Introduction to epigenetics: chromatin modifications, DNA methylation and the CpG Island landscape. Héctor Corrada Bravo CMSC702 Spring 2014

Transcription

1 Introduction to epigenetics: chromatin modifications, DN methylation and the p Island landscape Héctor orrada Bravo MS702 Spring 2014

2 enetics: the alphabet of life Letters of DN sequence carry information How is this information read and parsed? We need grammar! 2

3 Differentiation Different genes are expressed during different stages and in different tissues 3

4 (3.4x10-10 meters/bp) x (6x10 9 bp/genome) = ~2 meters/genome Klug and ummings, 1997 Radius of the nucleus is ~ 10 µm!!!

5 [(6 x 10 9 bp/genome) / (195 bp/nucleosome)] = ~ 30.8 x 10 6 nucleosomes/genome ~ 5 % of nuclear volume

6 onformation is dynamic! (we ll discuss methods to assay this conformation later on )

7 We ll study methods to assay a number of mechanisms of epigenetic regulation DN methylation Nucleosome positioning and histone modifications In eukaryotes, DN methylation usually occur at p dinucleotides

8 DN Methylation is sometimes repressive Unmethylated p dinucleotides Methylated p dinucleotides ranscription repressors bound to methyl- group Robertson and Wolffe, Nat Rev enet, 2000

9 DN methylation in cancer

10 DN methylation, histone modifications and nucleosome positioning are coordinated!! New technologies are allowing us to now assay this coordination! [Brinkman, et al., enome Research 2012]

11 Epigenetics: the grammar of life Epigenetics literally means above the genome

12 One more thing.! How does a cell retain epigenetic state?

13 Methylation H 3 H 3!!

14 H 3 H 3!! What happens during cell replication?

15 !!!! H 3 H 3 What happens during cell replication?

16 !!!! H 3 H 3 H 3 H 3 What happens during cell replication? DN methylation is replicated!

17 Liver Brain!!

18 !! H 3 H 3 H 3 H 3 Liver Brain

19 !! H 3 H 3 H 3 H 3 Liver Brain!! H 3 H 3 H 3 H 3

20 How do we measure DN methylation?

21 !! H 3 H 3!! H 3 H 3 Liver Brain

22 H 3 H 3 H 3 H 3 H 3 H 3 H 3 H 3 H 3 H 3 H 3 H 3 H 3 85% MethylaWon chr3:44,031,616-44,031,626

23 Bisulfite reatment

24 Bisulfite reatment!

25 BS-Seq overage: 12 MethylaWon Evidence: 8 MethylaWon Percentage: 67% NN NN -

26 BS- seq lignment is much trickier: Naïve strategy: do nothing, hope not many p in a single read Smarter strategy: bisulfite convert reference: turn all s to s Smartest strategy: be unbiased and try all combinations of methylated/un- methylated ps in each read

27 BS- seq here are similarities to SNP calling EXEP: we want to measure percentages Use a binomial model to estimate p, percentage of methylation llow for sequencing errors, coverage differences, etc.

28 Smoothing

29 Proportion of neighboring p also methylated/ not methylated

30 rue signal (simulated)

31 Observed data

32 Observed data and true signal

33 What is methylated (above 50%)?

34 Naïve approach

35 Many false positives (FP)

36 Smooth

37 No FP, but one false negative

38 Smooth less? No FN, lots of FP

39 We prefer this!

40 Measuring DN Methylation -./ / / !"#$%&'()'*+,#"%!"#$%&'/)'*+,#"%!"#$%&'2)'*+,#"% EsWmaWng percentages Use local- likelihood smoothing method Based on loess High- frequency smoothing detects local methylawon levels Low- frequency smoothing detects long- range methylawon levels

41 Large regions of methylation loss in cancer (a) Methylation PMD LOKS LD Islands enes 100kb normals cancers! Found large blocks of hypo-methylation (sometimes Mbps long) in colon cancer hese regions coincide with hyper-variable regions across cancer types ene expression analysis [Hansen, et al., Nature enetics] Hector orrada Bravo 41

42 Hector orrada Bravo enes with hyper-variable gene expression in colon cancer are enriched in these blocks expression SLO1B3 XL11 LDN18 ME6 INHB MMP10 IL24 S10012 IL6 PH ME12 R3 ME6 XL5 NFIP6 Normal ancer ) False posi rue positive rate yo Sab Sabates, et al. norm yorffy, et al. normal range upper bound B) ) ene expression hyper-variability [Hansen, et al., Nature enetics]

43 [Feinberg & Irizarry, 2009] 1. Variability, perhaps epigenetically mediated, increases disease susceptibility

44 [Feinberg & Irizarry, 2009] 1. Variability, perhaps epigenetically mediated, increases disease susceptibility! 2.Increased gene expression variability of specific genes/regions is a defining characteristic of cancer

45 gene expression anti-profiles expression SLO1B3 XL11 LDN18 ME6 INHB MMP10 IL24 S10012 IL6 PH ME12 R3 ME6 XL5 NFIP6 Normal ancer RESERH RILE Open ccess ene expression anti-profiles as a basis for accurate universal cancer signatures Héctor orrada Bravo 1*, Vasyl Pihur 2, Matthew Mcall 3, Rafael Irizarry 2 and Jeffrey Leek 2 bstract orrada Bravo et al. BM Bioinformatics 2012, 13:272

46 Universal anti-profile signatures rue positive rate adrenal_cortex: 0.99 U colon: 0.98 U endometrium: 0.99 U kidney: 0.87 U skin: 0.98 U stomach: 0.88 U vulva: 1.00 U False positive rate orrada Bravo et al. BM Bioinformatics 2012, 13:272 RESERH RILE ene expression anti-profiles as a basis for accurate universal cancer signatures Héctor orrada Bravo 1*, Vasyl Pihur 2, Matthew Mcall 3, Rafael Irizarry 2 and Jeffrey Leek 2 Open ccess 46

47 Universal anti-profile signatures Increased variability increases through cancer progression () (B) density all probesets anti profile probesets density all probesets anti profile probesets tumor variance/normal variance cancer variance/adenoma variance [Dinalankara & HB, under submission] 47

48 Universal anti-profile signatures Relapse in lung cancer patients patient relapse Stratification by anti-profile score Score based on departure from lung normal expression on universal anti-profile genes log-rank statistic 15.6 probability of survival [Dinalankara & HB, under submission] Lower 50% op 50% time(years) 48

49 nomaly lassification (anti-profilesvm) Extend the anti-profile idea to nonlinear classification methods ore idea: measure similarity between anomalies indirectly through similarity to normal class arcinoma Normal denoma [Dinalankara, et al., under submission]

50 Finding hypo-methylation blocks w/ beadarrays chr1: Methylation kb Normal ancer Methylation difference Minfi/ 176.0Mb 176.5Mb 177.0Mb 177.5Mb 178.0Mb Hansen et al. hypo hyper Smoothing method for large region detection for Illumina 450k array Implemented in minfi Bioconductor package [ryee, et al., Bioinformatics] 50

51 Hypo-methylation blocks in multiple solid tumors verage Methylation ancer-normal Methylation Standard Deviation Methylation ) B) ) colon lung pan thyroid D) E) F) 450k profiling across five tumor types colon, lung, breast, pancreas and thyroid Hypo-methylation blocks occur in all tumor types some universal, many cancer-specific [imp et al., in preparation] 51

52 Hypo-methylation blocks in multiple solid tumors in block odds ratio in block odds ratio colon hyper variability log ratio pancreas hyper variability log ratio in block odds ratio in block odds ratio breast hyper variability log ratio thyroid hyper variability log ratio in block odds ratio lung hyper variability log ratio ene expression hyper-variability enriched in hypo-methylation blocks [imp et al., in preparation] 52

53 Hypo-methylation blocks in multiple solid tumors olon Progression hyroid Progression verage in blocks verage in Island ) D) E) F) verage in blocks verage in Island Normal denoma ancer Metastasis Normal Benign denoma MinInv ancer apsinv ancer VascInv ancer Metastasis Degree of hypo-methylation in blocks increases through cancer progression 53

54 Summary Large regions of hypo-methylation seems to be consistent in cancer occur in pre-cancerous lesions hypo-methylation increases with cancer progression ene expression hyper-variability enriched within these regions tissue-specific genes also enriched within these regions can use degree of deviation from normality as stable diagnosis and prognosis mark in 54

55 H 3 H 3 H 3 H 3 H 3 H 3 H 3 H 3 H 3 H 3 H 3 H 3 H 3 85% MethylaWon chr3:44,031,616-44,031,626

56 )Methylation his is a heterogenous cell population PMD LOKS LD Islands enes 100kb normals cancers Normal umor 56

57 )Methylation his is a heterogenous cell population PMD LOKS LD Islands enes 100kb normals cancers Normal umor 57

58 Epipolymorphism mos anay s Weizmann n n n n Figu met but 58

59 )Methylation his is a heterogenous cell population PMD LOKS LD Islands enes 100kb normals cancers How are cell populations changing in normal and tumor? Same set of methylation patterns, with different abundances? re patterns lost or gained? 59

60 Heterogeneous elltype omposition elltype-specific Methylation Patterns Methylated p Unmethylated p

61 ligned Reads their overlap allows reconstruction of long methylation patterns

62 he pattern reconstruction problem iven a set of mapped reads Determine composition of cell-specific methylation patterns omposition: Pattern identification: which cell-specific methylation patterns are present Pattern abundances: abundance of each cell-specific pattern present Derive statistics: community richness: how many unique patterns community diversity: distribution of pattern abundance fragment uniformity: long-scale spatial methylation correlation ompare statistics across groups: re richness and/or diversity increasing or decreasing in cancer? Is spatial correlation lost in cancer? 62

63 elltype-specific Methylation Patterns Heterogeneous elltype omposition Methylated p Unmethylated p Region raph ligned Reads no. reads assigned

64 Methylation pattern reconstruction by network flows Region raph Penalized method of moments estimation procedure: # parameters: # paths through region graph region coverage penalty induces small number of patterns with non-zero abundance min p X X y v v u:(v,u)2e `vu X p:(v,u)2p p + X p p 64

65 Bisulfite capture data Frequency P 4 Frequency P_N_5 Frequency P assembled fragment size P_N_ assembled fragment size P assembled fragment size P_N_6 Frequency Frequency Frequency assembled fragment size assembled fragment size assembled fragment size 65

66 Bisulfite capture data 66

67 Bisulfite apture Data normal num. patterns tumor num. patterns normal entropy tumor entropy normal entropy (normalized) tumor entropy (normalized Similar entropy distribution in normal and tumor, but many regions of differential entropy

68 Bisulfite apture Data 68 Not associated with methylation difference (with some caveats) Subject 1 methylation difference entropy difference Subject 2 methylation difference entropy difference Subject 3 methylation difference entropy difference

69 elltype-specific methylation pattern reconstruction Unprecedented view into molecular profiling omplex relationship between cell-to-cell differences and average methylation differences Efficient formulation as network flow problem Exploring relationship to stochastic optimization In general: properties of population inferences from inferred features Here: patterns are inferred from overlaps metagenomics: OUs inferred from sequence similarity clustering RNseq: transcripts inferred from junction spanning reads (or junction spanning k-mers) 69