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

项目摘要/摘要 表观遗传记忆是在不改变DNA的情况下跨代改变特征遗传的现象 序列。我们目前预测或指导表观基因组行为的能力是极其有限的，即使 表观遗传因子几乎参与了多细胞发育、环境应激反应、 和疾病的发展。在我们目前关于DNA甲基化模式的知识中存在着严重的空白，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。我们已经开发了一个系统，将直接解决这些问题。我们的 长期的目标是破译可遗传的表观基因组，及其与生物表型的关系，特别是 以应对压力。 我们建议的研究的不同之处在于，在模式植物拟南芥中可以获得强大的生物制品 施加人为压力，反复的可遗传表观遗传记忆，以及甲基组重组。这些资源 源于MSH1基因的发现，该基因的中断导致了表观基因组的重新编程。最新数据 从这个系统引出了压倒一切的假设，即应激诱导的基因表达会引发甲基化 以非随机的方式对基因网络进行机械处理。为了解决这一假设，我们开发了小说 用于高分辨率鉴定基因相关甲基化的全基因组甲基组分析程序 改头换面。这些分析揭示了与表型变化惊人一致的基因网络，以及 显示基因内的重塑，通常很微妙，但可重现。我们还发现了表观遗传学 基于msh1的重新编程所必需的DNA甲基化和RdDM途径的组成部分 双重突变分析。基于强劲的初步数据，我们建议实现三个具体目标： 描述跨世代表观基因组行为：(1)描述稳定的、从头开始的表观等位基因 拟南芥msh1模型系统，利用五代记忆谱系，(2)开发一种机械 理解应激反应中稳定的表位基因形成，实现机器学习和突变 筛选，以及(3)测试表观等位基因建立的特定位点机制，利用基因重新定位来 界定与之密切相关的局部染色质特征。 拟议中的研究将通过提供第一个可诱导表观基因组学的例子来广泛影响该领域 以允许基于机器学习的预测建模的非随机模式进行重新编程 顺式作用序列特征的鉴定。结果将与哺乳动物系统相关，并可能， 到具有较强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.