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。尽管植物生命的这一基本属性，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开发可以在更少的水或能够承受极端气候条件下茁壮成长的作物的能力。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况的机制来解决这一知识缺口。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、适应压力的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生长对环境和土壤水分条件的响应，以便可以通过计算机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，以帮助确定和量化负责赋予干旱适应的遗传机制。获得的信息可被更广泛的社区用于作物改良和水资源管理-这是农业受到水供应减少威胁的一个关键方面。这些工具和技术也将被纳入本科生的研究机会，以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所带来的作物不安全。控制和调节作物中水分运动的遗传机制知之甚少，限制了在水分有限条件下提高作物弹性的能力。这种知识差距的存在是因为用于表征基因型特异性抗旱性状的方法是时间和劳动密集型的，并且不能扩展到遗传信息量大的群体。此外，目前的方法来量化基因型变异和改变生理性能的时间基础上响应于波动的环境条件之间的功能连接是严重缺乏的。弥合这一差距的一种方法是使用基于生物物理过程的模型（BPM），该模型基于基本的物理，化学和生物学理论模拟复杂系统。本项目旨在解决缺乏知识的遗传机制控制植物水分动态：1）确定时间动态的分子和表型反应的转录和生物物理机制的特点，在干旱条件下生长的植物; 2）利用这些数据来开发一个BPM能够模拟基因型特异性参数，捕获驯化响应水分限制; 3）利用代表棉花水分平衡调节生理过程的模型表型揭示棉花抗旱性的遗传基础。总之，我们的建议旨在缩小抗旱基因型与表型之间的差距，以提高作物的弹性，同时为剖析作物的胁迫适应性状提供一个范例。该奖项反映了NSF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。