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Genomics of Gene Regulation

Genomics of Gene Regulation. Genomic and Proteomic Approaches to Heart, Lung, Blood and Sleep Disorders Jackson Laboratories Ross Hardison October 6, 2010. Heritable variation in gene regulation. “Simple” Mendelian traits, e.g. thalassemias

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Genomics of Gene Regulation

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  1. Genomics of Gene Regulation Genomic and Proteomic Approaches to Heart, Lung, Blood and Sleep Disorders Jackson Laboratories Ross Hardison October 6, 2010

  2. Heritable variation in gene regulation “Simple” Mendelian traits, e.g. thalassemias Variation in expression is common in normal individuals Variation in expression may be a major contributor to complex traits (including heart, lung, blood and sleep disorders)

  3. Deletions of noncoding DNA can affect gene expression Forget and Hardison, Chapter in Disorders of Hemoglobin, 2nd edition

  4. Substitutions in promoters can affect expression Forget and Hardison, Chapter in Disorders of Hemoglobin, 2nd edition

  5. Variation of gene expression among individuals • Levels of expression of many genes vary in humans (and other species) • Variation in expression is heritable • Determinants of variability map to discrete genomic intervals • Often multiple determinants • This variation indicates an abundance of cis-regulatory variation in the human genome • "We predict that variants in regulatory regions make a greater contribution to complex disease than do variants that affect protein sequence" Manolis Dermitzakis, ScienceDaily • Microarray expression analyses of 3554 genes in 14 families • Morley M … Cheung VG (2004) Nature 430:743-747 • Expression analysis of EBV-transformed lymphoblastoid cells from all 270 individuals genotypes in HapMap • Stranger BE … Dermitzakis E (2007) Nature Genetics 39:1217-1224

  6. Risk loci in noncoding regions (2007) Science 316: 1336-1341

  7. DNA sequences involved in regulation of gene transcription Protein-DNA interactions Chromatin effects

  8. Distinct classes of regulatory regions Act in cis, affecting expression of a gene on the same chromosome. Cis-regulatory modules (CRMs) Maston G, Evans S and Green M (2006) Annu Rev Genomics Hum Genetics 7:29-59

  9. RNA polymerase II TATA box + Initiator: Core or minimal promoter. Site of assembly of preinitiation complex Upstream regulatory elements: Regulate efficiency of utilization of minimal promoter Maston, Evans & Green (2006) Ann Rev Genomics & Human Genetics, 7:29-59 General features of promoters • A promoter is the DNA sequence required for correct initiation of transcription • Most promoters are at the 5’ end of the gene.

  10. Conventional view of eukaryotic gene promoters Maston, Evans & Green (2006) Ann Rev Genomics & Human Genetics, 7:29-59

  11. CpG island, no TATA 80-90% of promoters Carninci … Hayashizaki (2006) Nature Genetics 38:626 Most promoters in mammals are CpG islands TATA, no CpG island 10-20% of promoters

  12. Differences in specificity of start sites for transcription for TATA vs CpG island promoters Fraction of mRNAs Carninci … Hayashizaki (2006) Nature Genetics 38:626

  13. CRM pr luciferase UCE pr lacZ Tested UCE Over half of ultraconserved noncoding sequences are developmental enhancers Pennacchio et al. (2006) Nature 444:499-502 Pennacchio et al., http://enhancer.lbl.gov/ About half of the enhancers predicted by interspecies alignments are validated in erythroid cells Wang et al. (2006) Genome Research 16:1480- 1492 Enhancers • Cis-acting sequences that cause an increase in expression of a gene • Act independently of position and orientation with respect to the gene.

  14. CRMs are clusters of specific binding sites for transcription factors Hardison (2002) on-line textbook Working with Molecular Genetics http://www.bx.psu.edu/~ross/

  15. me3 K27 H3 N-tail Repression by PcG proteins via chromatin modification Polycomb Group (PcG) Repressor Complex 2: ESC, E(Z), NURF-55, and PcG repressor SU(Z)12 Methylates K27 of Histone H3 via the SET domain of E(Z) OFF

  16. trx group (trxG) proteins activate via chromatin changes • SWI/SNF nucleosome remodeling • Histone H3 and H4 acetylation • Methylation of K4 in histone H3 • Trx in Drosophila, MLL in humans • http://www.igh.cnrs.fr/equip/cavalli/link.PolycombTeaching.html#Part_3 Me1,2,3 K4 ON H3 N-tail

  17. Features interrogated by ChIP-seq and RNA-seq assays DNase hypersensitive sites CTCF

  18. Chromatin immunoprecipitation: Greatly enrich for DNA occupied by a protein Elaine Mardis (2007) Nature Methods 4: 613-614

  19. Enrichment of sequence tags reveals function Barbara Wold & Richard M Myers (2008) “Sequence Census Methods” Nature Methods 5:19-21

  20. Illumina (Solexa) short read sequencing - 8 lanes per run - 10 M to 20 M reads of 36 nucleotides (or longer) per run. - 1 lane can produce enough reads to map locations of a transcription factor in a mammalian genome.

  21. Example of ChIP-seq ChIP vs NRSF = neuron-restrictive silencing factor Jurkat human lymphoblast line NPAS4 encodes neuronal PAS domain protein 4 Johnson DS, Mortazavi A, Myers RM, Wold B. (2007) Genome-Wide Mapping of in Vivo Protein-DNA Interactions. Science316:1497-1502.

  22. Distinctive histone modifications and protein binding at promoters and enhancers • Promoters • H3K4me3, H3K4me2 • RNA Pol II • Enhancers • H3K4me1 • P300 coactivator Heintzman …Ren (2007) Nature Genetics 39:311-308; Birney et al. (2007) Nature, 447:799-816

  23. Genomic features at T2D risk variants Overlap of risk associated variants with DHSs and other epigenetic features suggest a role in transcriptional regulation. Overlap with an exon of a noncoding RNA suggests a role in post-transcriptionalregulation. Different hypotheses to test in future work. UCSC Genome Browser, Regulation tracks, ENCODE http://genome.ucsc.edu/

  24. Variants in 8q24 associated with cancer risk UCSC Genome Browser, Regulation tracks, ENCODE http://genome.ucsc.edu/

  25. Factor occupancy at cancer-associated variant UCSC Genome Browser, Regulation tracks, ENCODE http://genome.ucsc.edu/

  26. Genomics of Erythroid Gene Regulation

  27. Hematopoiesis

  28. GATA1, partners, teammates

  29. add back GATA-1, hybrid protein with ER G1E-ER4 estradiol Differentiated erythroblasts G1E-ER4+estradiol Somatic cell model to study GATA1 function in vitro hematopoietic differentiation erythropoietin stem cell factor Gata1– ES cells immortalize G1E Erythroid progenitors BFU-e, CFU-e

  30. Restoration of GATA1 in G1E cells mimics many of the steps in erythroid differentiation

  31. Repress proliferative genes, induce differentiation genes

  32. Features interrogated by ChIP-seq and RNA-seq assays DNase hypersensitive sites CTCF

  33. ChIP-seq finds previously known distal CRMs: Hbb LCR Known CRMs Combine + - GATA1

  34. Active Discrete regions with activating and repressive chromatin modifications - + GATA1

  35. Chromatin state distinguishes on from off, not induction from repression Constitutive hetero- chromatin? Facultative hetero- chromatin? Dynamic chromatin but mostly repressed Euchromatin

  36. GATA1 activates Zfpm1 by displacing GATA2 and retaining TAL1

  37. GATA1 represses Kit by displacing GATA2 and TAL1

  38. All GATA1-occupied segments active as enhancers are also occupied by SCL and LDB1

  39. Chromatin state precedes GATA1-induced TF changes Chromatin state established (mostly): Active Repressed Dead zones Chromatin condenses Nucleus removed Induction and repression: Dynamics of transcription factor binding within the already-established chromatin context.

  40. Binding site motifs in occupied DNA segments can be deeply preserved during evolution Consensus binding site motif for GATA-1: WGATAR or YTATCW 5997 constrained 7308 not constrained 2055 no motif

  41. GATA1-occupied segments conserved between mouse and human are tissue-specific enhancers Collaboration with Len Pennacchio, Hardison lab, ENCODE

  42. Summary: Genomics of Gene Regulation • Genetic determinants of variation in expression levels may contribute to complex traits - phenotype is not just determined by coding regions • Biochemical features associated with cis-regulatory modules are being determined genome-wide for a range of cell types. • These can be used to predict CRMs, but occupancy alone does not necessarily mean that the DNA is actively involved in regulation. • Genome-wide data on biochemical signatures of functional sequences (DHS, chromatin modifications, transcription factor occupancy, transcripts, etc.) provide candidates for explaining how variants in noncoding regions contribute to phenotypes

  43. Thanks Francesca Chiaromonte Weisheng Wu, Yong Cheng, Demesew Abebe, Cheryl Keller Capone,Ying Zhang, Ross, Swathi Ashok Kumar, Christine Dorman, David King ….Tejaswini Mishra, Nergiz Dogan, Chris Morrissey, Deepti Jain Collaborating labs: Mitch Weiss and Gerd Blobel (Childrens’ Hospital of Philadelphia), James Taylor (Emory) Webb Miller, Francesca Chiaromonte, Yu Zhang, Stephan Schuster, Frank Pugh, Bob Paulson (PSU), Greg Crawford (Duke), Jason Ernst, Manolis Kellis (MIT) Funding: NIH NIDDK, NHGRI (ARRA), Huck Institutes of Life Sciences and Institute for Cyberscience, PSU

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