URoL: Epigenetics 2- Collaborative Research: Revealing how epigenetic inheritance governs the environmental challenge response with transformative 3D genomics and machine learning

该项目旨在定义植物如何应对环境挑战。了解植物对环境胁迫的反应对于确保我们未来在保持农业生产力和保护环境方面的国家利益是重要的。该项目将促进对二氧化碳升高如何在DNA组织的分子尺度以及植物生长尺度上改变植物形态的理解。这个提议询问这些植物的反应是否会持续几代，如果是的话，测试遗传机制。此外，该项目促进培训和发展多样化的未来科学劳动力。这项研究的成果将成为一个预测模型，用于预测环境挑战对整个植物界生长模式的持久影响。目前尚不清楚环境挑战如何广泛地改变植物形态，以及由此产生的变化是否通过表观遗传传给后代。该提案建立了一个具有基因组学，表型组学，数据可视化，图像分析，机器学习，建模和三维（3D）形状分析专业知识的团队，以弥合环境/基因型/表型差距的共同目标。这个提议的变革之处在于数据分析的新颖性。该团队在3D空间植物形态的数学和计算分析方面拥有尖端的专业知识。然而，对基因组调控的分析十年来一直没有改变，停留在二维上。研究人员建议利用他们在3D形状方面的专业知识将标准基因组分析提升到3D，从而为基于基因组调节和植物形态之间关系的新发现打开大门。通过利用不同的植物物种、特征良好的突变体和机器学习，研究人员将提供一种计算算法来预测和模拟整个植物界的环境挑战如何调节植物形态，以及这些方面中的哪些方面是表观遗传遗传给下一代的。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project seeks to define how plants respond to environmental challenge. Understanding plant responses to environmental stress is important to secure our future national interest in maintaining agricultural productivity and preserving the environment. This project will advance the understanding of how elevated carbon dioxide alters plant form on both the molecular scale of DNA organization, as well on the scale of plant growth. This proposal asks whether these plant responses will last generations, and if so, tests the mechanism of inheritance. Additionally, this project promotes the training and development of a diverse future science-enabled workforce. The product of this research will be a predictive model for the lasting consequences of environmental challenge on growth patterns across the plant kingdom.It is unclear how environmental challenge broadly alters plant form, and whether the resulting changes are inherited epigenetically to the subsequent generations. This proposal establishes a team with expertise in genomics, phenomics, data visualization, image analysis, machine learning, modeling, and analysis of three dimensional (3D) shapes for the common goal to bridge the environment / genotype / phenotype gap. The transformative aspect of this proposal is the novelty of the data analysis. The team has cutting-edge expertise in the mathematical and computational analysis of plant form in 3D space. However, the analysis of genome regulation hasn't changed for a decade and is stuck in 2D. The researchers propose to use their expertise on 3D shapes to elevate the standard genomic analysis to 3D, therefore opening doors to new discoveries based on the relation between genome regulation and plant form. By utilizing diverse plant species, well-characterized mutants, and machine learning, the researchers will deliver a computational algorithm to predict and model across the plant kingdom how environmental challenge regulates plant form, and which of these aspects are epigenetically inherited to the next generation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.