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 for et the al. 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 Drosophila embryo (Hi-C) 0.85/0.70 genome wide/repressed Sexton, et al. Cell ʼ12
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. J. ʻ09) Contact Probability Rg Rg architectural changesA schematic regulated? model diffusing molecules with an affinity EX for its A schematic model binding sites (a fraction f of the beads, e.g., 1/4). Chromatin conformations, and patterns, can arise by interaction withBarbieri, randomly Figure 1b,c Equilibrium/Dynamicsb : studiedPolymer by Mean-Field cs Mean spatial distance Gyration Radius diffusing DNA-binding molecules theory & Monte Carlo simulations (on a lattice). Barbieri, Figure 1b,c Strings&Binders Switch (SBS) model: A schematic model (Nicodemi&Prisco Biophys. J. ʻ09) Rg is small Rg is large Chromatin conformations, and patterns, can arise by interactionb Polymer with randomly c Mean spatial distance Gyration Radius diffusing DNA-binding molecules R System: a SAW polymer and a density, cm, of Barbieri, Figure 1b,c (Nicodemi&Prisco Biophys. J. ʼ09; Barbieri et al. PNAS ʼ12) Barbieri,Questions: how are i) stable conformations (and Figure A schematic 1b,c modelContact(Nicodemi&Prisco Probability Biophys. J. ʻ09) diffusing moleculestheir scaling with an properties affinity EX for) established its ? ii) Questions : how are i) stable conformationsChromatin (and conformations, and patterns, can arise by interactionb Polymer withR randomly c Mean spatial distance Gyration Radius binding sites (a fraction f of the beads, e.g., 1/4). their scaling properties) establisheddiffusing? ii) DNA-bindingBarbieri, moleculesFigure 1b,c Rg Rg architectural changes regulated? R R architectural changes regulated? g g R Questions: how are i) stable conformations (and s their scaling properties) established? ii) (Nicodemi&Priscos Biophys. J. ʻ09) Equilibrium/Dynamics: studied by Mean-Field (Nicodemi&Prisco Biophys. J. ʻ09) R Strings&BindersA Aschematic schematic SwitchBarbieri, (SBS) model:architectural model Figure changes regulatedmodel 1b,c? g Rg Rg is small Rg is large theory & Monte Carlo simulations (on a lattice). Chromatin conformations, and patterns, can arise by interaction with randomly System: a SAW polymer and a density, cm, of b Polymer c Mean spatial distance Gyrations Radius Barbieri, FigureChromatin 1b,cdiffusing DNA-binding conformations, molecules and patterns, can arise byContact interaction Probability with randomly Strings&Binders Switch (SBS) model:diffusing molecules with an affinity EX for its b Polymer Rg cis small Mean spatialRg is large distance Gyration Radius binding sites (a fraction f of the beads, e.g.,Strings&Binders 1/4). Switch (SBS) model: diffusing DNA-binding molecules Rg is small Rg is large R System: a SAW polymer and a density, cm, ofQuestions : how are i) stable conformationsSystem: a SAW (and polymer and a density, cm, of Equilibrium/Dynamics: studied by Mean-Field Contact ProbabilityContact Probability (Nicodemi&Prisco Biophys.their J.scaling ʻ09) properties) diffusingestablished molecules? ii) with an affinity EX for its A schematic model theory & Monte Carlo simulations (on a lattice). R R diffusing molecules with an affinity EX for itsarchitectural changes regulated(Nicodemi&Priscobinding? sites (a fraction Biophys. f of the beads, J. e.g., ʻ09) 1/4). g g AChromatin schematic conformations, and patterns, can arise by interactionmodel with randomly b Polymer c Mean spatial distance Gyration Radius R bindingdiffusing sites DNA-binding (a fraction molecules f of the beads, e.g.,Questions 1/4). : how are i)Equilibrium/Dynamics stable conformations: studied by Mean-Field (and s theory & Monte Carlo simulations (on a lattice). Chromatin conformations, and patterns, canStrings&Binders arise by Switch interaction (SBS) model: with randomly their scaling propertiesb )Polymer established? ii) c Mean spatial distanceRg is small Rg is large Gyration Radius Questions: how are i) stable conformations (and R diffusing DNA-binding molecules System: a SAW polymer and a density, cm, of R R Equilibrium/Dynamicstheir scaling properties) established: studied? ii) by Mean-Fieldarchitectural changes regulated? Contact Probability g g diffusing molecules with an affinity EX for its architectural changes regulated? Rg Rg Scenario: chromatin conformations arise by theory & Monte Carlo simulations (on a lattice).binding sites (a fraction f of the beads, e.g., 1/4). s Equilibrium/Dynamics: studied by Mean-Field R s QuestionsStrings&Binders: how Switch are (SBS) i) model:stable conformations theory(and & Monte Carlo simulations (on a lattice). Rg is small Rg is large theirSystem : scaling a SAW polymer properties and a density, cm, of) established Strings&Binders? ii) Contact Switch Probability (SBS) model: diffusing molecules with an affinity EX for its R is small R is large Rg Rg g g architecturalbinding sites (a fraction changes f of the beads, regulated e.g., 1/4). ? System: a SAW polymer and a density, cm, of Contact Probability interaction with randomly diffusing DNA-binding Equilibrium/Dynamics: studied by Mean-Field theory & Monte Carlo simulations (on a lattice). diffusing molecules with an affinity EX for its s binding sites (a fraction f of the beads, e.g., 1/4). Strings&Binders Switch (SBS) model: Rg is small Rg is large Equilibrium/Dynamics: studied by Mean-Field System: a SAW polymer and a density, cm, of theory & Monte Carlo simulations (on a lattice). Contact Probability molecules diffusing molecules with an affinity EX for its binding sites (a fraction f of the beads, e.g., 1/4). zoom
Equilibrium/Dynamics: studied by Mean-Field theory & Monteb Carlo simulations (on a lattice). Polymer c Mean spatial distance Gyration Radius Questions: how are i) different conformations (and their scaling properties) established? and ii) architectural changes reliably regulated?; iii) is such a scenario compatible with Hi-C and FISH data? R
R R g g The Strings&Binders Switch (SBS) model: s
System: a SAW polymer chain and a density, cm, of R is small R is large diffusing molecules with an affinity EX for the polymer g g binding sites. Contact Probability Equilibrium/Dynamics: subject to the laws of physics, the emergent properties of the model are studied by Mean-Field theory & Monte Carlo simulations (on a lattice). The SBS concept Conformations display switch-like responses to changes in binder concentration
Loops formed by (unlikely) random bridges are unstable: Low concentr.: bonds likely to break. binding "Open" polym. molecule
If the concentration of molecules, cm, is above a given threshold, ctr, different bridges stabilize each other, and contacts become stable.
High concentr.: bonds likely to persist. bridging "Closed" polym. molecules
At ctr a phase transition occurs as entropy loss due to polymer looping is compensated by energy gain of particles binding twice the polymer. Stable conformations The folding state depends on the concentration/affinity of the bridging molecules. Architectural classes correspond to stable emergent phases (a variety of off- equilibrium conformations exist, including the ʻfractal globuleʼ).
SBS phase diagram A thermodynamic transition line separates different phases corresponding to different T) 2.5 compact B stable architectural classes
/k (see, e.g., de Gennes 1979). X
2.0 Below / at / above threshold, fractal the polymer conformation is: a) open 1.5 open b) Θ-point fractal c) compact
binder affinity binder (E 1.0 Conformational changes are switch-like, controlled by molecule upregulation (cm) or 5 10 25 chemical modifications (Ex). binderconcentration concentration (c ,(c nmole/l)m, nmole/l) Contact probability The exponent α depends on binder concentration
100 compact 2 (s)) c 10-1 = 1.1 open fractal 1 10-2 compact cm=25.0 open c -3 cm=10.4 tr 10 cm=5.0 contact probability (P (nmole/l) 0 4 40 400 5 10 25
genomic distance (s/s0) concentration (cm, nmole/l)
In the SBS model, Pc(s) is power α law with an exponent depending Pc(s)~1/s with α=α(cm) on the concentration of binders: more data ... Fibroblasts, Chr.11 Pro-B cells, Chr.12 Mean spatial distance Mixtures of FISHstates data (left, Mateos- Langerak et al. PNAS ʼ09; right, A combination of open and closedJhunjhunwala chromatin et al. Cell explains ʼ08). the observed exponents
In the SBS model different fractions, f, of open (α=2.1) and compact (α=0) system = f +(1-f) domains give a Pc(s) with different α open compact (in a given s range) with 0 < α < 2.1: s f = fraction of open chromatin 0 10 SBS model =0.93
(s)) ... as seen in experiments: c =1.08 =0.93 =1.30 -9 Hi-C data 10 =1.08 Contact Probability =1.30 Hi-C data (Lieberman- -1 Aiden, et al. Science ʻ09): 10 -10 f=0.45 10 Mean Chr.X f=0.60 Chr.12
contact probability (P Chr.11 contact probability (Pc) f=0.80 contact probability (Pc) Chr.19 10-11 10 102 0.5 1 2 5 10 20 genomic distance, log(Mbp) genomic distance (s/s0) (Lieberman-A., et al. Science ʻ09) Mean square distance The exponent v depends on binder concentration )
2 c =5.0 0.6 0 m 3 cm=10.4 10 cm=25.0 open (s)/d 2 (nmole/l) fractal open
= 0.39 102 0.3
compact ctr compact
101
mean square dist. (R 0.0 4 40 400 5 10 25
genomic distance (s/s0) concentration (cm, nmole/l) s In the SBS model, R2(s) is power 2 2v law with an exponent depending R (s) ~ s with v=v(cm) on the concentration of binders: Model v.s. experiments The SBS model explains a range of observed chromatin behaviors Barbieri, Figure 1b,c Mean spatial distanceModel v.s.Fibroblasts, experiments Chr.11 Pro-B cells, Chr.12 FISH data (left, Mateos- Langerak et al. PNAS ʼ09; right,The SBS model explains a range of observed chromatin behaviors b Polymer cJhunjhunwala Mean spatial et al. distance Cell ʼ08). Gyration Radius Fibroblasts, Chr.11 Pro-B cells, Chr.12
FISH mean spatial distance 10 0.6 data are well described by the R 8 ) ) 2 SBS model, for example (left) in 2
m m 0.4 its ʻcompactRʼ gconformation. Rg 6 ( ( 2 2
R 4 R s (DATA FROM: Mateos-L. et al. PNAS 0.2 ʼ09; Jhunjhunwala et al. Cell ʼ08) 2 FG FG Rg is small Rg is large SBS SBS 0 0.0 Contact Probability 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) =0.93 -9 Hi-C data 10 =1.08 =1.30 Contact Probability Hi-C data (Lieberman-
Aiden, et al. Science ʻ09): -10 10 Mean Hi-C contact probability data across different Chr.X chromosomes and systems have different Chr.12 exponents, falling in the range predicted by Chr.11 SBS model (0 to 2.1). contact probability (Pc) contact probability (Pc) Chr.19 10-11 (DATA FROM: Lieberman-A., et al. Science ʼ09) 0.5 1 2 5 10 20 genomic distance, log(Mbp) Dissecting the different states The moment ratio
5.0 SBS model ctr 2 >
2 fractal open compact
>/ 1.0 5 10 25 concentration (cm, nmole/l) In the SBS model (Mateos-L. et al. PNAS ʼ09) Chr.1 LD 5.0 FISH data Chr.1 R Chr.11 LD IgH locus pB 2 4 2 2 IgH locus ppB > SBS model: >/ >/ 4 2 2 1.0 FISH data: the moment ratio, The SBS is the only model explaining the observed range of A picture of chromatin Chromatin is a complex mixture of differently folded regions compact A specific locus (of a given cell/locus 1 cell) can be folded in different conformations. The SBS model describes some of the mechanisms of self- cell/locus 2 open organization. The locus has α A thermodynamic transition line~ 0 separates / 1.5 / 2 if in a compactdifferent / phases corresponding to different stablefractal architectures / open state, according. Below/at/above fractal to its local binders. Gyration threshold live the a) open polymer, b) Θ-point fractal, c) compact globule Radius,cell/locusRg 3 conformations: ...... Average Pc(s), and its exponent α~1, from Hi-C is an average over loci in different states, e.g. open/fractal/compact, and depends on relative fractions. Additional quantities, e.g. • The Strings&Binders (SBS) model illustrates how polymer architectural patterns can be established and regulated by randomly diffusing binding molecules via thermodynamical mechanisms (Nicodemi et al. Biophys.J. 2009). • Folding classes correspond to stable emergent phases (a variety of transient conformations exist, including the ʻfractal globuleʼ). Conformational changes can be sharply controlled by simple strategies, e.g., protein upregulation or chromatin modification. • Hi-C and FISH data can be rationalized in the SBS scenario (Barbieri et al. PNAS 2012). The emerging picture is that chromatin is a complex mixture of differently folded regions, self-organized across spatial scales by SBS-like physical mechanisms ... but is it correct? Chromatin organization (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). Example: the Xicʼs of the X chrom.s colocalize at XCI (Heard et al.; Lee et al. ʻ07) (Pictures: Dekker et al. Science ʻ08) Chromatin organization (II) (D) Zoom: (Top) Chromosomes territories (CTs); (Middle) Open and closed chromatin within a CT; (Bottom) Complex structure at the Mbp scale (pictures: Lieberman-Aiden et al. Science ʻ09; Bolzer et al. PLoS B.ʼ05; Cremer et al. Nat.Gen.ʼ07). Folding state: the gyration radius The folding state depends on the concentration/affinity of the bridging molecules R g! 1.0 open fractal compact Switch controls AFFINITY CONCENTRATION BINDING SITES 1.0 2 Etr Ctr f tr Rg 0.6 fractal (rnd) g 0.2 2 1234-4 -3 -2 -1 -2 -1 0.6 Ex/kT log(c[%]) log(f) /R g Barbieri, Figure 1b,c! 2 R ctr b! Mean spatial distance! Gyration Radius! c! Polymer! compact R R R g! 0.2 g! Binding sites! s! 5 10 25 Rg is small! Rg is large! binder concentration (cm, nmole/l) Contact Probability! concentrationBinding (c , nmole/l) molecule! Pc(s) = 0.33 s! 9 702.&"829.!"#:;<%'("#1.+#.+,1.)0'121. The HoxB locus in!"#$%&'( ES)*+)$,')(-($'.)!"#$"%&'("#)"/)"$()+*%&)0,1('() The state of genes and their interaction with Transcr.1/2)34'5*6 Factories7)$,')#*&&'(0*/2"/8 explain))%"*+&+,-Snx11.&',%+* ControlarchitectureHoxb19) Hoxb13 Snf8 Snx11 Control Hoxb1Hoxb13 Snf8 The Hox B locus 9 /0'12.3. /0'12.4. /0'12.5. /0'12.6. Snx11 10 ! ! ! ! ! ! ! Hox B genes in UES cells are silent "'2 ! Control ,3%"" !" !8 • )&%" #$%&"'(9E+12!+!( "*+ Hoxb1 but primed for expression (bivalent), Snx11 Control Hoxb1Hoxb13 Snf8 Snx11 Control Hoxb1Hoxb13 Snf8 !0(9E+124!!( 4'(F:;;!/"#$%&%'()&*'"%$)%0)-2)*01)-$!-"%&-0',) i.e., marked by repressive PRC and 1"(#$%&0(( 4+().;<$<$8(( Snx11 Hoxb13 18(#$%&2(( !! !!! 1'(#$%&1( !! ! 14(#$%&!( *4(9:/47"(( poised RNAPII with phosphorylation (1!(#$%&4( **(=&>8?(( 10 Control Snf8 11(9E++82!+( 12(#$%&*( *+(,352(( ( 2+(#$%&'( of Ser5, but not Ser2 residues (Stock ( Snx11 Control Hoxb1Hoxb13 Snf8 Snx11 Control Hoxb1Hoxb13 Snf8 Hoxb1 28(#$%&8( FIG. 8. Four typical configurations at equilibriumSnx11 correspondingControl toHoxb1 the four RgHoxb13 energetic regionsSnf8 in fig.5. RNAPII Concentra-Snx11 Control Hoxb1Hoxb13 Snf8 ( ! 3 3 2*(#$%&"( ! tions are CB = CA =2 10− d0− . ( '!(6758&/"(( !! · Energetic values are: ! ,-./"((01 ! !"#$!"#$%&%'()&*'"%$)%&'($#)*#&+(#,-./$0.-.$+1$2'(*.34/#! $5+-"$!+*,-).) Snx11 ! ( REGION1 (top left box): EB =1.0, EA =1.0(KB T ) ! ! Snx11! et al. 2007; Brookes&Pombo 2009). /"#$%&%'()&*'"%$)%0)-2)*01)-$!-"%&-0',REGION2) (top right box): EB =1.0, EA =4.0(KB T ) Hoxb13 ! REGION3 (bottom left box): EB =4.0, EA =1.0(KB T ) 6-"#&#$.&#$,'$-7,.4/#$*.&.(#-#&189$ REGION4 (bottom right box): EB =4.0, EA =4.0(KB T ) Control !!!! Control Snf8 "8(BC$D/C$"( @*7.;3:8( Hoxb1 They have a preferential location !"#$%&'()*+, 8*( Hoxb1 "*((9&G' Snx11 Control Hoxb1Hoxb13 Snf8 Snx11 Control Hoxb1Hoxb13 Snf8 • /"#$%&%'()&*'"%$)%0)-2)*01)!"#$!"#$%&%'("4((A37:8 )&*'"%$-$!-"%&-0',)%&'($#)*#&+(#,-./) $0.-.$+1$2'(*.34/#Hoxb13 $5+-"$!+*,-).) in their CT (Chamberyon&Bickmore BIVALENT gene "1((9E"''084 Snx11 Hoxb13 Snx11 Control Hoxb1FIG. 8. FourHoxb13 typical configurationsSnf8 at equilibriumSnx11 correspondingControl to the fourHoxb1 Rg energeticHoxb13 regions inSnf8 fig.5. RNAPII Concentra- ACTIVE gene 6-"#&#$.&#$,'$-7,.4/#$*.&.(#-#&189$ 3 3 tions are CB = CA =2 10− d0− . Dev.‘04-08). In the first four days of Energetic values are: · Snf8 Control REGION1 (top left box): EB =1.0,Snx11EA =1.0(KB T ) Snf8 REGION2 (top right box): EB =1.0, EA =4.0(KB T ) REGION3 (bottom left box): EB =4.0, EA =1.0(KB T ) FIG. 9. Contact Proximity Matrix. Probability of finding two chain sites within 3d0 distance, from simulations performed in 3 3 REGION4 (bottom right box): EB =4.0, EA =4.0(KB T ) the four Rg energetic stable regions (fig.5). RNAPII Concentrations are CB = CA =2 10− d0− . differentiation, only the Hoxb1 gene in the locus becomes active and Snx11 Control Hoxb1Hoxb13 Snf8 Snx11 Control Hoxb1Energetic valuesHoxb13 are: Snf8 Hoxb1 · Control REGION1 (top left box): EB =1.0, EA =1.0(KB T ) !"#$!"#$%&%'(The)&*'"%$ model)%&'($ predicts#)*#&+(#,-./ four$0.-.$ phases+1$2'(*.34/#. Conformations$5+-"$!+*,-).) and proximityREGION2 (top right box):matricesEB =1.0, EA =4.0(KB T ) in Snx11 REGION3 (bottom left box): EB =4.0, EA =1.0(KB T ) separates from the others (a simple model system?). REGION4 (bottom right box): EB =4.0, EA =4.0(KB T ) 6-"#&#$.&#$,'$-7,.4/#$*.&.(#-#&189$ Hoxb1 Hoxb13 Snx11 Control Hoxb1 Hoxb13 Snf8 the four phases (above).Snx11 Control ExperimentsHoxb1Hoxb13 Snf8 Snx11 (below)Control Hoxb1Hoxb13 areSnf8 compatible with phase 4. Control Snx11 Snx11 Hoxb13 Snf8 Hoxb1 Control Q.: does gene state define locus architecture by interaction with TFs? Control Snf8 • Model: phase 1 phase 2 phase 3 phase 4 Cryo-FISH data Hoxb13 Hoxb1 FIG. 9. Contact Proximity Matrix. Probability of finding two chain sites within 3d0 distance, from simulations performed in Hoxb1 3 3 Snx11 Control Hoxb1Hoxb13 Snf8 Snx11 Control Hoxb1Hoxb13 Snf8 Snx11 Control Hoxb1the fourHoxb13 Rg energeticSnf8 stable regions (fig.5). RNAPIISnx11 ConcentrationsControl are CBHoxb1= CA =2 Hoxb1310− d0− . Snf8 Snx11 Control Hoxb1 Hoxb13 Snf8 Energetic values are: · REGION1 (top left box): EB =1.0, EA =1.0(KB T ) Active/Bivalent genes are known to localize into distinctSnx11 transcription REGION2 (top right box): EB =1Snx11.0, EA =4.0(KB T ) Snf8 Snx11 Hoxb13 Hoxb13 REGION3 (bottom left box): EB =4.0, EA =1.0(KB T ) • REGION4 (bottom right box): EB =4.0, EA =4.0(KB T ) factories (TFs) of active and poised RNAPII complexes ofControl 8 PolII each. Control Control Snf8 ∼ Snf8 Hoxb1 Hoxb1Snx11 Control Hoxb1 Hoxb13 Snf8 Hoxb1 Snx11 Control Hoxb1Hoxb13 Snf8 Snx11 Control Hoxb1Hoxb13 Snf8 Snx11 Hoxb13 Snx11 Hoxb13 Hoxb13 Control Snf8 Control Snf8 Snf8 Hoxb1 Hoxb1 Snx11 Control Hoxb1Hoxb13 Snf8 Snx11 Control(Barbieri,Hoxb1Hoxb13 Snf8 Jesus, Xie, Pombo, MN in prep. 2012) Snx11 Hoxb13 Hoxb13 Snx11 Control Hoxb1 Hoxb13 Snf8 Snx11 Control Snf8 Snf8 Hoxb1 Control Hoxb13 Hoxb1 Snx11 Control Hoxb1 Hoxb13 Snf8 Snx11 Snf8 Hoxb13 Control Snf8 Hoxb1 Snx11 Control Hoxb1 Hoxb13 Snf8 Snx11 Hoxb13 Control Snf8 Hoxb1 Hoxb13 Snf8