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Large Scale Organization of Chromatin

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Large Scale Organization of Chromatin LARGE SCALE ORGANIZATION OF CHROMATIN MARIO Nicodemi Dip.to di Fisica, Uni.NA “Federico II”, INFN In the cell nucleus chromosomes have a complex architecture serving vital functional purposes. Problem: how is their 3D structure orchestrated? Our contribution: a model of the molecular mechanisms of their self- organization by use of classical polymer physics. (Mirò, Chiffres & Constellations 1941) Research collaborators MRC, Imperial College, London Ana Pombo,, Mita Chotalia, Ines de Santiago, Liron-Mark Lavitas, Sheila Xie, Kedar Natarajan, Carmelo Ferrai, Robert Beagrie, ... Biology, McGill, CA Josée Dostie, James Fraser Physics, Univ. di Napoli, Italy Mariano Barbieri, Ilaria Cataudella, Antonio Scialdone, Melania Barile, Paolo Casale, Valentino Bianco, Emanuela de Falco, Deborah Pallotti, Gaetano Pellegrino, Andrea Piccolo, ... Chromatin organization (I) (A) Linear expression units in compact genomes v.s. spatially assembled units in complex genomes. (B) Colocalization of coregulated genes. (C) ChromatinGene localization organizationat transcription factories (TFs). Example: the Xicʼs of the X Chromosome territories and map chrom.s colocalize at XCI (Heard et al.; Lee et al. ʻ07) Chromatin organizationNuclear scale (I) (A) Linear expression units in compact genomes v.s. spatially assembled units in complex genomes. (B) Colocalization of coregulated genes. (C) Gene localization at transcription factories (TFs). Colocalization of coregulated genes at transcription factories Example: the Xicʼs of the X Transcription chrom.s colocalize at XCI Factory (Heard et al.; Lee et al. ʻ07) (Pictures: Dekker et al. Science ʻ08) Distal regulatory elements Gene Gene Expression Units Assembly of expression units Gene scale (Pictures: Bolzer et al. PLoS Bio. ʼ05; Dekker et al. Science ʼ08) (Pictures: Dekker et al. Science ʻ08) REPORTS of the genome inferred from Hi-C. More gen- To explore whether the two spatial compart- We repeated the above analysis at a resolution erally, a strong correlation was observed between ments correspond to known features of the ge- of 100 kb (Fig. 3G) and saw that, although the the number of Hi-C reads mij and the 3D distance nome, we compared the compartments identified correlation of compartment A with all other ge- between locus i and locus j as measured by FISH in our 1-Mb correlation maps with known genetic nomic and epigenetic features remained strong [Spearman’s r = –0.916, P =0.00003(fig.S3)], and epigenetic features. Compartment A correlates (Spearman’s r >0.4,P negligible), the correla- suggesting that Hi-C read count may serve as a strongly with the presence of genes (Spearman’s tion with the sole repressive mark, H3K27 trimeth- proxy for distance. r = 0.431, P <10–137), higher expression [via ylation, was dramatically attenuated (Spearman’s Upon close examination of the Hi-C data, we genome-wide mRNA expression, Spearman’s r =0.046,P <10–15). On the basis of these re- noted that pairs of loci in compartment B showed r =0.476,P <10–145 (fig. S5)], and accessible sults we concluded that compartment A is more aconsistentlyhigherinteractionfrequencyata chromatin [as measured by deoxyribonuclease I closely associated with open, accessible, actively given genomic distance than pairs of loci in com- (DNAseI) sensitivity, Spearman’s r =0.651,P transcribed chromatin. partment A (fig. S4). This suggests that compart- negligible] (16, 17). Compartment A also shows We repeated our experiment with K562 cells, Barbieri, Figure 1b,c ment B is more densely packed (15). The FISH enrichment for both activating (H3K36 trimethyl- an erythroleukemia cell line with an aberrant kar- data are consistent with this observation; loci in ation, Spearman’s r =0.601,P <10–296)and yotype (19). We again observed two compart- compartment B exhibited a stronger tendency for repressive (H3K27 trimethylation, Spearman’s ments; these were similar in composition to those Hi-C closeresults spatial localization. r = 0.282, P <10–56) chromatin marks (18). observed in GM06990 cells [Pearson’s r =0.732, b Polymer Thec contact Mean spatialprobability distance has a power-lawGyration behavior Radius Fig. 4. The local packing of A (Lieberman-Aiden, et al. Science ʻ09) B chromatin is consistent with the behaviorR of a fractal globule. (A) Contact probability as a function of genomic distance averaged Rg Rg across the genome (blue) shows a powers law scaling between -1.08 500 kb and 7 Mb (shaded re- gion) with a slope of –1.08Rg is (fit small Rg is large R = spatialshown indistance cyan). (B)Simulation on October 8, 2009 Contact Probability results for contact probability as = genomic distance s afunctionofdistance(1mono- Pc(s) =mer average ~ 6 nucleosomes contact ~ 1200 base probability pairs) (10)forequilibrium (red) and fractal (blue) globules. The slope for a fractal globule is In the 0.5-7.0Mb domain, P (s) very nearly –1(cyan),confirm- c α inghas our a predictionpower law (10). behavior The slope :CDPc(s)~1/s with α=1.08 for an equilibrium globule is –3/2, The valuematching α=1.08 prior is theoretical NOT found expec- in usual equilibrium polymer models (see, e.g., www.sciencemag.org Sachs et tations.al. ʻ95, TheMuenkel slope foret theal. fractalʻ98, Kreth et al. ʻ01, Bohn et al. ʻ07, ...), but in a precise transientglobule state closely (the resemblesFractal the Globule slope ) during, e.g., decompaction, where R(s) ~ s1/3. we observed in the genome. (C) Can(Top) Anchromatin unfolded polymer conformations chain, be described by a single state? 4000 monomers (4.8 Mb) long. Coloration corresponds to distance from one endpoint, ranging from blue to cyan, green, yellow, or- Downloaded from ange, and red. (Middle) An equi- librium globule. The structure is highly entangled; loci that are nearby along the contour (sim- ilar color) need not be nearby in 3D. (Bottom) A fractal globule. Nearby loci along the contour tend to be nearby in 3D, leading to monochromatic blocks both on the surface and in cross sec- tion. The structure lacks knots. (D)Genomearchitectureatthree scales. (Top) Two compartments, corresponding to open and closed chromatin, spatially partition the genome. Chromosomes (blue, cyan, green) occupy distinct territories. (Middle) Individual chromosomes weave back and forth between the open and closed chromatin compartments. (Bottom) At the scale of single megabases, the chromosome consists of a series of fractal globules. 292 9 OCTOBER 2009 VOL 326 SCIENCE www.sciencemag.org More data on Pc Different systems have different exponents. E.g., α ~ 1.6 in H1-hESC. Dixon et al. Hi-C (IMR90) ) Lieberman-A. et al. Hi-C (GM06990) c -9 10 Kalhor et al. Hi-C (GM12878) Kalhor et al. TCC (GM12878) Dixon et al. Hi-C (H1-hESC) Genome wide average data 10-10 across recent experiments, techniques and cell types =1.08 (DATA FROM: Lieberman-Aiden et al. =1.30 Science ʼ09; Kalhor et al. NBT ʼ11; contact probability (P =1.65 Dixon et al. Nature ʻ12) 10-11 0.5 1 2 5 10 20 genomic distance, log(Mbp) Cell type (Technique) Exponent α Ref. Human lymphoblast. (Hi-C) 1.08 (0.5-7.0Mb domain) Lieberman-A., et al. Science ʻ09 Sexton, et al. Cell ʼ12 Drosophila embryo (Hi-C) 0.85/0.70 genome wide/repressed Human lymphoblast. (TCC) different Pc in different regions Kalhor, et al. Nature Biotec. ʼ11 Pc of different chromosomes In a given system different chromosomes can have different exponents. A) Lieberman-Aiden et al. Hi-C (GM06990) B) Dixon et al. Hi-C (IMR90) =0.93 =0.93 -9 -9 10 =1.08 10 =1.08 =1.30 =1.30 -10 -10 10 Mean 10 Mean Chr.X Chr.X Chr.11 Chr.11 Chr.12 Chr.12 contact probability (Pc) Departures from average contact probability (Pc) -11 Chr.19 -11 Chr.19 are consistent across 10 10 0.5 1 2 5 10 20 0.5 1 2 5 10 20 systems (panels A-C), genomic distance, log(Mbp) genomic distance, log(Mbp) with notable exceptions, e.g. H1-hESC (panel D). C) Kalhor et al. TCC (GM12878) D) Dixon et al. Hi-C (H1-hESC) =0.93 =0.93 -9 -9 10 =1.08 10 =1.08 =1.30 =1.30 =1.65 -10 -10 10 Mean 10 Mean Chr.X Chr.X Chr.11 Chr.11 Chr.12 Chr.12 contact probability (Pc) contact probability (Pc) Chr.19 Chr.19 10-11 10-11 0.5 1 2 5 10 20 0.5 1 2 5 10 20 genomic distance, log(Mbp) genomic distance, log(Mbp) FISH data FISH data show different behaviors in different loci and a plateau in R(s) (Mateos-Langerak et al. PNAS ʼ09) (Jhunjhunwala et al. Cell ʼ08) Fibroblasts, Chr.11 Pro-B cells, Chr.12 10 0.6 8 ) ) 2 2 m m 0.4 6 ( ( 2 2 R 4 R 0.2 2 Fractal Globule Fractal Globule Barbieri, Figure 1b,c 0 0.0 0 10 20 30 40 50 60 70 80 0.0 0.5 1.0 1.5 2.0 2.5 3.0 genomic distance (Mbp) genomic distance (Mbp) b Polymer c Mean spatial distanceExamplesGyration of FISH Radius data across different experiments and cell types R The mean square distance, R(s), typically has a power-law R R increaseg at small genomicg separations, s, and then a plateau. s Details can be different in different systems. Rg is small Rg is large Contact Probability Barbieri, Figure 1b,c Barbieri, Figure 1b,c A schematic model (Nicodemi&PriscoA schematic Biophys. J. ʻ 09)model (Nicodemi&Prisco Biophys. J. ʻ09) Chromatin conformations, and patterns, can arise by interactionb Polymer with randomly c Mean spatial distance Gyration Radius Chromatin conformations, and patterns, can arise by interactionb diffusingPolymer with DNA-binding randomly molecules c Mean spatial distance Gyration Radius diffusing DNA-binding molecules Questions: how are i) stable conformations (and R theirBarbieri, scaling properties Figure) established 1b,c? ii) R R architectural changes regulated? g g Questions: how are i) stable conformations (and R s Strings&Binders Switch (SBS) model: their scaling properties) established? ii) Rg is small Rg is large System: a SAW polymer and a density, cm, of (Nicodemi&Prisco Biophys.
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