MIM: Discovering in reverse – using isotopic translation of omics to reveal ecological interactions in microbiomes.

淡水河流中的微生物群包括数百种藻类、细菌、真菌和微型动物。这些微生物是如何相互作用的？随着有机体群体争夺稀缺的资源，这种相互作用在很大程度上是竞争性的吗？有多少是有帮助的，其中一个有机体合成了另一个有机体所需的资源？这些相互作用对食物网、淡水中藻类的生长以及环境中营养物质的循环有多重要？这项研究的重点是枝角藻，一种生活在世界各地温带湖泊和河流中的绿色淡水藻类，特别是它的微生物群落，即生活在枝角藻表面的数百种微生物。研究人员将使用化学示踪剂来揭示碳和氮等元素是如何在微生物群中移动的，因为这些元素对支持生产力非常重要。研究人员还将测试微生物群如何对营养和光线做出反应，这是推动淡水生态的因素。这项研究将提高对微生物如何支持健康的生态系统以及可能使系统发生有害藻华的因素的理解。将使用新的机器学习工具来开发一个预测框架，以了解微生物物种如何相互作用并影响宏观食物网(昆虫、鱼类、鸟类)。此外，将对十多名学生进行培训，并将制作面向普通观众的教育材料，包括视频、书面故事和艺术品。该培训计划强调教授研究人员在职业生涯的所有阶段，以及如何通过视觉、书面和口头讲故事来交流他们的科学。这个项目将开发绿色大型藻类Cladadhora，作为理解微生物群管理过程的模型系统。这项研究将把假说驱动的生态学理论与机器学习的数据驱动力量结合起来，推动微生物组科学的发展。研究人员将把“组学”数据与最先进的稳定同位素技术结合在一起，例如定量稳定同位素探测，使用18O标记水的稳定同位素测量单个微生物物种的生长和死亡率；使用13C和15N的qSIP和Chip-SIP，通过测量核酸的同位素组成来量化特定分类群的C和N同化；以及NanoSIMS，分析通过微生物组及其宿主移动的C和N的空间和时间模式。将进行实验，以研究(1)微生物组在一个有充分记录的演替序列中的变化，(2)生产力对营养添加的反应，以及(3)对光的反应。跨学科的方法将通过跨越通常使用的物种共现矩阵的数据矩阵，在生物学和机器学习方面发展新的科学。将增加的其他特征包括特定分类群的生长和死亡率、碳和氮的吸收、新陈代谢和系统发育。加入这些将提高我们了解复杂微生物群中微生物相互作用的能力。利用机器学习，研究人员将建立和训练模型，以吸收这些同时存在的多个数据矩阵(一起称为张量)。这一方法将推动产生反映构成生态社区的关键相互作用(竞争、互惠、促进、寄生或捕食)重要性的相互作用术语。该项目由数学和物理科学(MPS)局的化学部(CHE)和生物科学(BIO)局的环境生物学(DEB)共同资助。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。

Microbiomes in freshwater rivers comprise hundreds of species of algae, bacteria, fungi and microscopic animals. How do these groups of micro-organisms interact? Are the interactions largely competitive, as groups of organisms vie for scarce resources? How many are helpful, where one organism synthesizes a resource that another needs? How important are these interactions for food webs, for the growth of algae in freshwater, and for the cycling of nutrients in the environment? This research focuses on Cladophora glomerata, a green freshwater alga that occurs in temperate lakes and rivers worldwide, and specifically on its microbiome, the hundreds of micro-organisms that live right on the surface of Cladophora. Researchers will use chemical tracers that reveal how elements like carbon and nitrogen move through the microbiome, because these elements are important for supporting productivity. Researchers will also test how the microbiome changes in response to nutrient and light, factors that drive the ecology of freshwaters. This research will improve understanding of how microbiomes support healthy ecosystems and the factors that can shift systems to harmful algal blooms. Novel machine learning tools will be used to develop a predictive framework for understanding how microbial species interact with one another and affect macroscopic food webs (insects, fish, birds). In addition, over ten students will be trained and educational materials including videos, written stories and artwork geared towards general audiences will be created. The training program emphasizes teaching researchers, at all career stages, and how to communicate their science through visual, written and oral storytelling.This project will develop the green macro-alga, Cladophora, as a model system for understanding processes governing microbiomes. This research will advance microbiome science by combining hypothesis-driven ecological theory with the data-driven power of machine learning. Researchers will integrate ‘omics’ data with state-of-the-art stable isotope techniques, such as quantitative stable isotope probing, qSIP, which measures growth and mortality rates of individual microbial species using stable isotopes of 18O labeled water; qSIP and Chip-SIP using 13C and 15N, which quantifies taxon-specific C and N assimilation by measuring the isotopic composition of nucleic acids; and NanoSIMS, which analyzes spatial and temporal patterns of C and N movement through the microbiome and its host. Experiments will be performed to study (1) shifts in the microbiome over a well-documented successional sequence, (2) changes in productivity in response to nutrient addition, and (3) responses to light. Interdisciplinary approaches will develop new science in biology and machine learning through data matrices that span beyond the commonly used species co-occurrence matrices. Additional features that will be added include taxon-specific growth and mortality rates, carbon and nitrogen uptake, metabolism, and phylogeny. Adding these will improve our ability to understand microbial interactions in complex microbiomes. Using machine learning researchers will build and train models assimilating these multiple, simultaneous matrices of data (together, a tensor). This approach will advance efforts to generate interaction terms that reflect the importance of the key interactions (competition, mutualism, facilitation, parasitism, or predation) that structure ecological communities.This project is co-funded by the Division of Chemistry (CHE) in the Mathematics and Physical Sciences (MPS) Directorate and by the Division of Environmental Biology (DEB) in the Biological Sciences (BIO) Directorate.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.