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

2. Components DNA-methylation Nucleosome position Histone modifications
Chromatin accessibility
Higher order chromatin interactions
Analogy

3. Nucleosome Occupancy & Histone Modification Influence Factor Binding
MNase-seq
Zentner & Henikoff, Nat Rev Genet 2014

4. MNase-seq Analysis
Park, Nat Rev Genet 2009

5. MNase-seq Analysis
Park, Nat Rev Genet 2009

6. Factors Influencing Nucleosome Positioning
Rotational
positioning
from sequence
Statistical positioning from trans-factors (e.g. TF binding)
Break
Jiang & Pugh, Nat Rev Genet 2009

7. Components DNA-methylation Nucleosome position Histone modifications
Chromatin accessibility
Higher order chromatin interactions
Analogy

8. Mapping Histone Modifications
Histone tails
Histone mark ChIP-seq
Park, Nat Rev Genet 2009

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

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

11. 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 mark: H3K27me3, H3K9me3
Annotate / segment the genome based on histone marks

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

15. 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.

16. 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.

17. 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.

18. Take the middle 73 nt

19. 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

20. 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

21. Predict Driving TFs and Bindings for Gut Differentiation

22. 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
Break
Verzi et al, Dev Cell, 2010

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

24. 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

25. 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

26. TF Network from DNase Footprint

27. 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

28. 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

29. 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

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

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

32. HiC In situ HiC has excellent resolution
Domains conserved between cells and even species

33. HiC Loops are condition-specific Assign binding to genes
Convergent CTCF at domain anchors
CTCF as insulators

34. Epigenetics and Chromatin

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