Modeling stress-induced, de novo epiallele architecture in the model Arabidopsis as a tractable entrypoint

项目概要/摘要 表观遗传记忆是一种跨代、改变性状遗传而不改变DNA的现象 顺序我们目前预测或指导表观基因组行为的能力极其有限，尽管 表观遗传因子参与多细胞发育，环境应激反应， 和疾病的发展。我们目前对DNA甲基化模式的认识存在严重差距， 表观等位基因的形成，以及全基因组表观基因组行为与基因表达和表型的关系 在植物和动物系统中。我们已经开发了一个系统，将直接解决这些问题。我们 长期目标是解码可遗传的表观基因组，以及它与生物表型的关系，特别是 来应对压力。 我们所提出的研究的区别是在模式植物拟南芥中获得了强大的生物制剂 施加人为压力，经常性遗传表观遗传记忆，和甲基化组重新图案化。这些资源 源自MSH1基因的发现，其破坏导致表观基因组重编程。最近的数据 从这一系统导致了一个总体假设，即压力诱导的基因表达招募甲基化 以非随机的方式将机器转化为基因网络。为了解决这一假设，我们开发了一种新的 用于高分辨率鉴定基因相关甲基化的全基因组甲基化分析程序 重塑这些分析揭示了与表型变化惊人一致的基因网络， 表现出基因内的，通常是微妙的，但可重复的重新模式化。我们还发现了表观遗传 DNA甲基化和RdDM途径的组成部分，这对基于msh1的重编程至关重要 双突变体分析基于强有力的初步数据，我们建议实现三个具体目标， 表征跨代表观基因组行为：（1）为了描绘稳定的，从头表观等位基因在 拟南芥msh1模型系统，利用一个五代记忆谱系，（2）开发一个机制， 理解稳定的表观等位基因形成对压力的反应，实施机器学习和突变 筛选，和（3）测试表观等位基因建立的基因座特异性机制，利用基因重定位， 划定密切相关的局部染色质特征。 拟议的研究将通过提供诱导表观基因组的第一个例子来广泛影响该领域 以非随机模式重新编程，允许基于机器学习的预测建模， 鉴定顺式作用序列特征。这些结果将与哺乳动物系统有关， 用于具有强GxE成分的疾病的诊断策略。

PROJECT SUMMARY/ABSTRACT Epigenetic memory is a phenomenon of trans-generational, altered trait inheritance without changes to DNA sequence. Our present ability to predict or direct epigenomic behavior is extremely limited, even though epigenetic factors participate in nearly all aspects of multicellular development, environmental stress response, and disease development. There are critical gaps in our current knowledge of DNA methylation patterning, stable epiallele formation, and the relationship of genome-wide epigenomic behavior to gene expression and phenotype in both plant and animal systems. We have developed a system that will directly address these questions. Our long-term goals are to decode the heritable epigenome, and its relationship to organismal phenotype, particularly in response to stress. What distinguishes our proposed research is the availability of robust biologicals in the model plant Arabidopsis to impose artificial stress, recurrent heritable epigenetic memory, and methylome repatterning. These resources emanate from discovery of the MSH1 gene, disruption of which leads to epigenomic reprogramming. Recent data from this system lead to the overarching hypothesis that stress-induced gene expression recruits methylation machinery to gene networks in a non-stochastic manner. To address this hypothesis, we have developed novel genome-wide methylome analysis procedures for high-resolution identification of gene-associated methylation repatterning. These analyses reveal gene networks that are strikingly consistent with phenotype changes, and display repatterning that is intragenic and often subtle, yet reproducible. We have also identified epigenetic components of the DNA methylation and RdDM pathways that are essential to reprogramming based on msh1 double mutant analysis. Building upon strong preliminary data, we propose to pursue three specific aims to characterize trans-generational epigenomic behavior: (1) To delineate stable, de novo epialleles in the Arabidopsis msh1 model system, exploiting a five-generation memory lineage, (2) to develop a mechanistic understanding of stable epiallele formation in response to stress, implementing machine learning and mutant screening, and (3) to test locus-specific mechanics of epiallele establishment, capitalizing on gene relocation to delimit germane local chromatin features. The proposed research will broadly impact the field by providing the first example of inducible epigenomic reprogramming in a non-stochastic pattern that permits machine learning-based predictive modeling and identification of cis-acting sequence features. The results will be pertinent to mammalian systems and, possibly, to diagnostic strategies for diseases with a strong GxE component.