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A genomic code for nucleosome positioning Hierarchical DNA folding in eukaryotic chromosomes DNA double helix Nucleosomes Felsenfeld & Groudine,, Nature 421: 448-453 (2003) Most eukaryotic DNA is sharply looped Luger et al., 1997 Random 220-mer pool (1 each of 5 x 10 12 individuals)

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slide2

Hierarchical DNA folding in eukaryotic chromosomes

DNA double helix

Nucleosomes

Felsenfeld & Groudine,,

Nature 421: 448-453 (2003)

slide4

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

Physical selection for stable nucleosome formation on chemically synthetic random DNAs

Lowary & Widom, 1998

slide5

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

slide7

AATTTA

GC

AATTTA

GC

AATTTA

GC

GC

AATTTA

AATTTA

GC

GC

AATTTA

AATTTA

GC

DNA sequence motifs that stabilize nucleosomes and facilitate spontaneous sharp looping

Segal et al., 2006

why dna some sequences have especially high affinity for histone octamer
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

slide11

Collect nucleosome-bound sequences

Validate model

Construct nucleosome-DNA interaction model

Compare within vivo positions

Predict intrinsically encodednucleosome organization

Associate intrinsic encoding with biological function

Understanding and predicting the genome’s nucleosome-forming potential

Segal et al., 2006

slide12

Isolation of natural nucleosome core DNA

Alberts et al., 4th ed., Fig. 4–24 (2002)

slide13

ACGTAGCTGTAGTGTACTGACGTACGTCGTC

ACTAGCTGATACGGAGACCCGCGCGATTTTGCGGTC

ACTGTTCGTCGTGTGTGTGTGCTGCTGTAGACTTGTGTG

TACTGTTTTTATTTTGCGGGCATGCTTGT

Center align

ACGTAGCTGTAGTGTACTGACGTACGTCGTC

ACTAGCTGATACGGAGACCCGCGCGATTTTGCGGTC

ACTGTTCGTCGTGTGTGTGTGCTGCTGTAGACTTGTGTG

TACTGTTTTTATTTTGCGGGCATGCTTGT

Center alignment of yeast nucleosome DNAs

  • ~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

slide14

Center alignment

Location mixture model alignment

Location mixture model alignment vs center alignment

Wang & Widom, 2005

slide15

Center alignment of chicken nucleosome DNAs

Chicken + Yeast merge

Chicken (in vivo) (Satchwell et al., 1986)

Yeast (in vivo)

Segal et al., 2006

slide16

Alignments of nucleosomes selected in vitro

Mouse (in vitro) (Widlund et al., 1997)

Random DNA (in vitro)(Lowary & Widom, 1998)

Yeast (in vitro)

Chicken (in vivo) (Satchwell et al., 1986)

Yeast (in vivo)

Segal et al., 2006

slide17

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

slide18

In vitro experimental validation of histone-DNA interaction model

  • Adding key motifs increases nucleosome affinity
  • Deleting motifs or disrupting their spacing decreases affinity

dyad

38

8

18

28

48

58

68

78

88

98

108

118

128

138

Segal et al.,

2006

slide19

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

slide20

AATTTA

GC

AATTTA

GC

AATTTA

GC

GC

AATTTA

AATTTA

GC

GC

AATTTA

AATTTA

GC

Nucleosome–DNA model

Differences from TF model

  • Signal is weak
  • Information in dinucleotides
  • Two-fold symmetry axis
  • Position specific dinucleotide distributions
  • Add reverse complement of each sequence
  • Local (3 bp) averaging

Segal et al., 2006

slide21

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

slide22

Log likelihood

Genomic Location (bp)

Placing nucleosomes on the genome

A free energy landscape, not just scores and a threshold !!

  • Nucleosomes occupy 147 bp and exclude 157 bp

Segal et al., 2006

slide23

Equilibrium configurations of nucleosomeson 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

slide24

Cross-correlation of average occupancies

predicted using yeast and chicken models

Segal et al., 2006

slide25

Nucleosome coding potential at the GAL1–10 locus: predicted distribution compared to experimental

Segal et al., 2006

slide27

Yeast intergenics depleted of nucleosomes (Bernstein et al.)

Yeast intergenics occupied by nucleosomes (Bernstein et al.)

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)

Segal et al., 2006

slide28

High Predictions

+ Controls

– Controls

In vivo nucleosome occupancies

at predicted high-occupancy locations

Segal et al., 2006

slide29

In vivo nucleosome occupancies

at predicted low-occupancy locations

Segal et al., 2006

slide30

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

slide31

Understanding and predicting the genome’s nucleosome-forming potential

Collect nucleosome-bound sequences

Validate model

Construct nucleosome-DNA interaction model

Compare within vivo positions

Predict intrinsically encodednucleosome organization

Associate intrinsic encoding with biological function

Segal et al., 2006

slide32

Average nucleosome occupancy

tRNAs

Telomeres

Centromere

Ribosomal RNAs

Divergent intergenic regions

Conserved DNA binding sites

Unique intergenic regions

Convergent intergenic regions

Ribosomal proteins

Autonomously replicating sequences

Protein coding regions

Nucleosome occupancy varies with chromosome region type

Segal et al., 2006

slide33

Does the genome’s intrinsic nucleosome organization facilitate occupancy of functional binding sites?

Segal et al., 2006

slide34

The yeast genome encodes low nucleosome occupancy over functional binding sites

Segal et al., 2006

slide35

Model

Permuted

Semi-stable nucleosomes

Semi-stable nucleosomes

Stable nucleosome

TATA Box

Distinctive nucleosome occupancy adjacent to TATA elements at yeast promoters

Segal et al., 2006

slide36

-36

-36

S. cerevisiae

-41

S. Paradoxus

-22

S. mikatae

-41

S. kudriavzevii

-42

S. bayanus

-23

S. castelli

-42

C. glabrata

-21

S. kluyveri

-16

K. walti

-53

K. lactis

-14

-9

A. gossypii

-26

D. hansenii

-56

C. albicans

Y. lipolytica

-3

A. nidulans

-2

S. pombe

-3

Nucleosome organization near 5’ ends of genes is conserved through evolution

Segal et al., 2006

slide37

Predicted nucleosome organization near 5’ ends of genes – comparison to experiment

Segal et al., 2006

slide38

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

slide39

Promoter region 1

Promoter region 2

0

100

200

0

100

200

20

0

-20

I

None

1

0

II

Factors present in the nucleus

1

0

III

+

1

0

/

Nucleosome score landscape

Stable nucleosome

DNA binding protein

/

Nucleosome occupancy

Unstable nucleosome

DNA binding site

Genome sequence influences the competition between nucleosomes and DNA binding proteins

Segal et al.,

2006

slide40

AATTTA

GC

AATTTA

GC

AATTTA

GC

GC

AATTTA

AATTTA

GC

GC

AATTTA

AATTTA

GC

Toward a proper free energy model for the sequence-dependent cost of DNA wrapping

Morozov, Segal, Widom, & Siggia

slide41

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.)