Epigenomics : methylation and histone modifications

1. Epigenomics: methylation and histone modifications Benjamin Rodriguez, PhD Wei Li and Peggy Goodell Labs Baylor College of Medicine Molecular Biology Refresher Course with Bioinformatics Sept 4th 2015

2. Software, Sites, Materials Course Materials: http://dldcc-web.brc.bcm.edu/lilab/benji/MBRB_2015/index.html Most up to date slides Supplementary materials Browsers: http://genome.ucsc.edu/ http://epigenomegateway.wustl.edu/ Web-based analysis: http://bejerano.stanford.edu/great/public/html/ http://david.abcc.ncifcrf.gov

3. Outline • DNA packaging and accessibility • DNA methylation • Histone modifications • Epigenetic inheritance in development and disease • Aberrant epigenetic changes in cancer

4. DNA is Packaged in Chromatin Chromatin consists of nucleosomes, DNA wrapped around histone proteins nucleosome histone DNA chromatin • Chromatin organizes genes to be accessible for transcription, replication, and repair

5. Unraveling the package Epigenetics as Lobbying Graphic from NIH RoadMapEpigenomics Site

6. Regulation of genes involved in differentiation, cell cycle, and cell survival Roles in Normal Development and Cancer EPIGENETICS Normal epigenetic mechanisms Differentiated cells Progenitor cell

7. Regulation of genes involved in differentiation, cell cycle, and cell survival Through epigenetic silencing of certain genes, affected cells may acquire new phenotypes which promote tumorigenesis Roles in Normal Development and Cancer EPIGENETICS Deregulated epigenetic mechanisms Normal epigenetic mechanisms Malignant progenitor cell Differentiated cells Progenitor cell Tumor

8. Epigenetic Mechanisms: DNA Methylation CG CG CG CG MCG MCG CG Normal 1 2 3 4 C: cytosine mC: methylcytosine

9. Epigenetic Mechanisms: DNA Methylation CpG island CG CG CG CG MCG MCG CG Normal 1 2 3 4 C: cytosine mC: methylcytosine

10. DNA Methylation and Gene Silencing CpG island CG CG CG CG MCG MCG CG Normal 1 2 3 4 MCG MCG MCG MCG CG CG CG Cancer 1 2 3 4 X C: cytosine mC: methylcytosine

11. Continuum of Methylation and Gene Expression Some genes (e.g. HOXB13 in breast cancer) show strong correlation of promoter methylation with expression R2 = 0.7817 P< 0.0005 Rodriguezet al. Carcinogenesis, 29(7), 1459-1465.

12. DNA Methylation and Regulation • Cytosine methylation blocks DNA-binding proteins’ access to regulatory sites and creates binding sites for repressive proteins • Methylation often follows decrease in site use Thurmanet tal. Nature, 489(7414), 75-82.

13. Methylation gets more complicated! • Many highly expressed genes have CpG methylation on their exons • Genomic imprinting (parent of origin DNA methylation) • Non-CpG cytosine bases are often methylated in embryonic stem cells • Hydroxymethylcytosine (5hmC) and demethylation

14. Methylation, Retroviruses and Repeats • Bacteria use DNA methylation to limit invasive DNA from viruses • A large fraction of the human genome consists of carcasses of retro-viruses and transposons • Almost all DNA repeats are heavily methylated • If they lose methylation they are more likely to be expressed

15. DNA Methylation and Development • Two major waves of germlinedemethylation • Increasing methylation at various times during fetal development restrict functionality • This is why cloning is difficult Leeet al.Cell stem cell, 14(6), 710-719 (2014)

16. DNA Methylation and Inheritance • Humans and mice show epigenetic inheritance apparently mediated by DNA methylation • Impact of nutritional and environmental influence on the fetal epigenome

17. Persistent epigenetic differences associated with prenatal exposure to famine in humans Individuals who were prenatally exposed to famine during the Dutch Hunger Winter in 1944–45 had, 6 decades later, less DNA methylation of the imprinted IGF2 gene compared with their unexposed, same-sex siblings Association was specific for periconceptional exposure, reinforcing that very early mammalian development is a crucial period for establishing and maintaining epigenetic marks Heijmans et al. (2008). PNAS, 105(44), 17046-17049.

18. Difference in IGF2 DMR methylation between individuals prenatally exposed to famine and their same-sex sibling Periconceptional exposure Exposure late in gestation Heijmans et al. (2008). PNAS, 105(44), 17046-17049.

19. Timing of famine exposure during gestation and IGF2 DMR methylation • Periconceptional exposure associated with lower methylation • Statistically significant association between timing and exposure Heijmans et al. (2008). PNAS, 105(44), 17046-17049.

20. DNA methylation as biomarker of disease • Specific methylation changes often correlated with clinical features • Potential for early detection, diagnosis, prognosis, therapeutic stratification and post-therapeutic monitoring

21. HOXB13hypermethylation associates with poor disease free survival in ERα-positive patients Rodriguezet al. Carcinogenesis, 29(7), 1459-1465.

22. Epigenetic Mechanisms: Post-Translational Modification to Histones Histone Acetylation Histone Methylation Ac Me • Epigenetic modifications of Histones include Histone Acetylation and Methylation

23. Histone Modifications • Different modifications at different locations by different enzymes • Potential temporal and spatial specificity

24. Histone Modifications • Gene body mark: H3K36me3, H3K79me2 • Active promoter (TSS) mark: H3K4me3 • Active enhancer (TF binding) mark: H3K4me1, H3K27ac • Both enhancers and promoters: H3K4me2, H3/H4ac, H2AZ • Repressive promoter mark: H3K27me3 • Repressive mark for DNA methylation: H3K9me3

25. Coordinated activities of chromatin modifying enzymes lead to condensation of chromatin and inhibition of gene expression DNMT Me Me Me Me Ac Ac Me Me Me Me Me Me Ac Epigenetic Modifications to Histones and DNA Can Cooperate to Silence Gene Expression HDAC HMT HMT HDAC Geneexpression Geneexpression

26. Broad peaks for trimethylation of histone H3 at lysine 4 (H3K4me3; wider than 4-kb) • first epigenetic signature for tumor suppressors in normal cell types • widespread shortening of broad H3K4me3 in cancers is associated with repression of tumor suppressors Chen, Kaifu, Zhong Chen, Dayong Wu, Lili Zhang, Xueqiu Lin, Jianzhong Su, Benjamin Rodriguez et al. Nature Genetics (24 Aug 2015).

27. Exceptionally Broad H3K4me3 signature Definition of H3K4me3 peak height and width H3K4me3 peak height plotted against peak width Chen, Kaifu, Zhong Chen, Dayong Wu, Lili Zhang, Xueqiu Lin, Jianzhong Su, Benjamin Rodriguez et al. Nature Genetics (24 Aug 2015).

28. Broad H3K4me3 • Peak widths for 4,167 promoters across ENCODE normal samples • Segmented into nine groups on basis of H3K4me3 peak width conservation level • Enrichment levels of promoter groups for housekeeping, oncogenes, and tumor suppressors • Tumorsuppressors enriched only in top two groups with most conserved H3K4me3 peaks

29. Shortening of broad H3K4me3 peaks in lung tumors Chen, Kaifu, Zhong Chen, Dayong Wu, Lili Zhang, Xueqiu Lin, Jianzhong Su, Benjamin Rodriguez et al. Nature Genetics (24 Aug 2015).

30. H3K79 methylation and MLL rearranged leukemia • Prominent example of cancer driven by mutations involving an epigenetic regulator • MLL-AF9 promotes enhanced H3K79me2 at fusion target genes • H3K79me2 specifically abnormal compared to other histone modifications • Loss of Dot1l selectively decreases leukemia-associated gene expression • Dot1l required for MLL-rearranged leukemia cell growth in vitro and in vivo Bernt et al (2011)Cancer cell, 20(1), 66-78.

31. Abnormal H3K79me2 at MLL-AF9 targets We will learn how to work with chromatin signal data Bernt et al (2011)Cancer cell, 20(1), 66-78.

32. Lecture Summary • Epigenetic inheritance in development and disease • Aberrant epigenetic changes in cancer • DNA packaging and accessibility • DNA methylation • Nutritionand environment -> fetal development • Disease biomarkers (Breast cancer prognosis) • Histone modifications • Broad regions of H3K4me3 • Aberrant H3K79me2 in MLL

33. Any questions? On to the Laboratory!

34. Miscellaneous Details • ChIPSequencing • UCSC Genome Browser • File formats • BED • BEDGRAPH • bigWig

35. Outline of lab exercises • Exercise 1: Epigenetic profiling of HSC and LSC • Data Visualization, Operating on Genomic Intervals • Creative problem solving for MLL-AF9 target genes • DAVID functional enrichment analysis of target genes • Exercise 2: Associate broad H3K4me3 peaks in HSC with genes and functions • Send analysis from UCSC Browser to GREAT • Understanding gene – region associations • Visualize results

36. Chromatin immunoprecipitation followed by sequencing (ChIP-seq) • Procedure for genome-wide assays of protein-DNA interaction • Mapping histone modifications seminal in epigenetics research

37. ChIP Sequencing: Interrogation of Histone Modificationsand Transcription Factor Binding • Resolution needs to be consistent (Covaris Adaptive Acoutstics) • Antibody specificity, Chromatin IP is a challenging technique

38. ChIP Sequencing: Computational Analysis Workflow Bailey et al. (2013). Practical guidelines for the comprehensive analysis of ChIP-seqdata. PLOS Computational Biology. DOI: 10.1371/journal.pcbi.1003326

39. How can we visualize genomic data? UCSC Genome Browser

40. Tools Covered: Genome Browser and Table Browser

41. Genome Browser zooms and scrolls over chromosomes, showing the work of annotators worldwide Table Browser provides convenient access to the underlying database

42. Bear with me I want to explain about the files we will use

43. BED Format • BED format provides a flexible way to define the data lines that are displayed in an annotation track • BED lines have up to 12 tab-delimited fields • required fields: chrom, chromStart, chromEnd • optional fields: name, score, strand, … and others. • Important, lower-numbered fields must always be populated if higher-numbered fields are used. First ten lines of our mouse promoter file. The header line identifies the track name. Why am I using the first three optional fields? If my promoters are all the same size, what do you suppose is the score field?

44. BedGraph Format • Allows display of continuous-valued data in track format • Useful for probability scores and transcriptomedata BedGraph files are very easy to work with, in my opinion

45. bigWig Format • For display of dense, continuous data • Elements must be equally sized • bigWig files are in an indexed binary format • Only the portions of the files needed to display a particular region are transferred to UCSC • bigWigfile remains on your web accessible server The processed data we will work with today are in bigWig format

46. Epigenetic profiling of HSC and LSC: Data Visualization, Operating on Genomic Intervals • (Mixed Lineage Leukemia) MLL-AF9 fusion gene • Histone methylation patterns Berntet al . MLL-rearranged leukemia is dependent on aberrant H3K79 methylation by DOT1L. Cancer Cell. 2011 Jul 12;20(1):66-78.

47. Epigenetic profiling of HSC and LSC: Data Visualization, Operating on Genomic Intervals http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29130 GMP, granulocyte-macrophage progenitor, a myeloid precursor for monoblasts and myeloblasts

48. Epigenetic profiling of HSC and LSC: Data Visualization, Operating on Genomic Intervals http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29130 GMP, granulocyte-macrophage progenitor, a myeloid precursor for monoblasts and myeloblasts In this exercise, our focus is MLL-AF9 fusion methyltransferase and HEK79me2 experiments. We will examine the chromatin signals and interrogate enrichment at gene promoter regions.

49. Epigenetic profiling of HSC and LSC: Data Visualization, Operating on Genomic Intervals Where did our bigWig files come from? SRA -> fastq -> sam -> bam -> bed -> bedgraph -> bigWig get.GSE29130.Chip-seq.job fastq-dump.job extractfastq.job alignbowtie2.job samtools.sirdu.job btools.bamToBed.job btools.extendBed.job btools.sortBed.job btools.bambgbw.job I created them from scratch, so to speak The nine job files, from top to bottom, represent the different steps 9 Jobs x 4 Experiments: MLL-AF9 H3K79me2_mLSC H3K79me2_mGMP H3K79me2_mHSC

50. Adding Custom Tracks From mm9 genome browser, choose Tools -> Table Browser Click on “add custom tracks” From a separate browser window, copy the bigWig and bed file “UCSC Genome Browser Tracks” lines from http://dldcc-web.brc.bcm.edu/lilab/benji/MBRB_2015/GSE29130.track.list.txt and paste them into “Paste URLs” box Click submit to load the tracks The mouse gene promoter bed and ChIP-seqbigWig tracks should now appear on Manage Custom Tracks Custom track files can also be uploaded via the “Choose File” option To upload many large files, you want to use a web server as we did above