Understanding Environment-Microbiome-Host metabolic interactions in Hydra

人类微生物组强烈影响宿主的生理和健康，生态失调可能对多种医疗状况产生巨大影响。因此，了解微生物组如何组装，对变化的环境做出反应，以及如何塑造它以改善生态失调细菌群落的影响是非常重要的。然而，人类微生物组在不同的个体中是多样的，并且非常复杂，与宿主以及细菌成员之间存在无数潜在的高阶相互作用。因此，希望在不太复杂的设置中研究宿主相关的微生物组，以更好地跟踪这些相互作用并确定一般机制，这些机制可以转移到更多样化的微生物群落中。在这里，我建议使用淡水水螅作为模型系统来研究在不断变化的营养条件下，其相关微生物组的细菌与宿主之间的代谢相互作用。水螅的微生物组比人类的简单得多，只有8-15个分类群。它的主要部分已经被分离出来，可以单独研究，而协议存在，以使水螅息肉与其微生物组的特定部分分离，使模型系统对于研究多细胞宿主中的细菌-细菌和细菌-宿主相互作用非常有价值。我将利用九头蛇的遗传信息和它的相关细菌来重建所有社区成员的代谢模型。通过这些模型，我将预测代谢细菌-细菌和细菌-宿主相互作用。此外，我将模拟环境中营养物质可用性的变化，并评估这如何影响这些相互作用。最后，结果将与16 S测序数据的变化相关，实验改变营养物质的可用性，以确定细菌群落的变化，这是由代谢相互作用的变化引起的。在第一部分中，我将特别关注成对细菌-细菌以及细菌-宿主相互作用及其预测群落动态的能力。其次，我将仔细研究细菌-群落相互作用，以评估一对多相互作用对群落动态的贡献。拟议的研究是高度跨学科的，结合代谢建模，基因组学和宏基因组学等计算工具来研究宿主-微生物组相互作用并描述微生物组的生态功能，最终提供对系统的更全面的了解。总的来说，拟议的研究为了解宿主、微生物组和环境之间的复杂关系提供了一个独特的窗口，有可能了解与人类健康和疾病相关的一般机制。

The human microbiome strongly impacts the physiology and health of its host, and a dysbiosis might have a huge impact on several medical conditions. Therefore, it is highly relevant to understand how a microbiome assembles, reacts to changing environments, and how it can be shaped to ameliorate the effect of dysbiotic bacterial communities. However, the human microbiome is diverse across different individuals and strikingly complex, harboring countless potential higher-order interactions with its host and between bacterial members. It is thus desirable to study the host-associated microbiome in a less complex setup to better track these interactions and identify general mechanisms, which can be transferred to more diverse microbial communities. Here, I propose to use the freshwater polyp Hydra as a model system to study the metabolic interactions between bacteria of its associated microbiome and the host under changing nutritional conditions. The Hydra microbiome is much simpler than the human one consisting of only 8-15 taxa. Major parts of it have been isolated and can be studied separately, while protocols exist to recolonize the Hydra polyp with specific parts of its microbiome, rendering the model system extremely valuable for studying bacterium-bacterium and bacteria-host interactions in a multicellular host. I will use the genetic information of Hydra and its associated bacteria to reconstruct metabolic models of all community members. With these models, I will predict metabolic bacteria-bacteria and bacteria-host interactions. Further, I will simulate changes in the nutrient availability in the environment and assess how this affects these interactions. Finally, the results will be related to changes in 16S sequencing data after an experimental alteration of nutrient availability to identify the variations in the bacterial community, which are caused by the changes in the metabolic interactions. In the first part I will focus specifically on pairwise bacterium-bacterium as well as bacterium-host interactions and their ability to predict community dynamics. Secondly, I will take a closer look at bacterium-community interactions to assess the contribution of one-to-many interactions to community dynamics. The proposed research is highly interdisciplinary, combining computational tools such as metabolic modeling, genomics, and metagenomics to investigate the host-microbiome interactions and describe ecological functions of the microbiome, eventually providing a more comprehensive understanding of the system. Overall, the proposed study offers a unique window into the complex relationships between the host, microbiome, and the environment, with the potential to inform about general mechanisms that are also relevant in human health and disease.