Learning the Formation Mechanism of Domain-Level Chromatin States with Epigenomics Data
Learning the Formation Mechanism of Domain-Level Chromatin States with Epigenomics Data
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DOI：
10.1016/j.bpj.2019.04.006
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发表时间：
2019-05-21
影响因子：
3.4
通讯作者：
Zhang，Bin
中科院分区：
文献类型：
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作者：
Xie，Wen Jun;Zhang，Bin
表观遗传修饰可以延伸到较长的基因组区域，形成结构域水平的染色质状态，在基因调控中发挥关键作用。这些状态建立和维持的分子机制尚不完全清楚，用现有的实验技术进行研究仍然具有挑战性。在这里，我们采用数据驱动的方法和参数化的信息理论模型，从全基因组表观遗传修饰谱中推断结构域水平染色质状态的形成机制。该模型再现了组蛋白修饰之间的统计相关性，并确定了众所周知的状态。重要的是，它预测了正染色质和异染色质形成的截然不同的机制和动力学途径。特别是，长而强的增强子和启动子状态是由短而稳定的调控元件经过多步过程逐渐形成的。另一方面，异染色质态的形成是高度协同的，在过渡路径上没有发现中间态。这种协同性可能来自染色质环介导的组蛋白甲基化标记的扩散，并支持塌缩的球形三维构象，而不是异染色质的规则纤维结构。我们利用细胞分化过程中表观遗传谱的变化进一步验证了这些预测。我们的研究表明，信息理论模型可以超越统计分析，获得有洞察力的动态信息，否则很难获得。
Epigenetic modifications can extend over long genomic regions to form domain-level chromatin states that play critical roles in gene regulation. The molecular mechanism for the establishment and maintenance of these states is not fully understood and remains challenging to study with existing experimental techniques. Here, we took a data-driven approach and parameterized an information-theoretic model to infer the formation mechanism of domain-level chromatin states from genome-wide epigenetic modification profiles. This model reproduces statistical correlations among histone modifications and identifies well-known states. Importantly, it predicts drastically different mechanisms and kinetic pathways for the formation of euchromatin and heterochromatin. In particular, long, strong enhancer and promoter states grow gradually from short but stable regulatory elements via a multistep process. On the other hand, the formation of heterochromatin states is highly cooperative, and no intermediate states are found along the transition path. This cooperativity can arise from a chromatin looping-mediated spreading of histone methylation mark and supports collapsed, globular three-dimensional conformations rather than regular fibril structures for heterochromatin. We further validated these predictions using changes of epigenetic profiles along cell differentiation. Our study demonstrates that information-theoretic models can go beyond statistical analysis to derive insightful kinetic information that is otherwise difficult to access.