1. Introduction to epigenetics: chromatin modifications, DNA methylation and the CpG Island landscape (part 2) Héctor Corrada Bravo CMSC858P Spring 2012 (many slides courtesy of Rafael Irizarry)

2. How do we measure DNA methylation?

3. Microarray Data

4. One question… Where do we measure? At least 7 arrays are needed to measure entire genome CpG are depleated Remaining CpGs cluster

5. CpG Islands

6. But variation seen outside

7. McRBC No Methylation Cuts at A m CG or G m CG Input

8. McRBC Methylation

9. McRBC after GEL Methylation

10. McRBC after GEL Methylation

11. Now unmethylated No Methylation

12. McRBC after Gel No Methylation

16. Gene Expression Normalization does not work well here

17. We use control probes

18. There are also waves

19. Smoothing

20. McRBC on tiling two channel array We smooth

21. Proportion of neighboring CpG also methylated/not methylated

22. True signal (simulated)

23. Observed data

24. Observed data and true signal

25. What is methylated (above 50%)?

26. Naïve approach

27. Many false positives (FP)

28. Smooth

29. No FP, but one false negative

30. Smooth less? No FN, lots of FP

31. We prefer this!

32. CHARM DMR for three tissues (five replicates) Irizarry et al, Nature Genetics 2009

33. Some findings [Irizarry et al., 2009, Nat. Genetics]

34. Tissue easily distinguished

35. Cancer DMR

36. Many Regions like this Note: hypo and hyper methylation

37. Both hyper and hypo methylated

38. Cancer and Tissue DMRs coincide

39. DMR enriched in Shores

40. Still affects expression T-DMRs

41. Still affects expression C-DMRs

42. USING SEQUENCING (BS-SEQ)

43. TTCGATTACGATTCGATTACGA AAGCTAATGCTAAGCTAATGCT CH 3 TTCGATTACGATTCGATTACGA AAGCTAATGCTAAGCTAATGCT LiverBrain

44. TTCGATTACGATTCGATTACGA AAGCTAATGCTAAGCTAATGCT CH 3 TTCGATTACGATTCGATTACGA AAGCTAATGCTAAGCTAATGCT TTCGATTACGATTCGATTACGA AAGCTAATGCTAAGCTAATGCT TTCGATTACGATTCGATTACGA AAGCTAATGCTAAGCTAATGCT TTCGATTACGATTCGATTACGA AAGCTAATGCTAAGCTAATGCT TTCGATTACGATTCGATTACGA AAGCTAATGCTAAGCTAATGCT TTCGATTACGATTCGATTACGA AAGCTAATGCTAAGCTAATGCT 85% Methylation chr3:44,031,616-44,031,626

45. Bisulfite Treatment

46. GGGGAGCAGCATGGAGGAGCCTTCGGCTGACT GGGGAGCAGTATGGAGGAGTTTTCGGTTGATT

47. BS-seq GTCGTAGTATTTGTCT GTCGTAGTATTTGTNN TGTCGTAGTATCTGTC TATGTCGTAGTATTTG TATATCGTAGTATTTT TATATCGTAGTATTTG NATATCGTAGTATNTG TTTTATATCGCAGTAT ATATTTTATGTCGTA ATATTTTATCTCGTA ATATTTTATGTCGTA GA-TATTTTATGTCGT GATCACAGGTCTATCACCCTATTAACCACTCACGGGAGCTCTCCATGCATTTGGTATTTTCGTCTGGGGGGTATGCACGCGATAGCATTGCGAGACGCTGGAGCCGGAGCACCCTATGTCGCAGTATCTGTCTTTGATTCCTGCCTCATCCTATTATTTATCGCACCTACG TTCAATATT Coverage: 13 Methylation Evidence: 13 Methylation Percentage: 100%

48. BS-seq GTCGTAGTATTTGTCT GTCGTAGTATTTGTNN TGTCGTAGTATCTGTC TATGTCGTAGTATTTG TATATTGTAGTATTTT TATATCGTAGTATTTG NATATTGTAGTATNTG TTTTATATTGCAGTAT ATATTTTATGTCGTA ATATTTTATCTTGTA ATATTTTATGTCGTA GA-TATTTTATGTCGT GATCACAGGTCTATCACCCTATTAACCACTCACGGGAGCTCTCCATGCATTTGGTATTTTCGTCTGGGGGGTATGCACGCGATAGCATTGCGAGACGCTGGAGCCGGAGCACCCTATGTCGCAGTATCTGTCTTTGATTCCTGCCTCATCCTATTATTTATCGCACCTACG TTCAATATT Coverage: 13 Methylation Evidence: 9 Methylation Percentage: 69%

49. BS-seq GTCGTAGTATTTGTCT GTCGTAGTATTTGTNN TGTTGTAGTATCTGTC TATGTTGTAGTATTTG TATATTGTAGTATTTT TATATTGTAGTATTTG NATATTGTAGTATNTG TTTTATATTGCAGTAT ATATTTTATGTCGTA ATATTTTATCTTGTA ATATTTTATGTTGTA GA-TATTTTATGTCGT GATCACAGGTCTATCACCCTATTAACCACTCACGGGAGCTCTCCATGCATTTGGTATTTTCGTCTGGGGGGTATGCACGCGATAGCATTGCGAGACGCTGGAGCCGGAGCACCCTATGTCGCAGTATCTGTCTTTGATTCCTGCCTCATCCTATTATTTATCGCACCTACG TTCAATATT Coverage: 13 Methylation Evidence: 4 Methylation Percentage: 31%

50. BS-seq Alignment is much trickier: – Naïve strategy: do nothing, hope not many CpG in a single read – Smarter strategy: “bisulfite convert” reference: turn all Cs to Ts Also needs to be done on reverse complement reference and reads – Smartest strategy: be unbiased and try all combinations of methylated/un-methylated CpGs in each read Computationally expensive (see Hansen et al, 2011, for a strategy)

51. BS-seq There are similarities to SNP calling (we’ll see this in a couple of weeks) EXCEPT: we want to measure percentages – Use a binomial model to estimate p, percentage of methylation – Allow for sequencing errors, coverage differences, etc.

52. Measuring DNA Methylation Estimating percentages Use “local-likelihood” method – Based on loess (Plot courtesy of Kasper Hansen)

53. BS-seq Lister et al. 2009, Nature

54. Gene Expression Regulation: DNA methylation in promoter regions Lister et al. 2009, Nature

55. DNA methylation patterns within genomic regions Lister et al. 2009

56. Putting it together

58. What were we after? The epigenetic progenitor origin of human cancer [Feinberg, et al., Nature Reviews Genetics, 2006] Stochastic epigenetic variation as driving force of disease [Feinberg & Irizarry, PNAS, 2009] Phenotypic variation, perhaps epigenetically mediated, increases disease susceptibility Increased epigenetic and gene expression variability of specific genes/regions is a defining characteristic of cancer

59. What did we do? Custom Illumina methylation microarray Confirmed increased epigenetic variability in specific regions across five cancer types

60. What did we do? Custom Illumina methylation microarray Confirmed increased epigenetic variability in specific regions across five cancer types

61. What did we do? Custom Illumina methylation microarray Confirmed increased epigenetic variability in specific regions across five cancer types Confirmed same sites are involved in tissue differentiation

62. What did we do? Custom Illumina methylation microarray Whole genome sequencing of bisulfite treated DNA – Found large blocks of hypo-methylation (sometimes Mbps long) in colon cancer

63. What did we do? Custom Illumina methylation microarray Whole genome sequencing of bisulfite treated DNA – Found large blocks of hypo-methylation (sometimes Mbps long) in colon cancer – These regions coincide with hyper-variable regions across cancer types

64. What did we do? Custom Illumina methylation microarray Whole genome sequencing of bisulfite treated DNA Gene Expression Analysis

65. Gene Expression Data

66. When using multiple microarray experiments, proper normalization is key [McCall, et al., Biostatistics 2010]

67. Normalization is key fRMA: a single-chip normalization procedure GNUSE: a single-chip quality metric Barcode: a single-chip common-scale measurement

68. What did we do? Custom Illumina methylation microarray Whole genome sequencing of bisulfite treated DNA Gene Expression Analysis – Genes with hyper-variable gene expression in colon cancer are enriched in hypo-methylation blocks [Corrada Bravo, et al., under review]

69. What are we doing next? Custom Illumina methylation microarray Whole genome sequencing of bisulfite treated DNA Gene Expression Analysis – Genes with hyper-variable gene expression in colon cancer are enriched in hypo-methylation blocks

70. Bigger gene expression study 7,741 HGU133plus2 samples 598 normal tissue samples, 4,886 tumor samples 176 different tissue types 175 different GEO studies

71. Bigger gene expression study [Corrada Bravo, et al., under review]

72. What are we doing next? Custom Illumina methylation microarray Whole genome sequencing of bisulfite treated DNA Gene Expression Analysis – Genes with hyper-variable gene expression in colon cancer are enriched in hypo-methylation blocks – Tissue-specific genes have hyper-variable gene expression across cancer types [Corrada Bravo, et al., under review]