RII Track-1: Microbiomes of Aquatic, Plant, and Soil Systems across Kansas

微生物组学是对特定环境中存在的所有微生物分类群的组合遗传物质的研究，越来越被视为解决生态问题的重要方法，包括维持农业生产同时保持土壤和水质等社会挑战。该项目将对植物、土壤和水生微生物群落及其环境特征进行大规模调查，对这些群落进行分类，并评估微生物群落影响作物生产、土壤条件和水质的能力。堪萨斯州是这个项目理想的自然实验室，因为整个州的降水和土地利用有很大的梯度。项目团队将整合其研究和教育活动，以提高城市和农村地区、主流、经济弱势和第一代大学生的科学、技术、工程和数学（STEM）教育能力，增强美国原住民和其他代表性不足群体的参与，并扩大微生物、植物和土壤科学、基因组学、生物信息学和生态学方面的劳动力。该项目与2016年白宫倡议协同，以提高对微生物组的理解。主要项目目标是：建立对微生物组介导的生态系统功能的机制理解，预测生态系统对降水和土地利用模式变化的反应，并确定选择和利用微生物组产生所需特征的方法，如提高农业生产力或耐旱性，有效的养分利用和提高土壤质量。先进的分子技术，包括大规模的DNA平行测序、扩增子测序和亚转录组学分析，将与大规模的田间和温室实验相结合。将建立数据库，将宏基因组数据与全州的环境参数联系起来，并确定在不同条件下可能产生期望反应的基因型。数据分析将使用标准的统计技术和先进的方法，如结构方程建模。结果将用于确定最大限度地提高植物生产力，水质和土壤健康的做法。

Non-technical DescriptionMicrobiome science, the study of the combined genetic material from the set of all microorganism taxa present in a particular environment, is increasingly seen as important for addressing ecological questions, including societal challenges such as sustaining agricultural production while maintaining soil and water quality. This project will undertake a large survey of plant, soil and aquatic microbiomes and their environmental characteristics to catalog these communities and assess the ability of microbiomes to influence crop production, soil condition and water quality. Kansas is an ideal natural laboratory for this project due to the large gradients in precipitation and land use across the state. The project team will integrate its research and educational activities to improve Science, Technology, Engineering, and Mathematics (STEM) education capacity in both urban and rural areas, among mainstream, economically disadvantaged, and first-generation college students, enhance the participation of Native Americans and other under-represented groups, and to expand the workforce in microbial, plant, and soil science, genomics, bioinformatics and ecology.Technical Description This project is synergistic with the 2016 White House Initiative to improve understanding of microbiomes. Major project goals are to: develop a mechanistic understanding of microbiome-mediated ecosystem functions, predict ecosystem responses to changes in precipitation and land-use patterns, and identify ways to select for and utilize microbiomes to produce desired characteristics such as increased agricultural productivity or drought tolerance, efficient nutrient utilization, and enhanced soil quality. Advanced molecular techniques, including massively parallel sequencing of DNA, amplicon sequencing, and metatranscriptomic analyses, will be utilized in conjunction with large-scale field and greenhouse experiments. Databases will be created to link metagenomic data with environmental parameters across the state and identify genotypes that may confer desired responses under varying conditions. Data will be analyzed using standard statistical techniques and advanced methods such as structural equation modeling. Results will be used to identify practices to maximize plant productivity, water quality, and soil health.