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

无论环境条件如何，植物都需要适当的水合状态。尽管植物生命的这些基本特性，但负责调节植物水分状况的遗传和分子过程还没有完全被理解。这种知识的缺乏限制了开发能够在较少的水中茁壮成长或能够承受极端气候的作物的能力。这个项目试图通过将生理和分子特性数据与计算方法相结合来解决这一知识鸿沟，以揭示负责控制植物水分状况的机制(S)。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有关，这些性状负责确保在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反应，以便可以在计算机上测试各种植物特性的组合，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，帮助确定和量化导致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--这是一个关键方面，因为农业受到水资源减少的威胁。这些工具和技术还将纳入本科研究机会，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造成的作物不安全问题。控制和调节作物水分运动的遗传机制知之甚少，限制了在水分有限的条件下提高作物抗逆性的能力。之所以存在这种知识鸿沟，是因为表征特定于基因的抗旱性状的方法耗费了大量的时间和劳动，而且不适用于遗传信息丰富的种群。此外，目前严重缺乏量化基因变异和生理表现变化之间的功能联系的方法，以响应波动的环境条件。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)，该模型基于基本的物理、化学和生物理论来模拟复杂的系统。该项目旨在解决有关控制植物水分动态的遗传机制方面的知识不足：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特定类型的参数的BPM，这些参数捕捉对水分限制的适应反应；以及3)通过使用模型衍生的表型来揭示棉花抗旱的遗传基础，这些表型代表了棉花调节水分平衡的生理过程。总而言之，我们的建议旨在缩小抗旱性从基因型到表型的差距，以提高作物的抗旱性，同时为剖析作物的逆境适应性状提供一个范例。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.