RESEARCH-PGR: Exploring the genetics of drought resistance with field-based phenomics and biophysical process-based modeling

RESEARCH-PGR：通过基于田间的表型组学和基于生物物理过程的建模探索抗旱遗传学

• 批准号: 2102120
• 负责人: Duke Pauli
• 金额: $ 267.02万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2027-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2102120&HistoricalAwards=false
• 关键词: RESEARCH; PGR; Exploring; genetics; drought

Plants require proper hydration status regardless of environmental conditions. Despite this fundamental property of plant life, the genetic and molecular processes responsible for regulating plant water status are not fully understood. This lack of knowledge limits the ability to develop crops that can thrive with less water or are able to withstand climatic extremes. This project seeks to address this knowledge gap by combining physiological and molecular trait data with computational approaches to reveal the mechanism(s) responsible for controlling plant water status. Cotton, the world’s most important fiber crop, will be used to study how gene expression relates to quantifiable changes in key, stress-adaptive plant traits responsible for ensuring growth and productivity under drought conditions. Using these data, a computational model will be developed to simulate cotton growth in response to environmental and soil water conditions so that various combinations of plant traits can be tested in silico to predict how plants may perform under a variety of conditions. This project will provide data and tools to help identify and quantify the genetic mechanisms responsible for conferring drought adaptation. Information gained can be used by the broader community for crop improvement and managing water resources – a critical aspect as agriculture is threatened by reduced water availability. The tools and techniques will also be integrated into undergraduate research opportunities to help provide training and education for the next generation of scientists to combat crop insecurity due to existential threats posed by climate change. The genetic mechanisms that control and regulate water movement in crop plants is poorly understood, limiting the ability to enhance crop resiliency under water limited conditions. This knowledge gap exists because the methods for characterizing genotype-specific drought resistance traits are time and labor intensive and do not scale to genetically informative populations. Additionally, current methods to quantify the functional connections between genotypic variation and altered physiological performance on a temporal basis in response to fluctuating environmental conditions are severely lacking. One approach to bridge this gap is the use of biophysical process-based models (BPMs), which simulate complex systems based on fundamental physical, chemical, and biological theory. This project aims to address the lack of knowledge surrounding the genetic mechanisms controlling plant water dynamics by: 1) determining the temporal dynamics of molecular and phenotypic responses characterized by transcriptional and biophysical mechanisms in plants grown under drought conditions; 2) utilizing these data to develop a BPM capable of simulating genotype-specific parameters that capture acclimation response to water limitation; and 3) uncovering the genetic basis of drought resistance in cotton by using model-derived phenotypes that represent the physiological processes regulating water balance in cotton. In sum, our proposal seeks to close the genotype-to-phenotype gap for drought resistance to improve crop resiliency while providing a paradigm for dissecting stress-adaptive traits in crop plants.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.

无论环境条件如何，植物都需要适当的水合状态。尽管植物生命具有这一基本特性，但负责调节植物水分状况的遗传和分子过程尚未完全了解。这种知识的缺乏限制了培育能够在更少的水下茁壮成长或能够承受极端气候的作物的能力。该项目旨在通过将生理和分子性状数据与计算方法相结合来揭示负责控制植物水分状态的机制来解决这一知识差距。棉花是世界上最重要的纤维作物，将用于研究基因表达如何与关键的、应激适应性植物性状的可量化变化相关，这些性状负责确保干旱条件下的生长和生产力。利用这些数据，将开发一个计算模型来模拟棉花响应环境和土壤水条件的生长，以便可以在计算机中测试植物性状的各种组合，以预测植物在各种条件下的表现。该项目将提供数据和工具，帮助识别和量化负责干旱适应的遗传机制。获得的信息可以被更广泛的社区用于作物改良和水资源管理——这是农业受到可用水量减少威胁的一个关键方面。这些工具和技术还将整合到本科生研究机会中，帮助为下一代科学家提供培训和教育，以应对气候变化造成的生存威胁造成的作物不安全。人们对控制和调节作物水分运动的遗传机制知之甚少，这限制了在缺水条件下增强作物恢复能力的能力。这种知识差距的存在是因为表征基因型特异性抗旱性状的方法是时间和劳动力密集型的，并且不能扩展到具有遗传信息的群体。此外，目前严重缺乏量化基因型变异与响应波动环境条件而改变的生理性能之间功能联系的方法。弥补这一差距的一种方法是使用基于生物物理过程的模型 (BPM)，该模型基于基本物理、化学和生物理论来模拟复杂系统。该项目旨在通过以下方式解决控制植物水动态的遗传机制知识的缺乏：1）确定干旱条件下生长的植物中以转录和生物物理机制为特征的分子和表型反应的时间动态； 2) 利用这些数据开发能够模拟基因型特异性参数的 BPM，捕获对水限制的适应反应； 3）通过使用代表棉花调节水平衡的生理过程的模型衍生表型，揭示棉花抗旱性的遗传基础。总之，我们的提案旨在缩小抗旱性的基因型与表型差距，以提高作物的抗逆能力，同时提供剖析作物逆境适应性状的范例。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。