1. Xiaole Shirley Liu STAT115, STAT215, BIO298, BIST520
Epigenetics
Xiaole Shirley Liu
STAT115, STAT215, BIO298, BIST520

2. Epigenetics
Heritable changes in gene function that occur without a change in the DNA sequence
How come not all the motif sites are bound by the factor?
How come TF binding only regulate some of the nearby genes?

3. Epigenetics
The study of heritable (transgenerational) changes in gene activity that are not caused by changes in the DNA sequence
The study of stable, long-term alterations in the transcriptional potential of a cell that are not necessarily heritable
Functionally relevant changes to the genome that do not involve a change in the nucleotide sequence

4. In Human Nature vs nurture Zygotic twins: same DNA different epigenome
North American Ice Storm of 1998

5. Agouti Mice and DNA Methylation

6. The Making of a Queen
Larvae
Queen
Worker
From Ting Wang, Wash U

7. Epigenetic Landscape Conrad Hal Waddington (1905–1975)
Developmental biologist
Paleontologist
Geneticist
Embryologist
Philosopher
Founder for systems biology

8. Components DNA-methylation
Nucleosome position and histone modifications
Chromatin accessibility
Higher order chromatin interactions
Analogy

9. DNA Methylation Distribution in Mammalian Genomes
In human somatic cells, 60%-80% of all CpGs
(~1% of total DNA bases) are methylated
Most methylation is found in “repetitive” elements
“CpG islands”, GC-rich regions that possess a high density of CpGs, remain methylation-free
The promoter regions of ~70% of genes have CpG islands
From Ting Wang, Wash U

10. Two classes of DNA methyltransferases (DNMTs)
The basis of this model is that DNA methylation patterns are established in germ cells and in developing embryos by the activity of the de novo DNA methyltransferases (DNMTs)
Methylation patterns are copied by DNMT1. copying a pattern that is present on DNA onto newly synthesized strand
Jones and Liang, 2009 Nature Review Genetics

11. Inheritance of DNA Methylation
From Ting Wang, Wash U

12. DNA Methylation Detection
Bisulfite sequencing
Unmethyl C  T
High resolution, quantitative, but expensive!

13. From Wei Li, Baylor

14. BS-seq Methylation Call
Most regions are either mostly methylated or mostly unmethylated (dichotomy)
Methylation level within a short distance is consistent
ACGGGCTTACTTGCTTTCCTACGGGCTTACTTGCTTTCCTACGGGCTTACTTGCTTTCCTACGGGCTTACTTGC
CGGGTTTATTTGCTTTTTTATGGGC
TGGGTTTATTTGCTTTTTTATGGGC
TGGGTTTATTTGCTTTCCTATGGGC
CGGGCTTATTTGCTTTCCTATGGGC
CGGGCTTATTTGCTTTCCTATGGGC
3/5
0/5
60% methylated
0% methylated
From Ting Wang, Wash U

15. DNA Methylation Controls Gene Expression
Methylation at CpG islands often repress nearby gene expression
Many highly expressed genes have CpG methylation on their exons
Some genes could be imprinted, so maternal and paternal copies have different DNA methylation
From Ting Wang, Wash U

16. DNA Methylation in Cancer
Prevalent misregulation of DNA methylation in cancer: global hypomethylation and CpG island hypermethylation
Methylation variable regions in cancer

17. DNA Demethylation
Recently, another type of DNA methylation called hydroxyl methylation (hmC) is found
hmC is an intermediate step between mC and C.
TET family of proteins are important for DNA demethylation
Mutation in TET is linked to many cancers

18. Components DNA-methylation
Nucleosome position and histone modifications
Chromatin accessibility
Higher order chromatin interactions
Analogy

19. Nucleosome Occupancy & Histone Modification Influence Factor BindingTF
Last year, Keji Zhao’s group published a pioneering studying using Solexa to conduct ChIP-seq of 21 histone marks in human CD4 T cells. In the study, chromatin was digested with MNase and mononucleosomes carrying specific histone mark was enriched using IP. Solexa sequencing was used to read 25-mer from either end of the nucleosomal DNA. Although an unprecedented 185 million nucleosome tags were sequenced, no analysis to-date aims to use them to study nucleosome position at specific locations in the genome.

20. Histone Modifications
Different modifications at different locations by different enzymes

21. Histone Modifications in Relation to Gene Transcription
From Ting Wang, Wash U

22. Histone Modifications
Gene body mark: H3K36me3, H3K79me3
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

23. lncRNA Identification
H3K4me3 active promoters
H3K36me3 transcription elongation
Guttman et al, Nat 2009

26. Nucleosome Occupancy & Histone Modification Influence Factor Binding
Antibody for
MNase digest TF
Last year, Keji Zhao’s group published a pioneering studying using Solexa to conduct ChIP-seq of 21 histone marks in human CD4 T cells. In the study, chromatin was digested with MNase and mononucleosomes carrying specific histone mark was enriched using IP. Solexa sequencing was used to read 25-mer from either end of the nucleosomal DNA. Although an unprecedented 185 million nucleosome tags were sequenced, no analysis to-date aims to use them to study nucleosome position at specific locations in the genome.

27. Combine Tags From All ChIP-Seq
So we went about looking at the data more. For each specific region, we combined all the tags from all marks, because they are all nucleosome ends.

28. Extend Tags 3’ to 146 nt Check Tag Count Across Genome
Then at each position, we counted the number of extended tags that landed there.
The sequenced nucleosomes were not always 150bp. To better locate the center of positioned nuclesomes, we took the center 75 bp of each extended tag.

29. Take the middle 73 nt

30. Use H3K4me2 / H3K27ac Nucleosome Dynamics to Infer TF Binding Events
Nucleosome Stabilization-Destabilization (NSD) Score
Condition 1
Condition 2
In a proof of concept study we collaborated with Myles Brown’s lab, and found that very often, a cell will anticipate the stimulus it might get, and mark the potential TF binding sites before the TF is stably bound. Upon….
We devised a simple scoring function to measure how much the middle down vs flanking up, and we call it NSD.
Positive NSD is nucleosome depletion, negative NSD is nucleosome moving back, NSD of 0 means no nucleosome moving.
He et al, Nat Genet, 2010; Meyer et al, Bioinfo 2011

31. Condition-Specific Binding, Epigenetics and Gene Expression
Condition-specific TF bindings are associated with epigenetic signatures
Can we use the epigenetic profile and TF motif analysis to simultaneous guess the binding of many TFs together?
Genes TF1 TF2 TF3 Epigenetics

32. Predict Driving TFs and Bindings for Gut Differentiation

33. Identify Major TF Modules Regulating Gut Differentiation and Function
GATA6
Cdx2
Embryonic and organ development genes
HNF4
Metabolic and digestive genes
Cdx2
Nucleosome dynamics now applied to hematopoiesis and cancer cell reprogramming
Verzi et al, Dev Cell, 2010

34. Components DNA-methylation
Nucleosome position and histone modifications
Chromatin accessibility
Higher order chromatin interactions
Analogy

35. DNase Hypersensitive (HS) Mapping
DNase randomly cuts genome (more often in open chromatin region)
Select short fragments (two nearby cuts) to sequence
Map to
active
promoters
and
enhancers
Ling et al, MCB 2010

36. DHS Peaks Capture Most TF Binding Sites
Motif occurrence in the DHS peaks suggest TF binding
Quantitative signal strength also suggest binding stability
Thurman et al, Nat 2012

37. TF Network from DNase Footprint

38. DnaseI Cleavage vs Footprint
Footprint occupancy score: FOS = (C + 1)/L + (C + 1)/R
Smaller FOS value better footprint, for
predicting base resolution TF binding
L C R
GAT ACA
CTA TGT

39. DnaseI Cleavage vs Footprint
Footprint occupancy score: FOS = (C + 1)/L + (C + 1)/R
Smaller FOS value better footprint, for
predicting base resolution TF binding
Intrinsic DNase cutting bias could
have 300-fold difference, creating fake footprints
L C R
GAT ACA
CTA TGT
CAGATA 0.004
CAGATC 0.004 …
ACTTAC 1.225
ACTTGT 1.273

40. Using DNaseI “Footprint” to Predict TF Binding
Using base-pair resolution cleavage pattern (“footprint”) hurts TF binding prediction when it is similar to intrinsic DNaseI cutting bias

41. Using DNaseI “Footprint” to Predict Novel TF Motifs
He et al, Nat Meth 2013

42. Epigenetics and Chromatin

43. Transcription and Epigenetic Regulation
Stem cell differentiation
Aging brain
Cancer