Collaborative Research: BoCP-Design: A multidomain microbial consortium to interrogate organic matter decomposition in a changing ocean

尽管现存量很低，但海洋中仍有大量的碳加工，主要是由不同的微生物消费者进行的。直到最近，细菌一直被认为是有机物的主要降解者，而非细菌消费者在碳循环中的作用在很大程度上被忽视了。然而，真菌等真核生物表现出不同的代谢能力和对环境变量的反应，这表明全球变化可能会改变海洋中微生物活动的平衡，并可能改变海洋碳的命运。在这里，研究人员将现场数据与建模和实验室实验与代表性培养相结合，以确定微生物在海洋有机物降解中的功能作用，并确定它们对不断变化的环境条件的反应。该项目将打开新的窗口，了解微生物代谢的多样性，以及这些动态将如何随着全球变化导致的气温和其他环境因素的增加而变化。此外，该项目建立了一支新的科研团队，并扩大了从K-5教师到本科生和博士生的各级科学培训。该项目将利用长达十年的沿海微生物时间序列--皮弗岛海岸观测站(PICO)，研究不同的异养微生物群落(细菌、浮游植物、真菌和迷宫霉菌原生生物)如何在不同的温度条件下代谢碳化合物。该项目将开发一个模型微生物联盟，通过将功能、生物相互作用和依赖环境的反应整合到一个建模框架中，有可能改变人们对沿海系统碳循环的看法。经验动力学建模将识别观察到的动态的驱动因素，区分因果关系和相关性，推断可能未观察到的变量(例如捕食)的影响，并量化有机体之间的相互作用。这些数据将进一步用于开发一个可培养的模型联合体，其成员代谢浮游植物衍生的有机物质的不同成分。为了检验模型预测和该联合体代表复杂微生物群落的能力，将对模型联合体和一个“野生”沿海海水微生物群落进行检测，以了解随着温度升高(+4°C)功能的变化(浮游植物DOM的降解)。这些实验将比较单个分离物、联合体和一个野生沿海微生物组在组成/丰度、基因表达和特定化合物的降解方面的结果。最后，实验结果将被用来参数化和改进非线性动力学集合稀疏辨识模型，该模型可以预测不同气候情景下海洋系统中碳的去向和转化。虽然这项研究利用了海洋微生物群方面的现有专业知识，但这种模式联盟方法可以应用于不同的系统，以回答有关环境过滤、生物相互作用和功能多样性的问题，这些问题对于预测生态系统对环境变化的反应至关重要。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。

Despite low standing biomass, extensive carbon processing occurs in the oceans, largely by diverse microbial consumers. Until recently, bacteria were considered the main degraders of organic matter, while non-bacterial consumers’ role in carbon cycling was largely been ignored. However, eukaryotes such as fungi exhibit distinct metabolic capacities and responses to environmental variables, suggesting global change may alter the balance of microbial activities in the oceans and potentially alter the fate of marine carbon. Here, researchers integrate field data with modeling and laboratory experiments with representative cultures to identify microbes’ functional roles in marine organic matter degradation and to determine their response to changing environmental conditions. This project will open new windows into the diversity of microbial metabolisms and how these dynamics will shift with global change driven increases in temperature and other environmental factors. Additionally, this projects builds a new scientific research team and expands scientific training at levels ranging from K-5 teachers, to undergraduate and PhD students. This project will leverage a decade-long, coastal microbial time series, the Piver’s Island Coastal Observatory (PICO), to examine how diverse heterotrophic microbial communities (bacteria, phytoplankton, fungi and Labyrinthulomycetes protists) metabolize carbon compounds under different thermal regimes. This project will develop a model microbial consortium that has the potential to transform perception of carbon cycling in coastal systems by integrating functional, organismal-interaction and environment-dependent responses into a modeling framework. Empirical Dynamic Modeling will identify drivers of the observed dynamics, differentiate causation from correlation, infer effects of possibly unobserved variables (e.g. predation), and quantify interactions between organisms. This data will further be used to develop a culturable model consortium whose members metabolize distinct components of phytoplankton-derived organic matter. To test both model predictions and how well the consortium represents complex microbiomes, both the model consortium and a “wild” coastal seawater microbiome will be assayed for changes in function (phytoplankton DOM degradation) as temperatures increase (+4 °C). These experiments will compare outcomes for individual isolates, the consortium, and a wild coastal microbiome in composition/abundance, gene expression and degradation of specific compounds. Finally, experimental results will be used to parameterize and refine an Ensemble Sparse Identification of Nonlinear Dynamics model that can predict the fate and transformation of carbon in marine systems under varying climate scenarios. While this research leverages existing expertise in marine microbiomes, this model consortium approach can be applied to diverse systems to answer questions about environmental filtering, organismal interactions and functional diversity critical to predicting ecosystem-level responses to environmental change.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.