (2012) The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2(5):401–404

    Article  PubMed  Google Scholar 

  38. van Dijk EL, Jaszczyszyn Y, Thermes C (2014) Library preparation methods for next-generation sequencing: tone down the bias. Exp Cell Res 322(1):12–20

    Article  PubMed  CAS  Google Scholar 

  39. Ren X et al (2017) Gene set analysis controlling for length bias in RNA-seq experiments. BioData Min 10:5

    Article  PubMed  PubMed Central  Google Scholar 

  40. Poirion OB et al (2016) Single-cell transcriptomics bioinformatics and computational challenges. Front Genet 7:163

    Article  PubMed  PubMed Central  Google Scholar 

  41. Choi SH et al (2017) Evaluation of logistic regression models and effect of covariates for case-control study in RNA-Seq analysis. BMC Bioinformatics 18(1):91

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15(12):550

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Kumar D et al (2016) Integrating transcriptome and proteome profiling: strategies and applications. Proteomics 16(19):2533–2544

    Article  CAS  PubMed  Google Scholar 

  44. MacArthur J et al (2017) The new NHGRI-EBI catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res 45(D1):D896–D901

    Article  CAS  PubMed  Google Scholar 

  45. Welter D et al (2014) The NHGRI GWAS catalog, a curated resource of SNP-trait associations. Nucleic Acids Res 42(Database issue):D1001–D1006

    Article  CAS  PubMed  Google Scholar 

  46. McKay JD et al (2017) Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes. Nat Genet 49(7):1126–1132

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Kote-Jarai Z et al (2011) Seven prostate cancer susceptibility loci identified by a multi-stage genome-wide association study. Nat Genet 43(8):785–791

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Willer CJ et al (2013) Discovery and refinement of loci associated with lipid levels. Nat Genet 45(11):1274–1283

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Sabatti C et al (2009) Genome-wide association analysis of metabolic traits in a birth cohort from a founder population. Nat Genet 41(1):35–46

    Article  CAS  PubMed  Google Scholar 

  50. Jostins L et al (2012) Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491(7422):119–124

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Shungin D et al (2015) New genetic loci link adipose and insulin biology to body fat distribution. Nature 518(7538):187–196

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Gelernter J et al (2014) Genome-wide association study of cocaine dependence and related traits: FAM53B identified as a risk gene. Mol Psychiatry 19(6):717–723

    Article  CAS  PubMed  Google Scholar 

  53. Boyle AP et al (2012) Annotation of functional variation in personal genomes using RegulomeDB. Genome Res 22(9):1790–1797

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Djebali S et al (2012) Landscape of transcription in human cells. Nature 489(7414):101–108

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Roadmap Epigenomics C et al (2015) Integrative analysis of 111 reference human epigenomes. Nature 518(7539):317–330

    Article  CAS  Google Scholar 

  56. Gallone G et al (2017) Identification of genetic variants affecting vitamin D receptor binding and associations with autoimmune disease. Hum Mol Genet 26(11):2164–2176

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Singh PK et al (2017) Integration of VDR genome wide binding and GWAS genetic variation data reveals co-occurrence of VDR and NF-kappaB binding that is linked to immune phenotypes. BMC Genomics 18(1):132

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Long MD et al (2015) Integrative genomic analysis in K562 chronic myelogenous leukemia cells reveals that proximal NCOR1 binding positively regulates genes that govern erythroid differentiation and Imatinib sensitivity. Nucleic Acids Res 43(15):7330–7348

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Long MD et al (2018) The miR-96 and RARγ signaling axis governs androgen signaling and prostate cancer progression. Oncogene. 2018 Aug 17. https://doi.org/10.1038/s41388-018-0450-6. [Epub ahead of print] PubMed PMID: 30120411

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Gao J et al (2013) Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 6(269):pl1

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Taylor BS et al (2010) Integrative genomic profiling of human prostate cancer. Cancer Cell 18(1):11–22

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Long MD, Campbell MJ (2015) Pan-cancer analyses of the nuclear receptor superfamily. Nucl Receptor Res 2:pii: 101182

    Article  Google Scholar 

  63. Long MD et al (2014) Cooperative behavior of the nuclear receptor superfamily and its deregulation in prostate cancer. Carcinogenesis 35(2):262–271

    Article  CAS  PubMed  Google Scholar 

  64. Cancer Genome Atlas Research Network (2014) Comprehensive molecular characterization of urothelial bladder carcinoma. Nature 507(7492):315–322

    Article  CAS  Google Scholar 

  65. Cancer Genome Atlas Network (2012) Comprehensive molecular portraits of human breast tumours. Nature 490(7418):61–70

    Article  CAS  Google Scholar 

  66. Cancer Genome Atlas Network (2012) Comprehensive molecular characterization of human colon and rectal cancer. Nature 487(7407):330–337

    Article  CAS  Google Scholar 

  67. Cancer Genome Atlas Network (2015) Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature 517(7536):576–582

    Article  CAS  Google Scholar 

  68. Ahn SM et al (2014) Genomic portrait of resectable hepatocellular carcinomas: implications of RB1 and FGF19 aberrations for patient stratification. Hepatology 60(6):1972–1982

    Article  CAS  PubMed  Google Scholar 

  69. Long MD, Campbell MJ (2017) Integrative genomic approaches to dissect clinically-significant relationships between the VDR cistrome and gene expression in primary colon cancer. J Steroid Biochem Mol Biol 173:130–138

    Article  CAS  PubMed  Google Scholar 

  70. Satelli A et al (2011) Galectin-4 functions as a tumor suppressor of human colorectal cancer. Int J Cancer 129(4):799–809

    Article  CAS  PubMed  Google Scholar 

  71. Belo AI et al (2013) Galectin-4 reduces migration and metastasis formation of pancreatic cancer cells. PLoS One 8(6):e65957

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Kim SW et al (2013) Abrogation of galectin-4 expression promotes tumorigenesis in colorectal cancer. Cell Oncol (Dordr) 36(2):169–178

    Article  CAS  Google Scholar 

  73. Tsai CH et al (2016) Metastatic progression of prostate cancer is mediated by autonomous binding of galectin-4-O-glycan to cancer cells. Cancer Res 76(19):5756–5767

    Article  CAS  PubMed  Google Scholar 

  74. Palmer HG et al (2004) The transcription factor SNAIL represses vitamin D receptor expression and responsiveness in human colon cancer. Nat Med 10(9):917–919

    Article  CAS  PubMed  Google Scholar 

  75. Pena C et al (2005) E-Cadherin and vitamin D receptor regulation by SNAIL and ZEB1 in colon cancer: clinicopathological correlations. Hum Mol Genet 14(22):3361–3370

    Article  CAS  PubMed  Google Scholar 

  76. Pereira F et al (2011) KDM6B/JMJD3 histone demethylase is induced by vitamin D and modulates its effects in colon cancer cells. Hum Mol Genet 20(23):4655–4665

    Article  CAS  PubMed  Google Scholar 

  77. Larriba MJ et al (2011) Vitamin D receptor deficiency enhances Wnt/beta-catenin signaling and tumor burden in colon cancer. PLoS One 6(8):e23524

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Wang W et al (2011) Rapid and efficient reprogramming of somatic cells to induced pluripotent stem cells by retinoic acid receptor gamma and liver receptor homolog 1. Proc Natl Acad Sci U S A 108(45):18283–18288

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Simandi Z et al (2016) OCT4 acts as an integrator of pluripotency and signal-induced differentiation. Mol Cell 63(4):647–661

    Article  CAS  PubMed  Google Scholar 

  80. Bryant SL et al (2014) Sex specific retinoic acid signaling is required for the initiation of urogenital sinus bud development. Dev Biol 395(2):209–217

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Lohnes D et al (1993) Function of retinoic acid receptor gamma in the mouse. Cell 73(4):643–658

    Article  CAS  PubMed  Google Scholar 

  82. Vezina CM et al (2008) Retinoic acid induces prostatic bud formation. Dev Dyn 237(5):1321–1333

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Mariotti A et al (1987) Actions and interactions of estradiol and retinoic acid in mouse anterior prostate gland. Biol Reprod 37(4):1023–1035

    Article  CAS  PubMed  Google Scholar 

  84. Mele M et al (2015) Human genomics. The human transcriptome across tissues and individuals. Science 348(6235):660–665

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Ecker JR et al (2017) The BRAIN initiative cell census consortium: lessons learned toward generating a comprehensive brain cell atlas. Neuron 96(3):542–557

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Palmer HG et al (2003) Genetic signatures of differentiation induced by 1alpha,25-dihydroxyvitamin D3 in human colon cancer cells. Cancer Res 63(22):7799–7806

    CAS  PubMed  Google Scholar 

  87. Koike M et al (1997) 19-nor-hexafluoride analogue of vitamin D3: a novel class of potent inhibitors of proliferation of human breast cell lines. Cancer Res 57(20):4545–4550

    CAS  PubMed  Google Scholar 

  88. Campbell MJ et al (1997) Inhibition of proliferation of prostate cancer cells by a 19-nor-hexafluoride vitamin D3 analogue involves the induction of p21waf1, p27kip1 and E-cadherin. J Mol Endocrinol 19(1):15–27

    Article  CAS  PubMed  Google Scholar 

  89. Elstner E et al (1999) Novel 20-epi-vitamin D3 analog combined with 9-cis-retinoic acid markedly inhibits colony growth of prostate cancer cells. Prostate 40(3):141–149

    Article  CAS  PubMed  Google Scholar 

  90. Peehl DM et al (1994) Antiproliferative effects of 1,25-dihydroxyvitamin D3 on primary cultures of human prostatic cells. Cancer Res 54(3):805–810

    CAS  PubMed  Google Scholar 

  91. Welsh J et al (2002) Impact of the vitamin D3 receptor on growth-regulatory pathways in mammary gland and breast cancer. J Steroid Biochem Mol Biol 83(1–5):85–92

    Article  CAS  PubMed  Google Scholar 

  92. Colston KW, Berger U, Coombes RC (1989) Possible role for vitamin D in controlling breast cancer cell proliferation. Lancet 1(8631):188–191

    Article  CAS  PubMed  Google Scholar 

  93. Colston K et al (1982) 1,25-dihydroxyvitamin D3 receptors in human epithelial cancer cell lines. Cancer Res 42(3):856–859

    CAS  PubMed  Google Scholar 

  94. Savli H et al (2002) Gene expression analysis of 1,25(OH)2D3-dependent differentiation of HL-60 cells: a cDNA array study. Br J Haematol 118(4):1065–1070

    Article  CAS  PubMed  Google Scholar 

  95. Akutsu N et al (2001) Regulation of gene expression by 1alpha,25-dihydroxyvitamin D3 and its analog EB1089 under growth-inhibitory conditions in squamous carcinoma cells. Mol Endocrinol 15(7):1127–1139

    CAS  PubMed  Google Scholar 

  96. Eelen G et al (2004) Microarray analysis of 1alpha,25-dihydroxyvitamin D3-treated MC3T3-E1 cells. J Steroid Biochem Mol Biol 89–90(1–5):405–407

    Article  PubMed  CAS  Google Scholar 

  97. Wang TT et al (2005) Large-scale in silico and microarray-based identification of direct 1,25-dihydroxyvitamin D3 target genes. Mol Endocrinol 19(11):2685–2695

    Article  CAS  PubMed  Google Scholar 

  98. Lin R et al (2002) Expression profiling in squamous carcinoma cells reveals pleiotropic effects of vitamin D3 analog EB1089 signaling on cell proliferation, differentiation, and immune system regulation. Mol Endocrinol 16(6):1243–1256

    Article  CAS  PubMed  Google Scholar 

  99. Ding N et al (2013) A vitamin D receptor/SMAD genomic circuit gates hepatic fibrotic response. Cell 153(3):601–613

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Heikkinen S et al (2011) Nuclear hormone 1alpha,25-dihydroxyvitamin D3 elicits a genome-wide shift in the locations of VDR chromatin occupancy. Nucleic Acids Res 39(21):9181–9193

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Meyer MB, Goetsch PD, Pike JW (2012) VDR/RXR and TCF4/beta-catenin cistromes in colonic cells of colorectal tumor origin: impact on c-FOS and c-MYC gene expression. Mol Endocrinol 26(1):37–51

    Article  CAS  PubMed  Google Scholar 

  102. Ramagopalan SV et al (2010) A ChIP-seq defined genome-wide map of vitamin D receptor binding: associations with disease and evolution. Genome Res 20(10):1352–1360

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Tuoresmaki P et al (2014) Patterns of genome-wide VDR locations. PLoS One 9(4):e96105

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  104. Raney BJ et al (2011) ENCODE whole-genome data in the UCSC genome browser (2011 update). Nucleic Acids Res 39(Database issue):D871–D875

    Article  CAS  PubMed  Google Scholar 

  105. Liao Y, Smyth GK, Shi W (2014) featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30(7):923–930

    Article  CAS  PubMed  Google Scholar 

  106. Lun AT, Smyth GK (2016) csaw: a Bioconductor package for differential binding analysis of ChIP-seq data using sliding windows. Nucleic Acids Res 44(5):e45

    Article  PubMed  CAS  Google Scholar 

  107. Zhang Y et al (2008) Model-based analysis of ChIP-Seq (MACS). Genome Biol 9(9):R137

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  108. Mei S et al (2017) Cistrome data browser: a data portal for ChIP-Seq and chromatin accessibility data in human and mouse. Nucleic Acids Res 45(D1):D658–D662

    Article  CAS  PubMed  Google Scholar 

  109. Hua S, Kittler R, White KP (2009) Genomic antagonism between retinoic acid and estrogen signaling in breast cancer. Cell 137(7):1259–1271

    Article  PubMed  PubMed Central  Google Scholar 

  110. Yang F et al (2017) Glucocorticoid receptor: MegaTrans switching mediates the repression of an ERalpha-regulated transcriptional program. Mol Cell 66(3):321–331. e6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Liu Z et al (2014) Enhancer activation requires trans-recruitment of a mega transcription factor complex. Cell 159(2):358–373

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Grebbin BM, Schulte D (2017) PBX1 as pioneer factor: a case still open. Front Cell Dev Biol 5:9

    Article  PubMed  PubMed Central  Google Scholar 

  113. Golson ML, Kaestner KH (2016) Fox transcription factors: from development to disease. Development 143(24):4558–4570

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Goldstein I, Hager GL (2018) Dynamic enhancer function in the chromatin context. Wiley Interdiscip Rev Syst Biol Med:10(1). https://doi.org/10.1002/wsbm.1390

    Google Scholar 

  115. Hager GL, McNally JG, Misteli T (2009) Transcription dynamics. Mol Cell 35(6):741–753

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Biddie SC, Hager GL (2009) Glucocorticoid receptor dynamics and gene regulation. Stress 12(3):193–205

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Mohammed H et al (2016) Rapid immunoprecipitation mass spectrometry of endogenous proteins (RIME) for analysis of chromatin complexes. Nat Protoc 11(2):316–326

    Article  CAS  PubMed  Google Scholar 

  118. Ernst J, Kellis M (2017) Chromatin-state discovery and genome annotation with ChromHMM. Nat Protoc 12(12):2478–2492

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Ernst J, Kellis M (2012) ChromHMM: automating chromatin-state discovery and characterization. Nat Methods 9(3):215–216

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Shaffer PL, Gewirth DT (2004) Structural analysis of RXR-VDR interactions on DR3 DNA. J Steroid Biochem Mol Biol 89-90(1–5):215–219

    Article  CAS  PubMed  Google Scholar 

  121. Sasaki H et al (1995) Transcriptional activity of a fluorinated vitamin D analog on VDR-RXR-mediated gene expression. Biochemistry 34(1):370–377

    Article  CAS  PubMed  Google Scholar 

  122. Carlberg C, Campbell MJ (2013) Vitamin D receptor signaling mechanisms: integrated actions of a well-defined transcription factor. Steroids 78(2):127–136

    Article  CAS  PubMed  Google Scholar 

  123. Birney E (2012) The making of ENCODE: lessons for big-data projects. Nature 489(7414):49–51

    Article  CAS  PubMed  Google Scholar 

  124. Sanli K et al (2013) FANTOM: functional and taxonomic analysis of metagenomes. BMC Bioinformatics 14:38

    Article  PubMed  PubMed Central  Google Scholar 

  125. Buenrostro JD et al (2015) ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr Protoc Mol Biol 109:21.291–21.299

    Article  Google Scholar 

  126. Jeselsohn R et al (2018) Allele-Specific Chromatin Recruitment and Therapeutic Vulnerabilities of ESR1 Activating Mutations. Cancer Cell 33(2):173–186. e5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Tarallo R et al (2017) The nuclear receptor ERbeta engages AGO2 in regulation of gene transcription, RNA splicing and RISC loading. Genome Biol 18(1):189

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  128. Hamed M et al (2017) Insights into interplay between rexinoid signaling and myogenic regulatory factor-associated chromatin state in myogenic differentiation. Nucleic Acids Res 45(19):11236–11248

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Armour SM et al (2017) An HDAC3-PROX1 corepressor module acts on HNF4alpha to control hepatic triglycerides. Nat Commun 8(1):549

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  130. Zhang Y et al (2017) The hepatic circadian clock fine-tunes the lipogenic response to feeding through RORalpha/gamma. Genes Dev 31(12):1202–1211

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Dreijerink KMA et al (2017) Enhancer-mediated oncogenic function of the menin tumor suppressor in breast cancer. Cell Rep 18(10):2359–2372

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Malinen M et al (2017) Crosstalk between androgen and pro-inflammatory signaling remodels androgen receptor and NF-kappaB cistrome to reprogram the prostate cancer cell transcriptome. Nucleic Acids Res 45(2):619–630

    Article  CAS  PubMed  Google Scholar 

  133. Savic D et al (2016) Distinct gene regulatory programs define the inhibitory effects of liver X receptors and PPARG on cancer cell proliferation. Genome Med 8(1):74

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  134. Taberlay PC et al (2016) Three-dimensional disorganization of the cancer genome occurs coincident with long-range genetic and epigenetic alterations. Genome Res 26(6):719–731

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. Fiorito E et al (2016) CTCF modulates estrogen receptor function through specific chromatin and nuclear matrix interactions. Nucleic Acids Res 44(22):10588–10602

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Mourad R et al (2014) Estrogen induces global reorganization of chromatin structure in human breast cancer cells. PLoS One 9(12):e113354

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  137. Wang J et al (2013) Genome-wide analysis uncovers high frequency, strong differential chromosomal interactions and their associated epigenetic patterns in E2-mediated gene regulation. BMC Genomics 14:70

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Kim YH et al (2018) Rev-erbalpha dynamically modulates chromatin looping to control circadian gene transcription. Science 359(6381):1274–1277

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. Neme A, Seuter S, Carlberg C (2016) Vitamin D-dependent chromatin association of CTCF in human monocytes. Biochim Biophys Acta 1859(11):1380–1388

    Article  CAS  PubMed  Google Scholar 

  140. Mifsud B et al (2017) GOTHiC, a probabilistic model to resolve complex biases and to identify real interactions in Hi-C data. PLoS One 12(4):e0174744

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  141. Lun AT, Perry M, Ing-Simmons E (2016) Infrastructure for genomic interactions: Bioconductor classes for Hi-C, ChIA-PET and related experiments. F1000Res 5:950

    Article  PubMed  PubMed Central  Google Scholar 

  142. Harmston N et al (2015) GenomicInteractions: An R/Bioconductor package for manipulating and investigating chromatin interaction data. BMC Genomics 16:963

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  143. Lun AT, Smyth GK (2015) diffHic: a bioconductor package to detect differential genomic interactions in Hi-C data. BMC Bioinformatics 16:258

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  144. Dayhoff MO (1969) Computer analysis of protein evolution. Sci Am 221(1):86–95

    Article  CAS  PubMed  Google Scholar 

  145. Dayhoff MO (1965) Computer aids to protein sequence determination. J Theor Biol 8(1):97–112

    Article  CAS  PubMed  Google Scholar 

  146. Hogeweg P (2011) The roots of bioinformatics in theoretical biology. PLoS Comput Biol 7(3):e1002021

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Roberts L et al (2001) A history of the Human Genome Project. Science 291(5507):1195

    Article  CAS  PubMed  Google Scholar 

  148. Consortium EP et al (2007) Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447(7146):799–816

    Article  CAS  Google Scholar 

  149. Bujold D et al (2016) The international human epigenome consortium data portal. Cell Syst 3(5):496–499. e2

    Article  CAS  PubMed  Google Scholar 

  150. Chen L et al (2016) Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell 167(5):1398–1414. e24

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  151. Cancer Genome Atlas Research Network et al (2013) The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45(10):1113–1120

    Article  CAS  PubMed Central  Google Scholar 

  152. Weaver W (1970) Molecular biology: origin of the term. Science 170(3958):581–582

    Article  CAS  PubMed  Google Scholar 

  153. Danna K, Nathans D (1971) Specific cleavage of simian virus 40 DNA by restriction endonuclease of Hemophilus influenzae. Proc Natl Acad Sci U S A 68(12):2913–2917

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  154. Saiki RK et al (1988) Primer-directed enzymatic amplification of DNA with a thermostable DNA polymerase. Science 239(4839):487–491

    Article  CAS  PubMed  Google Scholar 

  155. Hunkapiller T et al (1991) Large-scale and automated DNA sequence determination. Science 254(5028):59–67

    Article  CAS  PubMed  Google Scholar 

  156. Tavera-Mendoza LE et al (2008) Incorporation of histone deacetylase inhibition into the structure of a nuclear receptor agonist. Proc Natl Acad Sci U S A 105(24):8250–8255

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

M.J.C. acknowledges support in part from the Prostate program of the Department of Defense Congressionally Directed Medical Research Programs [W81XWH-14-1-0608].

Author information

Authors and Affiliations

  1. Division of Pharmaceutics and Pharmaceutical Chemistry, College of Pharmacy, The Ohio State University, Columbus, OH, USA

    Moray J. Campbell

Authors
  1. Moray J. Campbell
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Moray J. Campbell .

Editor information

Editors and Affiliations

  1. School of Pharmacy, University of Missouri-Kansas City, Kansas City, MO, USA

    Mostafa Z. Badr

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Campbell, M.J. (2019). High-Dimensional Data Approaches to Understanding Nuclear Hormone Receptor Signaling. In: Badr, M. (eds) Nuclear Receptors. Methods in Molecular Biology, vol 1966. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9195-2_23

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-9195-2_23

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9194-5

  • Online ISBN: 978-1-4939-9195-2

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation