NSF2026: EAGER: Identifying microbes’ population-level environmental responses using Bayesian modeling

NSF2026：EAGER：使用贝叶斯模型识别微生物和人口水平的环境响应

• 批准号: 2033934
• 负责人: Dana Hunt
• 金额: $ 29.97万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2033934&HistoricalAwards=false
• 关键词: NSF2026; EAGER; Identifying; microbes; population

With support from the Directorate for Geosciences and the NSF 2026 Fund Program in the Office of Integrated Activities, Professors Dana Hunt, Mark Borsuk, and James Clark at Duke University conduct research that provides new insights into the factors that shape microbial productivity and function in the oceans as well as how this change during extreme events such as hurricanes. The driver of this research comes from the fact that marine microbes provide essential ecosystem services, including primary production (photosynthesis) and organic matter turnover, that sustains all marine organisms. That said, it still remains unclear as to what extent microbiomes are shaped by environmental factors, such as temperature and primary productivity, that can be altered by season, disturbances, global change, and other factors. This research combines long-term observations at a coastal site at Beaufort Island, North Carolina and uses these data to capture annual changes in microbiomes and their environments using high frequency measurements that were taken before and after hurricanes Florence (2018) and Dorian (2019). Examining the impact of hurricanes on marine biomes is important because hurricanes are multi-factor disturbances that introduce both foreign freshwater and terrestrial microbes into a stable system while altering salinity, nutrients, and organic matter in the coastal ocean. This work combines information from field observations and modeling to develop new approaches that will allow the differentiation of factors that often co-occur in field samples, such as warmer temperatures and higher primary production that occur during the summer months in the coastal Atlantic Ocean. By integrating multiple aspects of microbiome research, this work deepens current understanding of the coastal ocean microbiome system and its functionality. It also develops new testable hypothesis to guide future research. Broader impacts of the work include advanced training for undergraduate, graduate, and postdoctoral students, as well as translating research results into products for K-12 students and the public. Additional impacts include the production of detailed user manuals and training materials for software developed in the course of the project to facilitate the use of research results for future microbiome research and undergraduate education.This research leverages an established decade-long microbial time-series, the Piver’s Island Coastal Observatory (PICO, Beaufort Inlet, NC USA) to improve the modeling of microbial populations and their relationship to changing environments. With 10 years of weekly (or more frequent) microbial community SSU rRNA gene sequence datasets, coupled with the suite of sample, in-situ, and environmental parameters, the PICO dataset is one of the most complete, long-term datasets for coastal ocean microbiomes. The work carried out uses the application of Bayesian modeling to the PICO time series to improve understanding and predictions of microbiome responses to ocean conditions. Bayesian models are well suited to microbial systems because they have the ability to handle sparse datasets, capture non-linear responses to environmental changes, and include impacts of disturbances. This research integrates microbiome applications and the Bayesian model gjamTime. This combination has the potential to transform microbial ecology by leveraging advances in multivariate time-series methods that accommodate the dependence among individual taxa and their environment over time. One goal of the project is to test model predictions using time-series data from natural disturbances (i.e., hurricanes) at the Beaufort Inlet site and explore various key environmental parameters such as temperature (+3 °C) and primary production as key environmental parameters. Similar work will be done more broadly for the ocean. Impacts of the research extend beyond the targeted coastal dataset as, if successful, the approach can be applied to other diverse study systems such as soil and human microbiomes. It can also be used to address questions about environmental filtering, disturbance and stochasticity, each of which is critical to understanding the factors and processes that govern microbial responses to environmental change.This project responds to the NSF2026 Idea Machine winning entries of "Global Microbiome in a Changing Planet" and "Imagine a Life with Clean Oceans"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.

在地球科学理事会和综合活动办公室的NSF 2026基金项目的支持下，杜克大学的Dana Hunt，Mark Borsuk和James Clark教授进行了研究，为塑造海洋中微生物生产力和功能的因素提供了新的见解，以及在飓风等极端事件中这种变化。这项研究的驱动力来自海洋微生物提供基本的生态系统服务，包括维持所有海洋生物的初级生产（光合作用）和有机物周转。也就是说，目前还不清楚微生物组在多大程度上受到环境因素的影响，如温度和初级生产力，这些因素可以被季节，干扰，全球变化和其他因素改变。这项研究结合了在北卡罗来纳州博福特岛沿海地区的长期观察，并使用这些数据来捕捉微生物组及其环境的年度变化，这些数据是在飓风佛罗伦萨（2018年）和多里安（2019年）之前和之后进行的高频测量。研究飓风对海洋生物群落的影响很重要，因为飓风是多因素干扰，将外来淡水和陆地微生物引入稳定的系统，同时改变沿海海洋的盐度，营养物质和有机物。这项工作结合了实地观察和建模的信息，开发了新的方法，可以区分野外样本中经常同时发生的因素，例如大西洋沿海夏季月份发生的温度升高和初级生产力提高。通过整合微生物组研究的多个方面，这项工作加深了目前对沿海海洋微生物组系统及其功能的理解。它还发展了新的可检验假设，以指导未来的研究。这项工作的更广泛影响包括对本科生、研究生和博士后的高级培训，以及将研究成果转化为面向K-12学生和公众的产品。其他影响包括为项目过程中开发的软件制作详细的用户手册和培训材料，以促进将研究成果用于未来的微生物组研究和本科生教育。（皮科，博福特湾，美国北卡罗来纳州），以改进微生物种群及其与变化环境的关系的建模。凭借10年每周（或更频繁）的微生物群落SSU rRNA基因序列数据集，加上一套样本，原位和环境参数，皮科数据集是沿海海洋微生物群落最完整，最长期的数据集之一。这项工作将贝叶斯建模应用于皮科时间序列，以提高对微生物组对海洋条件反应的理解和预测。贝叶斯模型非常适合微生物系统，因为它们能够处理稀疏数据集，捕获对环境变化的非线性响应，并包括干扰的影响。这项研究整合了微生物组应用和贝叶斯模型gjamTime。这种组合有可能通过利用多变量时间序列方法的进步来改变微生物生态学，这些方法可以适应个体分类群及其环境之间随时间的依赖性。该项目的一个目标是使用来自自然扰动的时间序列数据（即，在博福特湾现场进行了一系列的模拟（例如飓风），并探索了各种关键环境参数，如温度（+3 °C）和初级生产作为关键环境参数。类似的工作将在更广泛的海洋中进行。研究的影响超出了目标沿海数据集，如果成功，该方法可以应用于其他不同的研究系统，如土壤和人类微生物组。它还可以用来解决有关环境过滤、干扰和随机性的问题，每一个都是理解微生物对环境变化的反应的因素和过程的关键。这个项目响应了NSF 2026创意机器的获奖作品“变化中的星球上的全球微生物组”和“想象一个清洁海洋的生活”该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。

项目成果

Rapid changes in coastal ocean microbiomes uncoupled with shifts in environmental variables

• DOI: 10.1111/1462-2920.16086
• 发表时间: 2022-06-17
• 期刊: ENVIRONMENTAL MICROBIOLOGY
• 影响因子: 5.1
• 作者: Gronniger, Jessica L.;Wang, Zhao;Hunt, Dana E.
• 通讯作者: Hunt, Dana E.

Vertical community patterns of Labyrinthulomycetes protists reveal their potential importance in the oceanic biological pump

• DOI: 10.1111/1462-2920.15709
• 发表时间: 2021-08-16
• 期刊: ENVIRONMENTAL MICROBIOLOGY
• 影响因子: 5.1
• 作者: Bai, Mohan;Xie, Ningdong;Wang, Guangyi
• 通讯作者: Wang, Guangyi