RESEARCH-PGR: Predicting Gene-Specific Functional Contributions to Maize Reproduction: A Machine-Learning Approach

从生物学的角度来看，开花植物的成功与有性繁殖过程密切相关，有性繁殖过程由花及其产品、雄性花粉和嵌入在花中的雌性胚囊促进。在农业中，粮食和水果等初级产品依赖于有性繁殖的成功。特别是，花粉将精子细胞传输到胚囊的能力对下一代种子的产生至关重要。玉米是该项目的重点，高温等环境应激源会导致花粉活力丧失和随后的作物中断。该项目将利用一种新的成像和自动计算机视觉系统(EarVision)，结合一大组易于检测的突变体来测量数百个基因对玉米花粉功能的贡献。定量和计算方法将分析现有的基因组规模数据，以帮助预测玉米繁殖过程中的基因功能，并建立对这种作物的花粉遗传学的更好理解。该项目将为旨在实现多种农业目标的方法提供信息，例如提高作物复原力或控制生殖系活动。此外，该项目将通过夏令营和包容性研究项目向高中、本科生和博士后水平的学生提供植物遗传学和定量科学技能方面的教育。该项目针对雄配子体的两种细胞类型，营养细胞和精子细胞，使用突变询问作为基因功能的测试，进行数据驱动的系统水平的基因研究，以了解其生物学功能。利用花粉的单倍体性质和一大组被称为ds-GFP系(acdsintertions.org)的荧光标记插入突变，该项目将使用一种创新的自动化表型方法来生成数百个基因对花粉适合性的特定基因贡献的测量结果。机器学习和统计学方法将被用来分析所产生的量化数据集，开发综合模型来将基因组、转录组和蛋白质组数据等组学规模的数据类型与特定基因的功能丧失表型结果联系起来。最优模型将用于从基因型预测花粉表型，并随后通过间接和直接测试进行验证。该项目还将开发适当的统计方法来利用通过表型系统产生的高含量图像，以解决有关花粉功能改变如何影响玉米穗花序后代群体模式的假设。该项目汇集了俄勒冈州立大学的三名教职员工，他们拥有互补的专业知识，以支持其跨学科方法：植物遗传学和发育、统计学和系统分析以及机器学习和计算生物学。该项目将与俄勒冈州立STEM领袖项目合作，培训计算和定量方法在生物学问题上的跨学科应用方面的博士后，以及遗传学或定量方法方面的本科生研究人员。该奖项反映了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。

From a biological perspective, the success of flowering plants is intimately tied to the process of sexual reproduction, facilitated by flowers and their products, the male pollen and the female embryo sac embedded within the flower. In agriculture, primary products such as grain and fruit rely on the success of sexual reproduction. In particular, the ability of pollen to transmit sperm cells to the embryo sac is critical to the production of the next generation of seeds. In maize, the focus of this project, environmental stressors such as high heat can lead to loss of pollen viability and subsequent crop disruption. This project will utilize a novel imaging and automated computer vision system (EarVision) coupled with a large set of easily assayed mutants to measure the contributions of hundreds of genes to maize pollen function. Quantitative and computational approaches will analyze available genome-scale data to help predict gene function during maize reproduction and build an improved understanding of the pollen genetics of this crop plant. The project will inform approaches directed towards multiple agricultural goals such as improving crop resilience or controlling germ line activity. In addition, the project will educate students at high school, undergraduate, and postdoctoral levels in plant genetics and quantitative scientific skills via outreach through summer camps and inclusive research projects.This project targets the two cell types of the male gametophyte, vegetative and sperm cells, for a data-driven, systems-level investigation of genes underlying their biological functions using mutational interrogation as a test of gene function. Taking advantage of the haploid nature of pollen and a large set of fluorescently-marked insertional mutations known as Ds-GFP lines (acdsinsertions.org), the project will employ an innovative automated phenotyping approach to generate measures of gene-specific contributions to pollen fitness for several hundred genes. Machine learning and statistical approaches will be used to analyze the resulting quantitative dataset, developing integrative models to relate omics-scale datatypes, such as genome, transcriptome, and proteome data, to the loss-of-function phenotypic outcome for specific genes. The optimal model will be used to predict pollen phenotype from genotype, and subsequently be validated via indirect and direct tests. The project will also develop appropriate statistical methods to exploit the high-content images generated via the phenotyping system, to address hypotheses relating how altered pollen function can influence patterns in the progeny population on the maize ear inflorescence. The project brings together three Oregon State University faculty members with complementary expertise to support its transdisciplinary approach: in plant genetics and development, in statistics and systems analysis, and in machine learning and computational biology. The project will train a postdoc in transdisciplinary application of computational and quantitative approaches to a biological question as well as undergraduate researchers in either genetic or quantitative approaches in biology in collaboration with the Oregon State STEM Leaders Program.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.