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

清洁的水对生命至关重要，对维持健康的生态系统至关重要。无论是在切萨皮克湾，还是像污水处理厂这样的工程系统，管理清洁的水都需要对这些生态系统如何应对因修复或气候变化而产生的环境变化进行建模。微生物过程对这种生态系统的反应至关重要，但在模型开发或验证期间，模型通常不包括对生态系统(其微生物群)中微生物的任何直接测量。这个项目利用了生命的一个基本规则，即细胞过程是由生物中的基因编码的，以提供对生态系统中微生物的直接测量。该团队将专注于反硝化过程，这一过程消除了过量的氮污染，对污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调查影响参与反硝化的特定基因丰度和反硝化率之间关系的因素，这些基因涉及生态系统中的所有微生物。该项目将确定基因丰度数据如何提高不同类型模型的预测价值，这些模型以不同的方式编码反硝化作用，并用于管理切萨皮克湾和污水处理厂。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，该团队将专注于努力使那些受废水和切萨皮克湾水质影响最大的人受益，最终改进指导管理决策的模型预测。这项工作使用定量聚合酶链式反应测量的基因丰度作为微生物反硝化作用的非保守示踪剂，作为“功能基团细胞密度”的替代，以估计废水、接收水体和下游生态系统的营养模型中细胞密度依赖的反应率。像切萨皮克湾这样的生态系统模型通常不编码依赖于细胞密度的反应，但如果反应速度依赖于密度，并且使用基因丰度来跟踪和校准官能团丰度，则可能会更准确。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，以及这种关系是否会随着干扰频率的变化而变化。该项目将系统地比较作为细胞密度相关或独立反应的反硝化编码如何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，以校准海湾和废水处理模型中其他依赖细胞密度的反应。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，以促进其作为反硝化措施的使用，以改善模型预测。这项工作将作为一个例子，说明如何将基因丰度用作对一系列受管理的生态系统中的微生物过程进行编码的模型的额外输入。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影响审查标准进行评估，被认为值得支持。

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.