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Distinct polymer physics principles govern chromatin dynamics in mouse and Drosophila topological domains.

Ea V, Sexton T, Gostan T, Herviou L, Baudement MO, Zhang Y, Berlivet S, Le Lay-Taha MN, Cathala G, Lesne A, Victor JM, Fan Y, Cavalli G, Forné T - BMC Genomics (2015)

Bottom Line: Using simple polymer models, we previously showed that, in mouse liver cells, gene-rich domains tend to adopt a statistical helix shape when no significant locus-specific interaction takes place.Interestingly, this statistical helix organization is considerably relaxed in mESC compared to liver cells, indicating that the impact of the constraints responsible for this organization is weaker in pluripotent cells.Finally, depletion of histone H1 in mESC alters local chromatin flexibility but not the statistical helix organization.

View Article: PubMed Central - PubMed

Affiliation: Institut de Génétique Moléculaire de Montpellier, UMR5535, CNRS, Université de Montpellier, 1919 Route de Mende, 34293, Montpellier, Cedex 5, France. vuthy.ea@igmm.cnrs.fr.

ABSTRACT

Background: In higher eukaryotes, the genome is partitioned into large "Topologically Associating Domains" (TADs) in which the chromatin displays favoured long-range contacts. While a crumpled/fractal globule organization has received experimental supports at higher-order levels, the organization principles that govern chromatin dynamics within these TADs remain unclear. Using simple polymer models, we previously showed that, in mouse liver cells, gene-rich domains tend to adopt a statistical helix shape when no significant locus-specific interaction takes place.

Results: Here, we use data from diverse 3C-derived methods to explore chromatin dynamics within mouse and Drosophila TADs. In mouse Embryonic Stem Cells (mESC), that possess large TADs (median size of 840 kb), we show that the statistical helix model, but not globule models, is relevant not only in gene-rich TADs, but also in gene-poor and gene-desert TADs. Interestingly, this statistical helix organization is considerably relaxed in mESC compared to liver cells, indicating that the impact of the constraints responsible for this organization is weaker in pluripotent cells. Finally, depletion of histone H1 in mESC alters local chromatin flexibility but not the statistical helix organization. In Drosophila, which possesses TADs of smaller sizes (median size of 70 kb), we show that, while chromatin compaction and flexibility are finely tuned according to the epigenetic landscape, chromatin dynamics within TADs is generally compatible with an unconstrained polymer configuration.

Conclusions: Models issued from polymer physics can accurately describe the organization principles governing chromatin dynamics in both mouse and Drosophila TADs. However, constraints applied on this dynamics within mammalian TADs have a peculiar impact resulting in a statistical helix organization.

No MeSH data available.


Related in: MedlinePlus

Fitting the statistical helix model to contact frequencies quantified in mESC. Quantitative 3C data were obtained from wild-type mouse ESC in five gene-rich TADs (a), two gene-poor TADs (b) and one gene-desert TAD (c) (see genomic maps in Additional file 1). For each type of TAD, data obtained from all the anchor primers used for each locus (Additional file 7) were compiled in a single graph (each locus is represented by a specific color). Error bars are standard error of the mean of three independent quantitative 3C assays each quantified at least in triplicate. Dashed lines delimit supranucleosomal domains that encompass separation distances where contact frequencies are alternatively lower and higher (see Methods). The graphs show the best fit analyses obtained with the unconstrained chromatin model [eqs. 1 and 2] (black curves) or the statistical helix model [eqs. 1 and 3] (red curves). Correlation coefficients (R2) are indicated on the graphs. Best fit parameters, and the genomic distance contained within one statistical helix turn (Sh in kb), are given in the upper part of Table 1. For each supranucleosomal domains, the mean contact frequencies and the number (n) of experimental points are indicated on the graphs. p-values (Mann–Whitney U-test) account for the significance of the differences observed between the experimental means of two adjacent domains (double asterisks indicate a p-value < 0.05 and > 0.01 and triple asterisks a p-value < 0.01)
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Fig2: Fitting the statistical helix model to contact frequencies quantified in mESC. Quantitative 3C data were obtained from wild-type mouse ESC in five gene-rich TADs (a), two gene-poor TADs (b) and one gene-desert TAD (c) (see genomic maps in Additional file 1). For each type of TAD, data obtained from all the anchor primers used for each locus (Additional file 7) were compiled in a single graph (each locus is represented by a specific color). Error bars are standard error of the mean of three independent quantitative 3C assays each quantified at least in triplicate. Dashed lines delimit supranucleosomal domains that encompass separation distances where contact frequencies are alternatively lower and higher (see Methods). The graphs show the best fit analyses obtained with the unconstrained chromatin model [eqs. 1 and 2] (black curves) or the statistical helix model [eqs. 1 and 3] (red curves). Correlation coefficients (R2) are indicated on the graphs. Best fit parameters, and the genomic distance contained within one statistical helix turn (Sh in kb), are given in the upper part of Table 1. For each supranucleosomal domains, the mean contact frequencies and the number (n) of experimental points are indicated on the graphs. p-values (Mann–Whitney U-test) account for the significance of the differences observed between the experimental means of two adjacent domains (double asterisks indicate a p-value < 0.05 and > 0.01 and triple asterisks a p-value < 0.01)

Mentions: Fitting globule models to contact frequencies quantified in mESC. Experimental 3C-qPCR data obtained for wt mESC in gene-rich TADs (Fig. 2) have been displayed into a Log-Log plot and globule models were fitted to the following power-law: X(s) = k*sα (adapted from Eq. 6 and Eq. 9 from ref. [20]), where X(s) is the cross-linking frequency, s (in kb) is the site separation along the genome, K is representing the efficiency of cross-linking and the exponent α is the slope associated to this power-law. Best-fits (using the nls object of the R software) show that the slope associated to our experimental data (red line) is approximately α = −1/2 (−0.52) with a correlation coefficient R2 = 0.47, while correlation coefficients associated to the equilibrium (α = −3/2) (black line) or crumpled globules (α = −1) (green line) are much lower


Distinct polymer physics principles govern chromatin dynamics in mouse and Drosophila topological domains.

Ea V, Sexton T, Gostan T, Herviou L, Baudement MO, Zhang Y, Berlivet S, Le Lay-Taha MN, Cathala G, Lesne A, Victor JM, Fan Y, Cavalli G, Forné T - BMC Genomics (2015)

Fitting the statistical helix model to contact frequencies quantified in mESC. Quantitative 3C data were obtained from wild-type mouse ESC in five gene-rich TADs (a), two gene-poor TADs (b) and one gene-desert TAD (c) (see genomic maps in Additional file 1). For each type of TAD, data obtained from all the anchor primers used for each locus (Additional file 7) were compiled in a single graph (each locus is represented by a specific color). Error bars are standard error of the mean of three independent quantitative 3C assays each quantified at least in triplicate. Dashed lines delimit supranucleosomal domains that encompass separation distances where contact frequencies are alternatively lower and higher (see Methods). The graphs show the best fit analyses obtained with the unconstrained chromatin model [eqs. 1 and 2] (black curves) or the statistical helix model [eqs. 1 and 3] (red curves). Correlation coefficients (R2) are indicated on the graphs. Best fit parameters, and the genomic distance contained within one statistical helix turn (Sh in kb), are given in the upper part of Table 1. For each supranucleosomal domains, the mean contact frequencies and the number (n) of experimental points are indicated on the graphs. p-values (Mann–Whitney U-test) account for the significance of the differences observed between the experimental means of two adjacent domains (double asterisks indicate a p-value < 0.05 and > 0.01 and triple asterisks a p-value < 0.01)
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Related In: Results  -  Collection

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Fig2: Fitting the statistical helix model to contact frequencies quantified in mESC. Quantitative 3C data were obtained from wild-type mouse ESC in five gene-rich TADs (a), two gene-poor TADs (b) and one gene-desert TAD (c) (see genomic maps in Additional file 1). For each type of TAD, data obtained from all the anchor primers used for each locus (Additional file 7) were compiled in a single graph (each locus is represented by a specific color). Error bars are standard error of the mean of three independent quantitative 3C assays each quantified at least in triplicate. Dashed lines delimit supranucleosomal domains that encompass separation distances where contact frequencies are alternatively lower and higher (see Methods). The graphs show the best fit analyses obtained with the unconstrained chromatin model [eqs. 1 and 2] (black curves) or the statistical helix model [eqs. 1 and 3] (red curves). Correlation coefficients (R2) are indicated on the graphs. Best fit parameters, and the genomic distance contained within one statistical helix turn (Sh in kb), are given in the upper part of Table 1. For each supranucleosomal domains, the mean contact frequencies and the number (n) of experimental points are indicated on the graphs. p-values (Mann–Whitney U-test) account for the significance of the differences observed between the experimental means of two adjacent domains (double asterisks indicate a p-value < 0.05 and > 0.01 and triple asterisks a p-value < 0.01)
Mentions: Fitting globule models to contact frequencies quantified in mESC. Experimental 3C-qPCR data obtained for wt mESC in gene-rich TADs (Fig. 2) have been displayed into a Log-Log plot and globule models were fitted to the following power-law: X(s) = k*sα (adapted from Eq. 6 and Eq. 9 from ref. [20]), where X(s) is the cross-linking frequency, s (in kb) is the site separation along the genome, K is representing the efficiency of cross-linking and the exponent α is the slope associated to this power-law. Best-fits (using the nls object of the R software) show that the slope associated to our experimental data (red line) is approximately α = −1/2 (−0.52) with a correlation coefficient R2 = 0.47, while correlation coefficients associated to the equilibrium (α = −3/2) (black line) or crumpled globules (α = −1) (green line) are much lower

Bottom Line: Using simple polymer models, we previously showed that, in mouse liver cells, gene-rich domains tend to adopt a statistical helix shape when no significant locus-specific interaction takes place.Interestingly, this statistical helix organization is considerably relaxed in mESC compared to liver cells, indicating that the impact of the constraints responsible for this organization is weaker in pluripotent cells.Finally, depletion of histone H1 in mESC alters local chromatin flexibility but not the statistical helix organization.

View Article: PubMed Central - PubMed

Affiliation: Institut de Génétique Moléculaire de Montpellier, UMR5535, CNRS, Université de Montpellier, 1919 Route de Mende, 34293, Montpellier, Cedex 5, France. vuthy.ea@igmm.cnrs.fr.

ABSTRACT

Background: In higher eukaryotes, the genome is partitioned into large "Topologically Associating Domains" (TADs) in which the chromatin displays favoured long-range contacts. While a crumpled/fractal globule organization has received experimental supports at higher-order levels, the organization principles that govern chromatin dynamics within these TADs remain unclear. Using simple polymer models, we previously showed that, in mouse liver cells, gene-rich domains tend to adopt a statistical helix shape when no significant locus-specific interaction takes place.

Results: Here, we use data from diverse 3C-derived methods to explore chromatin dynamics within mouse and Drosophila TADs. In mouse Embryonic Stem Cells (mESC), that possess large TADs (median size of 840 kb), we show that the statistical helix model, but not globule models, is relevant not only in gene-rich TADs, but also in gene-poor and gene-desert TADs. Interestingly, this statistical helix organization is considerably relaxed in mESC compared to liver cells, indicating that the impact of the constraints responsible for this organization is weaker in pluripotent cells. Finally, depletion of histone H1 in mESC alters local chromatin flexibility but not the statistical helix organization. In Drosophila, which possesses TADs of smaller sizes (median size of 70 kb), we show that, while chromatin compaction and flexibility are finely tuned according to the epigenetic landscape, chromatin dynamics within TADs is generally compatible with an unconstrained polymer configuration.

Conclusions: Models issued from polymer physics can accurately describe the organization principles governing chromatin dynamics in both mouse and Drosophila TADs. However, constraints applied on this dynamics within mammalian TADs have a peculiar impact resulting in a statistical helix organization.

No MeSH data available.


Related in: MedlinePlus