A sequence-dependent physical model of nucleosome occupancy along the eukaryotic chromatin fiber. Alain Arneodo

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1 A sequence-dependent physical model of nucleosome occupancy along the eukaryotic chromatin fiber Alain Arneodo Laboratoire de Physique, Ecole Normale Supérieure de Lyon, France Benjamin Audit Françoise Argoul ENS de Lyon, France Antoine Baker Zofia Haftek-Terreau Guillaume Chevereau Monique Marilley Julien Moukhtar Pascale Milani Leonor Palmeira Cédric Vaillant Yves d Aubenton-Carafa Claude Thermes CGM, Gif-sur-Yvette, France

2 To which extent does the DNA sequence code for the 3D structure of chromatin?

3 PERIODIC DISTRIBUTION OF DNA BENDING SITES FAVORS NUCLEOSOME FORMATION Crothers, Travers, Trifonov, Widom - E 1 helix turn ~10 bp

4 PERIODIC DISTRIBUTION OF DNA BENDING SITES Luger et al., Nature (1997) Segal et al., Nature (2006) But it concerns only a small proportion of nucleosomes (15% according to Peckham et al., Genome Res. (2007))

5 Modeling sequence effect on nucleosome organisation

6 Parking phenomenon by excluding energy barriers Percus, J. Stat. Phys. (1976) Vanderlick et al., Phys. Rev. A (1986) Kornberg & Stryer, Nucleic Acids Res. (1988)

7 COMPUTING THE FREE-ENERGY NECESSARY TO BEND THE DNA DOUBLE HELIX TO FORM NUCLEOSOMES Vaillant et al., Phys. Rev. Lett. (2007) E(s) Local polymer structure locally defined by 3 angles: and 3 flexibilities: Equilibrium configuration: : structural tables (crystallography, AFM, molecular dynamics)

8 Trinucleotidic structural tables based on experiments Nucleosome positioning local curvature Dnase I sensitivity Local flexibility

9 Physical modeling of in vitro and in vivo nucleosome occupancy Yeast chromosome 12

10 Genome-wide prediction of nucleosome occupancy versus in vitro data Chevereau et al., Phys. Rev. Lett. (2009) in vitro data from Kaplan, Nature (2008). in vivo data from Lee Nature Genetics (2007).

11 Histogram of Pearson correlation values between model predictions and in vitro (Kaplan, 2009) and in vivo (Lee, 2007) data r r d M (bp) Our physical model at 30% coverage Our physical model at 75% coverage Yuan and Liu (2008) model based on statistical learning Theoretical energy landscape Random occupancy landscape

12 Fragment from S. Cerevisiae Chr. 7 (595 bp) including gene YGR105W (450 bp). One nucleosome: AFM data Predicted profile (sym.) Milani et al., PNAS (2009) Two nucleosomes: AFM data Predicted profile (sym.) In vivo data Predicted profile

13 Intrinsic versus extrinsic nucleosome positioning

14 Chevereau et al., Phys. Rev. Lett. (2009) Nucleosome Free Regions at transcription start and end sites Physical model Energy landscape In vivo In vivo In vitro Lee et al., Nat. Genet. (2007) Kaplan et al., Nature (2009)

15 In vivo nucleosome organization in Yeast genes Chevereau et al., Phys. Rev. Lett. (2009) Vaillant et al., Genome Res. (2009)

16 Prediction of nucleosome occupancy in genes versus in vivo data Chevereau et al., Phys. Rev. Lett. (2009) Vaillant et al., Genome Res. (2009)

17 Bi-stable gene: superposition of two crystal states Chevereau et al., Phys. Rev. Lett. (2009)

18 Prediction of nucleosome occupancy in genes versus in vivo data Vaillant et al., Genome Res. (2009)

19 Bi-stable genes present a higher transcriptional plasticity Vaillant et al., Genome Res. (2009)

20 Nucleosome Free Regions are observed at replication origins (Eaton, Genes & dev. (2010))

21 ORC binding induces ordering of flanking nucleosomes modeling NFR at ORC-ACS is coded in the sequence

22 Nucleosomal occupancy and replication timing at ORC-ACS Experiments Modeling Lee, Nature Genetics (2007) early high (100%) efficiency mid (~85%) late low (<80%) efficiency Kaplan, Nature (2008) Chevereau, Phys. Rev. Lett. (2009) Vaillant, Genome Res. (2010) In vivo In vitro - Timing data: McCune, Genetics (2008) - Efficiency Multiple Initiator Model: Yang, Mol. Syst. Biol. (2010)

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24 A sequence-dependent physical model of nucleosome occupancy along the eukaryotic chromatin fiber Alain Arneodo Laboratoire de Physique, Ecole Normale Supérieure de Lyon, France Benjamin Audit Françoise Argoul ENS de Lyon, France Antoine Baker Zofia Haftek-Terreau Guillaume Chevereau Monique Marilley Julien Moukhtar Pascale Milani Leonor Palmeira Cédric Vaillant Yves d Aubenton-Carafa Claude Thermes CGM, Gif-sur-Yvette, France

25 Long-range correlations in genomic DNA: A signature of the nucleosomal structure Audit et al., Phys. Rev. Lett. (2001) J. Mol. Biol. (2002) long-range correlations polymer intrinsically more compact Vaillant et al., Phys. Rev. Lett. (2005) Eur. Phys. J. E (2006)

26 Genome-Scale Identification of Nucleosome Positions in S. cerevisiae (Yuan et al., Science 309 (2005)) Chr III Hybridisation of nucleosomal DNA on chip

27 Statistical characterization : I Experiment Vaillant et al., Phys. Rev. Lett. (2007) ν = 2H-1 Large-scale disorder of nucleosome positioning Long-range correlations between 200 and 1000bp with H~0.8 H = 0.74 Small-scale periodic component at 160 bp Weak positioning: fat tail (exponential) statistics

28 Statistical characterization : II One nucleosome model Vaillant et al., Phys. Rev. Lett. (2007) Miele et al., Nucleic Acids Res. (2008) ν = 2H-1 One nucleosome energy landscape based on a simple sequence dependent helical model H = 0.80 H = 0.74 Radius R = 4.19 nm Pitch P = 2.49 nm Total length l = 125bp

29 Statistical characterization: III Many nucleosomes simulations Vaillant et al., Phys. Rev. Lett. (2007) ν = 2H-1 Many nucleosomes Grand Canonical Monte- Carlo simulations of the nucleosomal array Chemical potential: 1nuc / 200bp H = 0.73 H = 0.74 Experimental evidence that long-range correlations influence nucleosomal organisation

30 genes inter-genes

31 PREDICTED PATTERNS CAN SLIGHTLY DIFFER FROM OBSERVED PATTERNS WILD TYPE PATTERNS = PREDICTED PATTERNS + REMODELING?

32 ALTERNANCE OF CRYSTAL-LIKE AND BI-STABLE PATTERNS 4/5 bi-stable 5/6 bi-stable 4 nucl. 5 nucl.

33 ALTERNANCE OF CRYSTAL-LIKE AND BI-STABLE PATTERNS 5 nucl. 5/6 bi-stable 4/5 bi-stable 5/6 bi-stable 4 nucl. 5 nucl. 6 nucl.

34 ALTERNANCE OF CRYSTAL-LIKE AND BI-STABLE PATTERNS 5 nucl. 5/6 bi-stable 4/5 bi-stable 5/6 bi-stable 4 nucl. 5 nucl. 6 nucl.

35 ALTERNANCE OF CRYSTAL-LIKE AND BI-STABLE PATTERNS 5 nucl. 5/6 bi-stable 4/5 bi-stable 5/6 bi-stable 4 nucl. 5 nucl. 6 nucl. Does this distribution result from energy barriers only?

36 Prediction of nucleosome occupancy in genes versus in vivo data Chevereau et al., Phys. Rev. Lett. (2009)

37 Crystal Crystal Bi-stable

38 Most intron containing genes are bi-stable Vaillant et al., Genome Res. (2009)