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.

清洁水对于生命至关重要，对于维持健康的生态系统至关重要，无论是在切萨皮克湾还是污水处理厂等工程系统中，管理清洁水都需要模拟这些生态系统如何应对因修复或气候变化而导致的环境变化，这对于维持健康的生态系统至关重要。这种生态系统反应，但模型通常不包含对生态系统中微生物（其微生物组）的任何直接测量，无论是在模型开发还是验证过程中，该项目都利用了生命的基本规则，即细胞过程由基因编码。该团队将重点研究反硝化过程，该过程可消除氮过量污染，这对于废水处理厂和切萨皮克湾至关重要。研究影响反硝化作用的特定基因丰度（对生态系统中所有微生物进行测量）与反硝化率之间关系的因素。该项目将确定基因丰度数据如何提高不同类型编码模型的预测价值。通过在整个项目中听取水管理和社区合作伙伴的关注和意见，该团队将重点努力使受废水和切萨皮克湾水影响最大的人们受益。质量，最终改进指导管理决策的模型预测。这项工作使用通过定量 PCR 测量的基因丰度作为微生物反硝化的非保守示踪剂，作为“功能组细胞密度”的代理，来估计废水、受纳水体和下游生态系统的营养模型中的细胞密度依赖性反应速率，例如切萨皮克湾，通常不会编码细胞密度依赖性反应，但如果速率反应具有密度依赖性和功能性，则可能会更准确。使用基因丰度来跟踪和校准群体丰度。受控生物反应器实验将测试基因丰度和反硝化率之间的关系，以及该关系是否随干扰频率而变化。该项目将系统地比较编码方式。反硝化作为细胞密度依赖或独立反应影响切萨皮克湾的模型结果，我们还将测试基因丰度是否可以用作非保守示踪剂来校准海湾和废水处理中的其他细胞密度依赖反应。我们将与管理合作伙伴合作，了解将基因丰度测量纳入其典型监测和建模工作流程的预算和技术限制，并设计解决方案以推进其作为反硝化措施的使用，以改进模型预测。将作为一个例子，说明如何将基因丰度用作编码一系列受管理生态系统中微生物过程的模型的额外输入。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评估进行评估，被认为值得支持。影响审查标准。