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

将拟南芥模型中应激诱导的从头表观等位基因结构建模为易于处理的入口点

基本信息

批准号：
10454432
负责人：
SALLY A MACKENZIE
金额：
$ 31.97万
依托单位：
PENNSYLVANIA STATE UNIVERSITY, THE
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10454432
关键词：
Address Affect Animals Arabidopsis Architecture Behavior Biological Assay Biological Models Cellular Stress Chromatin Chromatin Modeling Cis-Acting Sequence Complement Cytosine DNA DNA Methylation DNA Sequence Data Data Analyses Data Set Detection Development Diagnostic Discrimination Disease Environmental Impact Epigenetic Process Gene Expression Gene Targeting Generations Genes Genetic Genetic Transcription Genomics Genotype Goals Health Heritability Homeostasis Information Systems Knowledge Lead Life Style Link Liquid substance Machine Learning Malignant Neoplasms Mechanics Medicine Memory Metabolic Methodology Methods Methylation Modeling Nucleosomes Organism Pathway interactions Pattern Pattern Recognition Phenotype Plant Model Plants Positioning Attribute Procedures Process Psychological reinforcement RNA Splicing Recurrence Reporting Reproducibility Reproduction Research Resolution Resources Signal Transduction Site Small RNA Statistical Data Interpretation Stress System Testing Thermodynamics Tissue-Specific Gene Expression Toxin Transgenes Variant base behavior influence biological adaptation to stress biophysical properties bisulfite density diagnostic strategy environmental stressor epigenetic memory epigenome epigenomics experimental study flexibility gene network genome-wide histone modification in utero innovation insight machine learning prediction methylation pattern methylome mutant novel programs public health priorities recruit response screening trait transgenerational epigenetic inheritance

项目摘要

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.

项目摘要/摘要表观遗传记忆是跨代，改变性状遗传的现象，而没有变化DNA 顺序。我们目前预测或直接表观基因组行为的能力极为有限，尽管表观遗传因素几乎参与多细胞发育的各个方面，环境压力反应，和疾病发展。我们目前对DNA甲基化模式的知识存在关键的差距，稳定 Epiallele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动植物系统中。我们已经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表型的关系，尤其是响应压力。我们提出的研究的区别是模型植物拟南芥中可靠的生物学施加人造压力，反复出现的可遗传遗传记忆和甲基化体重叠。这些资源从发现MSH1基因的发现中散发出来，这会导致表观基因组重编程。最新数据从该系统导致了总体假设，即应激诱导的基因表达募集甲基化以非传统方式到基因网络的机械。为了解决这一假设，我们开发了小说全基因组甲基分析程序，用于高分辨率鉴定基因相关甲基化重新制作。这些分析揭示了与表型变化非常一致的基因网络，并且显示重新制作的重新制作，通常是微妙的，但可重复的。我们还确定了表观遗传 DNA甲基化和RDDM途径的成分对于基于MSH1重新编程至关重要双突变体分析。在强大的初步数据的基础上，我们建议追求三个特定目标跨代表观基因组行为表征：（1）描绘稳定的稳定，从头开始拟南芥MSH1模型系统，利用五代记忆谱系，（2）开发机械理解稳定的Epiallele形成，以应对压力，实施机器学习和突变体筛选，（3）测试Epiallele建立的基因座特异性力学，利用基因迁移到界限局部局部染色质特征。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策略模式重新编程，该模式允许基于机器学习的预测建模和识别顺式作用序列特征。结果将与哺乳动物系统以及可能有关的结果为具有强大GXE成分的疾病的诊断策略。