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 序列。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 表观遗传因素参与多细胞发育、环境应激反应、 和疾病的发展。我们目前对 DNA 甲基化模式的了解存在严重差距，稳定 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。我们开发了一个可以直接解决这些问题的系统。我们的 长期目标是解码可遗传的表观基因组及其与生物体表型的关系，特别是 以应对压力。 我们提出的研究的独特之处在于模型植物拟南芥中有效的生物制品 施加人为压力、反复遗传的表观遗传记忆和甲基化组重新模式。这些资源 源于 MSH1 基因的发现，该基因的破坏会导致表观基因组重编程。近期数据 从这个系统得出一个总体假设：应激诱导的基因表达会招募甲基化 以非随机方式将机器转化为基因网络。为了解决这个假设，我们开发了新颖的 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 重新图案化。这些分析揭示了与表型变化惊人一致的基因网络，并且 显示基因内的、通常微妙但可重复的重新模式。我们还发现了表观遗传 DNA 甲基化和 RdDM 途径的组成部分，对于基于 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）描绘稳定的、从头的表观等位基因 拟南芥 msh1 模型系统，利用五代记忆谱系，(2) 开发机械 了解应对压力的稳定表观等位基因形成，实施机器学习和突变 筛选，以及（3）测试表观等位基因建立的位点特异性机制，利用基因重定位 界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和 顺式作用序列特征的鉴定。结果将与哺乳动物系统相关，并且可能， 具有强大 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.