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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.


Fitting the statistical helix model to contact frequencies quantified in mouse H1 TKO ESC. Quantitative 3C data were obtained from mouse ESC that are Triple Knock-Out for Histone H1 genes (H1 TKO), for five gene-rich TADs (a), two gene-poor TADs (b) and one gene-desert TAD (c). 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). The data (see Additional file 8) were analyzed and are depicted as described in the legend of Fig. 2. Best-fit parameters, and the genomic distance contained within one statistical helix turn (Sh in kb), are given in the lower part of Table 1
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Fig3: Fitting the statistical helix model to contact frequencies quantified in mouse H1 TKO ESC. Quantitative 3C data were obtained from mouse ESC that are Triple Knock-Out for Histone H1 genes (H1 TKO), for five gene-rich TADs (a), two gene-poor TADs (b) and one gene-desert TAD (c). 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). The data (see Additional file 8) were analyzed and are depicted as described in the legend of Fig. 2. Best-fit parameters, and the genomic distance contained within one statistical helix turn (Sh in kb), are given in the lower part of Table 1

Mentions: Fitting the statistical helix model to the relative contact frequencies observed in wild-type (upper part, rows 2–4) and triple KO (lower part, rows 5–7) mouse ES cells (mESCs)


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 mouse H1 TKO ESC. Quantitative 3C data were obtained from mouse ESC that are Triple Knock-Out for Histone H1 genes (H1 TKO), for five gene-rich TADs (a), two gene-poor TADs (b) and one gene-desert TAD (c). 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). The data (see Additional file 8) were analyzed and are depicted as described in the legend of Fig. 2. Best-fit parameters, and the genomic distance contained within one statistical helix turn (Sh in kb), are given in the lower part of Table 1
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4536789&req=5

Fig3: Fitting the statistical helix model to contact frequencies quantified in mouse H1 TKO ESC. Quantitative 3C data were obtained from mouse ESC that are Triple Knock-Out for Histone H1 genes (H1 TKO), for five gene-rich TADs (a), two gene-poor TADs (b) and one gene-desert TAD (c). 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). The data (see Additional file 8) were analyzed and are depicted as described in the legend of Fig. 2. Best-fit parameters, and the genomic distance contained within one statistical helix turn (Sh in kb), are given in the lower part of Table 1
Mentions: Fitting the statistical helix model to the relative contact frequencies observed in wild-type (upper part, rows 2–4) and triple KO (lower part, rows 5–7) mouse ES cells (mESCs)

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.