MTM1: Network properties of fungal-bacterial interactions: Predictive modeling and functional analysis of the Hawaiian Drosophila gut microbiome

动物如何发育、繁殖和适应环境，不仅取决于其基因组编码的特征，也取决于其微生物组的贡献。在许多微生物群落中，细菌和真菌之间的相互作用对群落性质和寄主生理有着深远的影响。然而，人们对王国之间的相互作用有助于健康的微生物群和促进宿主健康的机制知之甚少。这项建议试图了解细菌-真菌相互作用在夏威夷果蝇(果蝇)寄主生理中的作用。夏威夷苍蝇已经适应了广泛的环境和生活方式。夏威夷果蝇生态的显著多样性，以及在它们肠道中发现的相对简单的细菌和真菌物种的混合，为理解微生物如何帮助宿主动物适应不同的栖息地和饮食提供了一个极好的系统，潜在地催化了新物种的进化。阐明控制微生物-微生物和微生物-宿主相互作用的基本原理对于开发新的抗微生物治疗方法和改进农产品种植具有重要意义。拟议项目的结果将通过确定与微生物有关的策略来改善宿主的健康和生存，从而对保护濒危的夏威夷本土动物(包括果蝇)具有广泛的适用性。夏威夷大学和火奴鲁鲁夏米纳德大学是为少数群体服务的机构，夏威夷原住民学生分别占学生总数的16.5%和19%。让来自服务不足人群的学生学习数据科学和微生物学方法，包括为该项目开发的工具，将增加该地区训练有素的科学家的人数，并鼓励多代夏威夷原住民参与STEM。这一提议的中心假设是，细菌-真菌相互作用通过稳定微生物群和增强宿主代谢过程有利于宿主健康。在计算机网络模型、高通量扩增序列数据和微生物操纵实验中的初步工作表明，i)夏威夷苍蝇肠道中的细菌和真菌成员可能根据它们的共生模式相互作用；ii)真菌功能对宿主健康至关重要。中心假说将通过以下方法解决：首先，将产生计算工具，以允许对微生物相互作用和新出现的群落表型进行强有力的预测，如稳定性、生长和对病原体的恢复能力。其次，细菌和真菌种类将从夏威夷本地果蝇的肠道中分离出来，并单独培养或成对组合培养。将使用代谢组学分析和在正常和压力条件下对社区属性的测量来确定混合社区和纯社区以及有益和有害组合之间的差异。这些数据将用于在Silico模型中进行改进和验证。最后，跨界相互作用对寄主生理的影响将通过用不同的微生物组合接种果蝇并测量对寄主代谢物谱和生活史特征的影响来确定，如生育力、抗逆性和寿命。代谢组数据将被用来确定潜在的微生物-宿主相互作用的候选途径。这一奖项反映了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。

How an animal develops, reproduces, and adapts to its environment depends upon not only the traits encoded by its genome, but, as well, the contributions of its microbiome. In many microbial communities, the interactions between bacteria and fungi have profound effects on community properties and host physiology. However, little is known about the mechanisms by which interactions between kingdoms contribute to a healthy microbiome and promote host fitness. This proposal seeks to understand the role of bacterial-fungal interactions in the host physiology of Drosophila (pomace flies) from the Hawaiian clade. Hawaiian flies have adapted to a wide range of environments and lifestyles. The remarkable diversity of Hawaiian Drosophila ecology together with the relatively simple mix of bacterial and fungal species found in their gut provide an excellent system for understanding how microbes help host animals adapt to diverse habitats and diets, potentially catalyzing the evolution of new species. Elucidating the basic principles that govern microbe-microbe and microbe-host interactions holds promise for the development of novel antimicrobial treatments and improving the cultivation of agricultural products. The findings of the proposed project will have broad applicability to the conservation of endangered native Hawaiian fauna (including Drosophila) by identifying microbe-related strategies that will improve host health and survival. The University of Hawai‘i and Chaminade University of Honolulu are minority-servicing institutions with Native Hawaiian students comprising, respectively, 16.5% and 19% of the student body. Involving students from underserved populations with data science and microbiology methods, including the tools developed for this project, will increase the pool of trained scientists in the region and encourage multi-generational Native Hawaiian engagement with STEM.The central hypothesis of this proposal is that bacterial-fungal interactions benefit host fitness by stabilizing the microbiome and enhancing host metabolic processes. Preliminary work using in silico network modeling, high throughput amplicon sequencing data, and microbial manipulation experiments reveal that i) bacterial and fungal members in the gut of Hawaiian flies are likely to interact based on their patterns of co-occurrence; and ii) fungal function is critical for host fitness. The central hypothesis will be addressed with the following approaches: first, computational tools will be generated to allow robust prediction of microbial interactions and emergent community phenotypes such as stability, growth, and resilience to pathogens. Second, bacterial and fungal species will be isolated from native Hawaiian Drosophila guts and cultured in isolation or in pairwise combinations. Metabolomic profiling and measurements of community properties under normal and stressed conditions will be used to identify differences between mixed and pure communities as well as beneficial and deleterious combinations. The data will be used to refine and validate in silico models. Finally, the effect of interkingdom interactions on host physiology will be determined by inoculating Drosophila with various microbial combinations and measuring the impact on host metabolite profile and life history features such as fertility, stress resistance, and lifespan. Metabolomic data will be used to identify candidate pathways underlying microbial-host interactions.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.