RII Track-4: High-Throughput Phenotyping and Analysis for the Study of Plant Robustness

从个体生物的DNA序列预测其特征和行为被证明是一个非常具有挑战性和复杂的问题。一个复杂的因素是DNA和性状之间的联系是不一致的，即使在相同的环境下，相同的DNA序列也可能不会产生相同的性状。这是因为某些基因会影响一个性状的稳定性或可变性（健壮性），而有些基因产生的性状或多或少是可变的。因此，需要确定影响鲁棒性的基因，以建立从DNA序列预测性状的模型。这些知识对于加快作物育种、满足全球日益增长的粮食需求以及为美国生产者提供经济保障至关重要。目前发现控制植物鲁棒性的基因的瓶颈是测量植物性状的低效过程。为了解决这个问题，密苏里州圣路易斯市的唐纳德·丹福斯植物科学中心建造了最先进的设备，并开发了计算机程序来自动测量数千种植物的特性。通过广泛的合作访问，PI和实习学生将使用这些设施，学习处理这些数据的方法，以及如何建立这些自动化系统的低成本版本，目的是将这些基础设施带到蒙大拿州。这种支持将使识别DNA序列，影响鲁棒性和鲁棒性之间的关系的特征。从基因型预测表型被证明是具有挑战性和复杂的。一个复杂的因素是基因型和表型之间的关系是有问题的。虽然生物体的整个基因组，而不是单个等位基因，决定表型和表型变异性的程度，但对如何在分子和细胞水平上控制稳健性的理解仍然很差。这种知识差距的存在是因为要测量任何给定特征的稳健性，必须测量具有相同基因组成的大量个体。因此，该奖学金的目的是在唐纳德丹福斯植物科学中心对拟南芥进行高通量表型分析。此外，该研究金还支持PI和一名培训生在高通量数据收集和分析方面的全面培训。获得的数据和技能将使检验中心假设成为可能：基因以可预测的方式影响性状的稳健性。具体来说，这些数据将支持鉴定1)调节稳健性（表型变异）的基因和2)整个植物发育过程中的稳健性模式。我们期望通过模型物种的大量数据，我们将确定描述跨植物物种（包括作物）比较的稳健性的模式。这些数据将促进作物育种，通过其对粮食和经济安全的影响，对NSF的国家安全使命至关重要。此外，该奖学金将通过支持低成本自动化表型平台的开发，使蒙大拿州立大学的研究基础设施受益，该平台可用于蒙大拿州种植的作物。此外，植物自动表型分析的实际演示将使农村女孩接触计算机科学/工程，这是传统上女性代表性不足的领域。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。

Predicting the characteristics, or traits, and behaviors of an individual organism from its DNA sequence is proving to be a very challenging and complex problem. One complicating factor is that the connection between DNA and traits is not consistent, and identical DNA sequences may not produce identical traits even when the environment is the same. This occurs because certain genes influence how stable or variable (robust) a trait is, with some genes producing traits that are more or less variable. Therefore, the identity of the genes that influence robustness is needed to build models to predict traits from DNA sequences. This knowledge is crucial to accelerate crop breeding, to meet growing global demands for food, and provide economic security to American producers. The current bottleneck in discovering genes that control robustness in plants is the inefficient process of measuring plant traits. To address this problem, the Donald Danforth Plant Science Center in St. Louis, Missouri has built state-of-the-art facilities and developed computer programs to automatically measure the characteristics of thousands of plants. Through extensive collaborative visits, the PI and a student trainee will use these facilities and learn methods for processing this data and how to build low-cost versions of these automated systems with the intent to bring this infrastructure to Montana. This support will enable the identification of DNA sequences that influence robustness and the relationships in robustness among characteristics.Predicting phenotypes from genotypes is proving to be challenging and complex. One complicating factor is that the relationship between genotype and phenotype is problematic. While the entire genome of an organism, rather than single alleles, determines the phenotype and degree of phenotypic variability, a poor understanding of how robustness is controlled at the molecular and cellular levels remains. This knowledge gap exists because to measure robustness for any given trait, a large number of individuals with the same genetic make-up must be measured. Therefore, the objective of this fellowship is high-throughput phenotyping at the Donald Danforth Plant Science Center for the plant Arabidopsis thaliana. In addition, this fellowship supports the comprehensive training of the PI and a trainee in high-throughput data collection and analyses. The data and skills gained will enable testing of the central hypothesis: genetics influences the robustness of traits in predictable ways. Specifically, the data will support identifying 1) genes modulating robustness (phenotypic variability) and 2) patterns of robustness throughout plant development. We expect that with extensive data from a model species, we will identify patterns that describe robustness for comparison across plant species, that include crops. These data will facilitate crop breeding, crucial to the NSF's mission of national security through its impacts on food and economic security. Furthermore, this fellowship will benefit Montana State University's research infrastructure through supporting the development of a low-cost automated phenotyping platform that can be utilized across crops bred in Montana. Further, hands-on demonstrations of automated phenotyping for plants will expose rural girls to computer science/engineering, fields in which women are traditionally underrepresented.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.