Collaborative Research: URoL:ASC: Determining the relationship between genes and ecosystem processes to improve biogeochemical models for nutrient management

合作研究：URoL:ASC：确定基因与生态系统过程之间的关系，以改进营养管理的生物地球化学模型

• 批准号: 2319124
• 负责人: Jeseth Delgado Vela
• 金额: $ 16.34万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2319124&HistoricalAwards=false
• 关键词: Collaborative; Research; URoL; ASC; Determining

Clean water is essential to life and critical for maintaining healthy ecosystems. Whether in the Chesapeake Bay or engineered systems like wastewater treatment plants, managing clean water requires modelling how these ecosystems respond to environmental changes due to remediation or climate change. Microbial processes are crucial to such ecosystem responses, but models typically do not incorporate any direct measurements of the microbes in the ecosystem (its microbiome), either during model development or validation. This project takes advantage of a fundamental rule of life, that cellular processes are encoded by the genes in living things, to provide that direct measurement of microbes in an ecosystem. The team will focus on the process of denitrification, which removes excess nitrogen pollution and is critical in both wastewater treatment plants and Chesapeake Bay. They will use controlled, laboratory experiments to investigate factors influencing the relationship between the abundance of particular genes involved in denitrification, as measured across all the microbes in an ecosystem, and denitrification rates. The project will determine how gene abundance data can improve the predictive value of different types of models that encode denitrification in different ways and that are used in managing Chesapeake Bay and wastewater treatment plants. By listening to the concerns and input of water management and community partners throughout the project, the team will focus on efforts to benefit those most impacted by wastewater and Chesapeake Bay water quality, ultimately improving model predictions that guide management decisions.This work uses gene abundance, measured by quantitative PCR, as a non-conservative tracer of microbial denitrification, serving as a proxy for “functional group cell density”, to estimate cell-density dependent reaction rates in nutrient models of wastewater, receiving waterbodies, and downstream ecosystems. Models of ecosystems, like the Chesapeake Bay, typically do not encode cell-density dependent reactions but may be more accurate if reaction rates are density dependent and functional group abundance is tracked and calibrated using gene abundance. Controlled bioreactor experiments will test the relationship between gene abundance and denitrification rates, and whether that relationship changes in response to disturbance frequency. The project will systematically compare how encoding denitrification as either cell-density dependent or independent reactions influences model results in the Chesapeake Bay. We will also test whether gene abundance, can be used as a non-conservative tracer to calibrate other cell-density dependent reactions in both the Bay and wastewater treatment models. We will work with management partners to understand the budgetary and technical limitations of incorporating gene abundance measurements into their typical surveillance and modeling workflows, and design solutions to advance its use as a measure of denitrification to improve model predictions. This work will serve as an example of how gene abundance can be used as an additional input into models encoding microbial processes across a range of managed ecosystems.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.

清洁水对生命至关重要，对于维持健康的生态系统至关重要。无论是在切萨皮克湾（Chesapeake Bay）还是废水处理厂等工程系统中，管理清洁水都需要建模这些生态系统如何应对补救或气候变化引起的环境变化。微生物过程对于此类生态系统响应至关重要，但是在模型开发或验证过程中，模型通常不会在生态系统（其微生物组）中纳入微生物的任何直接测量。该项目利用了生命的基本规则，细胞过程是由生物中的基因编码的，以提供生态系统中微生物的直接测量。该小组将重点关注反硝化的过程，该过程消除了过量的氮污染，并且在废水处理厂和切萨皮克湾都至关重要。他们将使用受控的实验室实验来研究因素，影响了硝化特定基因的丰富性之间的关系，如生态系统中所有微生物的测量，以及硝化率。该项目将确定基因丰度数据如何以不同方式改善不同类型的模型的预测价值，这些模型以不同的方式编码非硝化模型，并用于管理Chesapeake湾和废水处理厂。 By listening to the concerns and input of water management and community partners throughout the project, the team will focus on efforts to benefit those most impacted by wastewater and Chesapeake Bay water quality, ultimately improving model predictions that guide management decisions.This work uses gene abundance, measured by quantitative PCR, as a non-conservative tracer of microbial denitriification, serving as a proxy for “functional group cell density”, to估计废水养分模型中的细胞密度依赖性反应速率，接受水体和下游生态系统。生态系统的模型，例如切萨皮克湾，通常不编码细胞密度依赖性反应，但如果反应速率取决于密度并使用基因抽象来跟踪和校准，则可能更准确。受控的生物反应器实验将测试基因抽象和分解率之间的关系，以及这种关系是否会响应灾难频率而变化。该项目将系统地比较作为细胞密度依赖性或独立反应影响模型导致切萨皮克湾的模型的编码方式。我们还将测试基因丰度是否可以用作非保守的示踪剂，以校准海湾和废水处理模型中其他细胞密度依赖性反应。我们将与管理合作伙伴合作，了解将基因丰富度测量结果纳入其典型的监视和建模工作流程的预算和技术局限性，以及设计解决方案，以推动其用作衡量硝化作用以改善模型预测的方法。这项工作将作为如何将基因丰度用作编码一系列托管生态系统的微生物过程的模型的额外输入的一个例子。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛影响的评估标准来评估的支持。