Collaborative Research: MTM 2: Searching for General Rules Governing Microbiome Dynamics Using Anaerobic Digesters as Model Systems

合作研究：MTM 2：使用厌氧消化器作为模型系统寻找微生物组动力学的一般规则

• 批准号: 2025558
• 负责人: Jizhong Zhou
• 金额: $ 129.68万
• 依托单位: University of Oklahoma Norman Campus
• 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: 2020-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2025558&HistoricalAwards=false
• 关键词: Collaborative; Research; MTM; Searching; General

Microorganisms inhabit almost every imaginable environment on Earth, playing integral roles in various ecosystem processes. Microbiomes, collections of microbes in specific habitats, change significantly from place to place and over time. Although rapid advances in genetic technologies have revolutionized our understanding of microbiomes, the rules governing microbiome function are yet to be learned. Research to understand these mechanisms in natural ecosystems can be difficult due to their open nature and extremely high diversity. Adequately capturing this complexity results in a need for extremely large datasets over long time scales. By contrast to open natural systems, engineered anaerobic digesters (ADs) are enclosed systems with controlled environments. AD systems are used globally for waste treatment and represent the largest engineering application of microbial biotechnology. As such, AD systems provide an ideal model system for understanding the rules governing microbiome function because of their microbial diversity, environmental significance, and the ability to control the environment. The goal of this research is to identify the rules controlling microbiome dynamics in ADs that can be used for other microbial ecosystems. This study will provide fundamental knowledge critical to predicting microbiome behavior in engineered and natural microbial ecosystems. Benefits to society resulting from this project will include improved science-based management of microbial ecosystems in both engineered and natural systems. Additional benefits include the training of the next generation of microbiome professionals with broad interdisciplinary expertise and skills to understand and control microbiome dynamics. The overall goal of this project is to identify general ecological rules governing microbiome dynamics in different ecosystems with a focus on ADs as model microbial ecosystems. This will be achieved by examining whether general rules exist for species-area relationships as are known to exist in ecology. The four fundamental ecological processes of selection, dispersal, diversification, and drift will serve as a general theory to explain how microbial communities in microbiomes are assembled across space and time. Specific research objectives to achieve this goal include the following tasks: 1) Laboratory AD systems will be used to determine the short- ( 1 year) and long-term (15 years) stability of microbiome biodiversity, structure, and function in responses to various environmental changes; 2) Advanced statistical tools will be used to elucidate underlying community assembly mechanisms in AD systems; 3) Novel mathematical approaches will be developed to detect the transient dynamics of AD microbiomes in response to environmental perturbations; and 4) Novel metagenomics-enabled anaerobic digestion models will be developed to provide effective frameworks for predicting and manipulating the dynamics of AD systems for desired functions. Resulting rules describing AD microbiome dynamics will be tested for their utility in describing other microbiomes from a variety of habitats including soils, marine, lacustrine, groundwater, gut, and other engineered systems. Cross-disciplinary training and workforce development will be achieved through research, training, and workshops to meet future needs for microbiome professionals.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.

微生物几乎栖息在地球上每一个可以想象的环境中，在各种生态系统过程中发挥着不可或缺的作用。微生物组是特定栖息地中微生物的集合，随着时间和地点的不同，微生物群落会发生显着变化。尽管遗传技术的快速进步彻底改变了我们对微生物组的理解，但控制微生物组功能的规则仍有待了解。由于自然生态系统的开放性和极高的多样性，研究了解自然生态系统中的这些机制可能很困难。充分捕捉这种复杂性导致需要长时间尺度上的极大数据集。与开放的自然系统相比，工程厌氧消化器（AD）是环境受控的封闭系统。 AD 系统在全球范围内用于废物处理，代表了微生物生物技术最大的工程应用。因此，AD 系统由于其微生物多样性、环境重要性以及控制环境的能力，为理解微生物组功能的规则提供了一个理想的模型系统。这项研究的目标是确定控制 AD 中微生物组动态的规则，这些规则可用于其他微生物生态系统。这项研究将为预测工程和自然微生物生态系统中的微生物组行为提供至关重要的基础知识。该项目给社会带来的好处将包括改善工程系统和自然系统中微生物生态系统的科学管理。其他好处包括培训下一代微生物组专业人员，使其具有广泛的跨学科专业知识和技能，以了解和控制微生物组动态。该项目的总体目标是确定不同生态系统中控制微生物组动态的一般生态规则，重点关注 AD 作为微生物生态系统模型。这将通过检查生态学中已知的物种与区域关系是否存在一般规则来实现。选择、扩散、多样化和漂移这四个基本生态过程将作为一个通用理论来解释微生物组中的微生物群落如何跨空间和时间组装。实现这一目标的具体研究目标包括以下任务： 1）实验室 AD 系统将用于确定微生物组生物多样性、结构和功能响应各种变化的短期（1 年）和长期（15 年）稳定性。环境变化； 2）先进的统计工具将用于阐明AD系统中底层的社区组装机制； 3）将开发新的数学方法来检测 AD 微生物组响应环境扰动的瞬态动态； 4) 将开发新的宏基因组学厌氧消化模型，为预测和操纵 AD 系统的动力学以实现所需的功能提供有效的框架。描述 AD 微生物组动态的所得规则将在描述来自各种栖息地（包括土壤、海洋、湖泊、地下水、肠道和其他工程系统）的其他微生物组方面的实用性进行测试。将通过研究、培训和研讨会来实现跨学科培训和劳动力发展，以满足微生物组专业人员的未来需求。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Experimental warming leads to convergent succession of grassland archaeal community

实验变暖导致草原古菌群落趋同演替

• DOI: 10.1038/s41558-023-01664-x
• 发表时间: 2023-05-04
• 期刊: Nature Climate Change
• 影响因子: 30.7
• 作者: Ya Zhang;D. Ning;Linwei Wu;M. Yuan;Xishu Zhou;Xue Guo;Yuanliang Hu;Siyang Jian;Zhifeng Yang;Shun Han;Jiajie Feng;Jialiang Kuang;Carolyn R. Cornell;Colin T. Bates;Yupeng Fan;Jonathan P. Michael;Yang Ouyang;Jia;Zhipeng Gao;Z. Shi;Naijia Xiao;Ying Fu;Aifen Zhou;Liyou Wu;Xue;Yunfeng Yang;J. Tiedje;Ji
• 通讯作者: Ji

Assessing mechanisms for microbial taxa and community dynamics using process models

使用过程模型评估微生物分类群和群落动态的机制

• DOI: 10.1002/mlf2.12076
• 发表时间: 2023-09-12
• 期刊: mLife
• 作者: Linwei Wu;Yunfeng Yang;D. Ning;Qun Gao;Huaqun Yin;Naija Xiao;Benjamin Y. Zhou;Si Chen;Qiang He;Jizhong Zhou
• 通讯作者: Jizhong Zhou

Disentangling direct from indirect relationships in association networks

摆脱关联网络中的直接关系和间接关系

• DOI: 10.1073/pnas.2109995119
• 发表时间: 2022-01-11
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1
• 作者: Xiao N;Zhou A;Kempher ML;Zhou BY;Shi ZJ;Yuan M;Guo X;Wu L;Ning D;Van Nostrand J;Firestone MK;Zhou J
• 通讯作者: Zhou J