1. A genomic code for nucleosome positioning

2. Hierarchical DNA folding in eukaryotic chromosomes
DNA double helix
Nucleosomes
Felsenfeld & Groudine,,
Nature 421: (2003)

3. Most eukaryotic DNA is sharply looped
Luger et al., 1997

4. Physical selection for stable nucleosome formation on chemically synthetic random DNAs
Random 220-mer pool
(1 each of 5 x 1012 individuals)
PCR amplify, HPLC purify
Select best 10%
(dialysis from 2.0M NaCl)
2x Sucrose gradient purification
Extract DNA
Population diversity analysis
Free energy analysis
Clone, sequence, analyze individuals
Lowary & Widom, 1998

5. Differing DNA sequences exhibit a > 5,000-fold range of affinities for nucleosome formation
Lowary & Widom, 1998
Thåström et al., 1999
Widom, 2001
Thåström et al., 2004

6. Basepair steps as fundamental units of DNA mechanics
Zhurkin
Olson

7. DNA sequence motifs that stabilize nucleosomes and facilitate spontaneous sharp looping
AA TT TA GC
Segal et al., 2006

8. Why DNA some sequences have especially high affinity for histone octamer
More or better bonds
Appropriately bent
More easily bendable
Appropriate twist
More easily twistable
Widom, 2001
Cloutier & Widom, 2004

9. Felsenfeld, G. & Groudine, M. (2003), Nature 421: 448-453

10. ~10.2 bp periodicity of AA/TT steps in the C. elegans genome
Widom, 1996

11. Understanding and predicting the genome’s nucleosome-forming potential
Collect nucleosome-bound sequences
Associate intrinsic encoding with biological function
Predict intrinsically encoded nucleosome organization
Construct nucleosome-DNA interaction model
Compare with in vivo positions
Validate model
So we used a combined experimental and computational approach to study this problem and we started by experimentally collecting sequences that are wrapped in nucleosomes. We then construct a model that captures the commonalities that we find in these sequences so that we describe in this model the DNA preferences of nucleosomes, validate the model and then use it to computationally predict the organization of the entire genome into nucleosomes, and then compare this intrinsic organization with in vivo positions and study its biological implications.
Segal et al., 2006

12. Isolation of natural nucleosome core DNA
Alberts et al., 4th ed., Fig. 4–24 (2002)

13. Center alignment of yeast nucleosome DNAs
ACGTAGCTGTAGTGTACTGACGTACGTCGTC
ACTAGCTGATACGGAGACCCGCGCGATTTTGCGGTC
ACTGTTCGTCGTGTGTGTGTGCTGCTGTAGACTTGTGTG
TACTGTTTTTATTTTGCGGGCATGCTTGT
Center align
~10bp periodicity of AA/TT/TA
Same period for GC, out of phase with AA/TT/TA
Signals important for DNA bending
NO signal from alignment of randomly chosen genomic regions
Yeast (in vivo nucleosomes)
Segal et al., 2006

14. Location mixture model alignment vs center alignment
Wang & Widom, 2005

15. Center alignment of chicken nucleosome DNAs
Chicken + Yeast merge
Chicken (in vivo) (Satchwell et al., 1986)
Yeast (in vivo)
Segal et al., 2006

16. Alignments of nucleosomes selected in vitro
Chicken (in vivo) (Satchwell et al., 1986)
Yeast (in vivo)
Yeast (in vitro)
Mouse (in vitro) (Widlund et al., 1997)
Random DNA (in vitro) (Lowary & Widom, 1998)
Segal et al., 2006

17. In vitro experimental validation of histone-DNA interaction model
Adding key motifs increases nucleosome affinity
Deleting motifs or disrupting their spacing decreases affinity
Segal et al., 2006

18. In vitro experimental validation of histone-DNA interaction model
Adding key motifs increases nucleosome affinity
Deleting motifs or disrupting their spacing decreases affinity
dyad 8 18 28 38 48 58 68 78 88 98
108
118
128
138
To make sure that these really are the rules of the game we did in vitro experiments, and what we found is that indeed if you start from a sequence and add key motifs at the right positions you can get sequences that have better binding affinities, whereas deleting these motifs or changing their spacing decreases their affinity.
Segal et al.,
2006

19. Summary
Differing DNA sequences exhibit a > 5,000-fold range of affinities for nucleosome formation
We have a predictive understanding of the DNA sequence motifs that are responsible

20. Nucleosome–DNA model Differences from TF model Signal is weak
Information in dinucleotides
Two-fold symmetry axis
AA TT TA GC
Position specific dinucleotide distributions
Add reverse complement of each sequence
Local (3 bp) averaging
Segal et al., 2006

21. Summary
Differing DNA sequences exhibit a > 5,000-fold range of affinities for nucleosome formation
We have a predictive understanding of the DNA sequence motifs that are responsible
Sequences matching these motifs are abundant in eukaryotic genomes, and are occupied by nucleosomes in vivo

22. Placing nucleosomes on the genome
A free energy landscape, not just scores and a threshold !!
Log likelihood
Genomic Location (bp)
Nucleosomes occupy 147 bp and exclude 157 bp
Segal et al., 2006

23. Equilibrium configurations of nucleosomes on the genome
One of very many possible configurations
P(S)
PB(S)
P(S)
PB(S)
P(S)
PB(S)
P(S)
PB(S)
Chemical potential – apparent concentration
Probability of placing a nucleosome starting at each allowed basepair i of S
Probability of any nucleosome covering position i ( average occupancy)
Locations i with high probability for starting a nucleosome ( stable nucleosomes)
Segal et al., 2006

24. Cross-correlation of average occupancies
predicted using yeast and chicken models
Segal et al., 2006

25. Nucleosome coding potential at the GAL1–10 locus: predicted distribution compared to experimental
Segal et al., 2006

26. Nucleosome coding potential at the CHA1 locus
Segal et al., 2006

27. Reliable classification of nucleosome occupancies on depleted vs occupied regions
Yeast ORFs depleted of nucleosomes (Lee et al.)
(0.82, 0.68)
Yeast ORFs occupied by nucleosomes (Lee et al.)
(0.88, 0.76)
(0.88, 0.55)
p<10–6
p<10–9
(0.82, 0.32)
Yeast intergenics occupied by nucleosomes (Bernstein et al.)
Yeast intergenics depleted of nucleosomes (Bernstein et al.)
Segal et al., 2006

28. In vivo nucleosome occupancies at predicted high-occupancy locations
+ Controls
– Controls
High Predictions
Segal et al., 2006

29. In vivo nucleosome occupancies at predicted low-occupancy locations
Segal et al., 2006

30. Summary
Differing DNA sequences exhibit a > 5,000-fold range of affinities for nucleosome formation
We have a predictive understanding of the DNA sequence motifs that are responsible
Sequences matching these motifs are abundant in eukaryotic genomes, and are occupied by nucleosomes in vivo
A model based only on these DNA sequence motifs and nucleosome-nucleosome exclusion explains ~50% of in vivo nucleosome positions

31. Understanding and predicting the genome’s nucleosome-forming potential
Collect nucleosome-bound sequences
Construct nucleosome-DNA interaction model
Validate model
So we used a combined experimental and computational approach to study this problem and we started by experimentally collecting sequences that are wrapped in nucleosomes. We then construct a model that captures the commonalities that we find in these sequences so that we describe in this model the DNA preferences of nucleosomes, validate the model and then use it to computationally predict the organization of the entire genome into nucleosomes, and then compare this intrinsic organization with in vivo positions and study its biological implications.
Predict intrinsically encoded nucleosome organization
Compare with in vivo positions
Associate intrinsic encoding with biological function
Segal et al., 2006

32. Nucleosome occupancy varies with chromosome region type
Centromere
Convergent intergenic regions
Autonomously replicating sequences
Unique intergenic regions
Ribosomal proteins
Divergent intergenic regions
Protein coding regions
Telomeres
Conserved DNA binding sites
Ribosomal RNAs
tRNAs
Average nucleosome occupancy
Segal et al., 2006

33. Does the genome’s intrinsic nucleosome organization facilitate occupancy of functional binding sites?
Segal et al., 2006

34. The yeast genome encodes low nucleosome occupancy over functional binding sites
Segal et al., 2006

35. Semi-stable nucleosomes
Distinctive nucleosome occupancy adjacent to TATA elements at yeast promoters
Stable nucleosome
Semi-stable nucleosomes
Permuted
Model
TATA Box
Segal et al., 2006

36. S. cerevisiae
S. Paradoxus
S. mikatae
S. kudriavzevii
S. bayanus
S. castelli
C. glabrata
S. kluyveri
K. walti
K. lactis
A. gossypii
D. hansenii
C. albicans
Y. lipolytica
A. nidulans
S. pombe
-36
-41
-22
-42
-23
-53
-21
-14
-26 -3
-16 -9
-56 -2
Nucleosome organization near 5’ ends of genes is conserved through evolution
Segal et al., 2006

37. Predicted nucleosome organization near 5’ ends of genes – comparison to experiment
Segal et al., 2006

38. Summary
Differing DNA sequences exhibit a > 5,000-fold range of affinities for nucleosome formation
We have a predictive understanding of the DNA sequence motifs that are responsible
Sequences matching these motifs are abundant in eukaryotic genomes, and are occupied by nucleosomes in vivo
A model based only on these DNA sequence motifs and nucleosome-nucleosome exclusion explains ~50% of in vivo nucleosome positions
These intrinsically encoded nucleosome positions are correlated with, and may facilitate, essential aspects of chromosome structure and function

39. Factors present in the nucleus
Genome sequence influences the competition between nucleosomes and DNA binding proteins
Factors present in the nucleus
None +
200
100
Promoter region 1 20
-20 1
Nucleosome score landscape
Nucleosome occupancy
Stable nucleosome
Unstable nucleosome
DNA binding protein
DNA binding site /
Promoter region 2 I II
III
Segal et al.,
2006

40. Toward a proper free energy model for the sequence-dependent cost of DNA wrapping
AA TT TA GC
Morozov, Segal, Widom, & Siggia

41. Acknowledgements Nucleosome positioning in vivo
Eran Segal (Weizmann Inst.)
Yvonne Fondufe-Mittendorf
Lingyi Chen
Annchristine Thåström
Yair Field (Weizmann Inst.)
Irene Moore
Jiping Wang (Northwestern U. Statistics)
Alexandre Morozov (Rockefeller U.)
Eric Siggia (Rockefeller U.)