Collaborative Research: RESEARCH-PGR: Uncovering latent vascular function in maize

由于其作为食物、动物饲料、燃料和其他生物制品的来源的效用，玉米（Zea）可能会被称为亚sp。在美国，玉米的年产量超过830亿美元。干旱和洪水等极端天气事件日益频繁，威胁着依赖玉米生产的美国企业和社区。来自俄勒冈州立大学（PI Leiboff）和加州大学伯克利分校（Co-PI Chuck）的研究人员正在共同努力，了解调节玉米维管系统的机制，以使我们国家最重要的作物为气候变化做好准备。在最近的一项突破中，Co-PI Chuck展示了在玉米植物内部看似相同的复杂静脉网络中，有一些特殊的静脉在干旱中生存和适应植物微生物方面具有独特的功能。通过应用单细胞基因组学、突变图谱和机器学习的尖端技术，PI Leiboff和Co-PI Chuck将揭开定义这些特殊静脉的秘密遗传学，并通过精确育种为快速改进玉米维管系统提供工具。pi将使用这项研究开发的技术，为农村和城市的高中教师提供低成本的智能手机显微镜套件，这将提高高中生对生命科学的参与度。这项研究的数据将用于制作一个免费的教育资源，“用单细胞基因组学教学”，为大学教育者提供课程计划、动画、多媒体演示和补充教科书，以确保我们下一代的生物学本科生接受这项革命性的新技术的培训。密集脉网的动态生成是C4禾本科植物高效光合作用的关键。当组织生长或遇到新环境时，草类通过反复的发育程序来维持生理功能，从而产生这个网络。虽然这些程序的产物在结构上是相似的，但这些重复的事件为不同时间和地点产生的组织之间的专业化提供了独特的机会。这项研究探讨了生物体内相似组织如何为不同目的而构建的生物学问题。玉米叶脉密度高，叶脉起始亚型发育顺序可预测，是研究动态维管事件的理想实验系统。pi将测试时空基因调控导致同一植物脉间独特发育和生理的假设。这项研究将利用最先进的单细胞转录组学来跟踪和识别所有发育中的叶细胞中叶脉亚型发育的关键命运决定因素。接下来，研究人员将合作应用基因组学来加速玉米特化维管突变体的定位和功能表征。通过构建神经网络机器学习模型，本研究将从数万张清除的叶片图像中预测维管表型，对942个自交系图谱进行GWAS分析，剖析维管发育的遗传结构和等位基因之间的时空基因表达变化。从这些目标中获得的数据将为专门维管性状的精确育种提供信息，以继续改进玉米，以满足全球对粮食、饲料和燃料的需求。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。

Because of its utility as a source of food, animal feed, fuel, and other bioproducts, maize (Zea mays subsp. mays) production is valued at over $83 billion annually in the US. The increased frequency of extreme weather events like drought and flooding threaten American businesses and communities that depend on maize production. Researchers from Oregon State University (PI Leiboff) and the University of California, Berkeley (Co-PI Chuck) are working together to understand the mechanisms that regulate the maize vascular system to prepare our nation’s most important crop for climate change. In a recent breakthrough, Co-PI Chuck showed that amongst the complex network of seemingly identical veins within a maize plant, there are specialized veins with unique functions in surviving drought and accommodating plant microbes. By applying cutting edge techniques in single cell genomics, mutant mapping, and machine learning, PI Leiboff and Co-PI Chuck will uncover the secret genetics that define these specialized veins and provide tools for the rapid improvement of the maize vascular system by precision breeding. The PIs will use techniques developed by this research to provide rural and urban high school teachers with low-cost smartphone microscope kits that will improve high school student engagement with the life sciences. Data from this research will be used to produce a free educational resource, “Teaching with Single Cell Genomics” providing university educators with lesson plans, animations, multimedia presentations, and a supplemental textbook to ensure that our next generation of undergraduates in biology receive training in this revolutionary new technology.The dynamic production of the dense network of veins is critical for efficient photosynthesis in C4 grasses. Grasses produce this network through reiterative developmental programs that maintain physiological functions as tissues grow or encounter new environments. Although the products of these programs are similar in structure, these repeated events provide a unique opportunity for specialization amongst tissues generated at different times and locations. This research explores the biological question of how similar tissues within an organism can be constructed for different purposes. Maize is an ideal experimental system for studying dynamic vascular events because of its high vein density and predictable developmental sequence of initiating leaf vein subtypes. The PIs will test the hypothesis that spatiotemporal gene regulation leads to unique development and physiology amongst veins in the same plant. This research will leverage state-of-the-art advances in single cell transcriptomics to track and identify key fate-determining factors in developing leaf vein subtypes amongst all developing leaf cells. Next, researchers will collaboratively apply genomics to accelerate the mapping and functional characterization of maize specialized vascular mutants. By constructing a neural network machine learning model, this work will predict vascular phenotypes from tens of thousands of cleared leaf images to perform a GWAS of a 942-inbred mapping panel, dissecting the genetic architecture of specialized vascular development and spatiotemporal gene expression changes between alleles. Data derived from these aims will inform the precision breeding of specialized vascular traits for the continued improvement of maize to meet global demand for food, feed, and fuel.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.