RII Track-2 FEC: Comparative Genomics and Phenomics Approach to Discover Genes Underlying Heat Stress Resilience in Cereals

非技术描述该研究基础设施改善(RII)Track-2聚焦EPSCoR协作(RII Track-2 FEC)奖是内布拉斯加州大学林肯分校、阿肯色州立大学和堪萨斯州立大学的联合项目。该项目将创造在压力环境下提高小麦和水稻产量所需的基础知识。全球、区域和农场层面的趋势表明，夜间最低气温的上升幅度明显高于白天最高气温的上升速度。夜间气温的升高显著降低了水稻和小麦等主要农作物的粮食产量和质量，这两种作物总共提供了全球人类热量摄入量的50%以上。为了确保全球粮食安全，迫切需要提高作物对高温胁迫引起的产量和品质损失的复原力。该项目将以内布拉斯加州、堪萨斯州和阿肯色州的互补专业知识和基础设施为基础，开发新的研究基础设施，并做出最终导致为美国农民开发更高产、更有弹性的品种的发现。该计划将创造机会，与关键的行业合作伙伴合作，确保有希望的发现转化为应用，为农民带来经济效益。计划的劳动力发展活动将集中于指导六名早期职业教师，培训博士后以及研究生和本科生，并扩大代表不足的少数民族和缺乏服务的农村人口在STEM领域的参与。该项目旨在阐明水稻和小麦耐高温的生理和遗传基础，将这些发现转化为公共和私人育种项目的遗传和表型标记，并为STEM教育发展广泛的连续性。为此，研究人员采取了涉及植物生理学家、数量遗传学家、计算生物学家、生物化学家、工程师、信息学家和精确农学家的多学科方法。该计划正在利用自动化的基于图像的表型组学方法，结合应用于小麦和水稻多样性面板的转录组学和代谢组学，建立基因组与表型组之间的联系。通过这种方法发现的基因和途径将进行功能测试，以确定它们在提高水稻和小麦的温度适应性方面所起的作用。计划中的方法跨温室和田间尺度进行整合；以高时空分辨率捕捉谷物发展期间环境和基因组之间的复杂相互作用；并在基于模型的量化框架内将基因组学和表现学结果结合起来。这项研究将通过可视化平台使生物学家更容易获得整合的数据集。六名早期职业教师将接受专业进步和与国际表现学社区互动的指导。将为植物科学、农业工程、计算生物学、生物化学、统计学和计算机科学的学生开发一门跨学科的研究生课程。这门课程的在线课程将确保所有三所参与大学的学生参与其中。

Non-technical DescriptionThis Research Infrastructure Improvement (RII) Track-2 Focused EPSCoR Collaborations (RII Track-2 FEC) award is a joint project among the University of Nebraska-Lincoln, Arkansas State University, and Kansas State University. The project will create the foundational knowledge needed to improve wheat and rice yields under stressful environments. Trends at the global-, regional-, and farm-level point to an increase in minimum night temperatures that is significantly higher than the rate of increase in maximum day temperatures. Increases in night temperatures significantly decrease the grain yield and quality of major crops such as rice and wheat, which together provide over 50% of the caloric intake for humans worldwide. To ensure global food security, there is an urgent need to improve crop resilience to high night temperature stress-induced yield and quality losses. This project will build upon complementary expertise and infrastructure in Nebraska, Kansas, and Arkansas to develop novel research infrastructure and make discoveries that ultimately lead to development of higher yielding and resilient cultivars for the U.S. farmers. This program will create opportunities to collaborate with key industry partners to ensure that promising discoveries are translated into applications with economic benefit for farmers. Planned workforce development activities will focus on mentoring six early career faculty members, training of postdocs and graduate and undergraduate students, and broadening the participation of underrepresented minorities and underserved rural population in STEM fields.Technical DescriptionThis project aims to elucidate the physiological and genetic basis of high night temperature resiliency of rice and wheat, translate these discoveries into genetic and phenotypic markers for public and private breeding programs, and develop a broad continuum for STEM education. To this end, the investigators are taking a multidisciplinary approach involving plant physiologists, quantitative geneticists, computational biologists, biochemists, engineers, informaticians, and precision agronomists. This program is building genome to phenome linkages using automated image-based phenomics approaches in combination with transcriptomics and metabolomics applied to wheat and rice diversity panels. Gene and pathways discovered from this approach will be functionally tested for their role in improving the temperature resilience in rice and wheat. The planned approach integrates across greenhouse and field scales; captures complex interactions between the environment and genome during grain development at high spatiotemporal resolution; and couples genomics and phenomics outcomes within a quantitative, model-based framework. This research will make the integrated dataset more accessible to biologists through a visualization platform. Six early career faculty will be mentored for professional progress and interactions with international phenomic communities. An Interdisciplinary graduate course will be developed for students in plant sciences, agricultural engineering, computational biology, biochemistry, statistics, and computer sciences. On-line offerings of this course will ensure student participation from all three participating universities.