Micron-scale Spatial Metagenomic Mapping of Microbial Biogeography in the Gastrointestinal Tract

最近对肠道微生物组进行表征的努力显着增加了我们对组成的了解 以及健康和患病状态下宿主相关细菌群落的丰度。然而，翻译 将这些结果转化为临床信息疗法的进展缓慢。除了抗生素策略，粪便 微生物群移植是恢复微生物群健康的唯一其他经临床验证的方法。一个关键 障碍一直是缺乏详细的机制了解肠道微生物群如何殖民 胃肠道和特定的因素，使他们的成功。详细了解当地微生物 由于没有微生物地理学资料，因此只能推断出大量微生物对健康的贡献- 平均数据集。在微米尺度上描绘微生物群的精确空间分布和异质性 并且它们与宿主特异性细胞类型的特异性关联可能导致对其在以下方面的作用的更好理解： 胃肠道健康和疾病。该提案旨在通过“空间”开发一种新的方法， 宏基因组学”绘制沿着胃肠道的微米级微生物微生物学图谱，并应用 用于理解小鼠模型中肠道微生物群定殖的系统。我们假设健康的肠道 微生物群沿着胃肠道以微米尺度沿着以限定的空间模式组织， 微生物间和宿主-微生物相互作用的潜在的稳健和稳态网络， 受到特定环境暴露和宿主因素的破坏，导致生态失调和疾病状态。我们 将首先产生细胞颗粒， 用于下一代测序的高通量分析。数据的反卷积导致共定位 网络，告知人口在其原生栖息地的空间结构。我们将描述 沿着小鼠胃肠道的不同部分的微生物组学变化及其 对健康状态下饮食变化的反应。然后，我们将探讨这些空间变化后，暴露于 可以选择性地或广泛地破坏潜在的微生物群相互作用网络的各种抗生素。这些 然后，被破坏的社区将接受粪便微生物群移植， 将探索建立新的空间微生物群架构的方法。同时，我们 将开发基于代谢和网络的模型，以分析微生物的详细机制， 在这些健康和破坏状态下胃肠道的空间结构。如果成功，这 该项目将首次展示肠道微生物群在健康和破坏的空间组织 使用无偏和高通量的方法，并生成关键数据集和对微生物的见解 分布沿着胃肠道在微米级分辨率。这些见解可能会转化为 用于微生物群空间组织的新型生物标志物，可用于未来研究人类队列 微生物研究

Recent efforts to characterize the gut microbiome have significantly increased our knowledge of the composition and abundance of host-associated bacteria communities in healthy and diseased states. However, translation of these results into clinically informative therapeutics has been slow. Beyond antibiotic strategies, fecal microbiota transplantation is the only other clinically validated approach to restore microbiome health. A key roadblock has been the lack of detailed mechanistic understanding for how gut microbiota colonize the gastrointestinal tract and specific factors that enable their success. Detailed understanding of local microbial biogeography are not available, leading to inference of contribution of microbiota on health only from bulk- averaged datasets. Delineating the precise spatial distribution and heterogeneity of microbiota at a micron-scale and their specific association with host-specific cell types may lead to improved understanding of their role in gastrointestinal health and disease. This proposal aims to develop a new approach through “spatial metagenomics” to map the micron-scale microbial biogeography along the gastrointestinal tract and apply the system for understanding gut microbiota colonization in a murine model. We hypothesize that the healthy gut microbiota are organized in defined spatial patterns at the micron-scale along the gastrointestinal tract that reflect an underlying robust and homeostatic network of inter-microbial and host-microbial interactions, which is disrupted by specific environmental exposures and host factors that lead to dysbiosis and diseased states. We will first generate cell particles that encapsulate groups of microbiota cells in their native co-association states for high-throughput profiling by next-generation sequencing. Deconvolution of the data results in co-localization networks that inform the spatial architecture of the population in their native habitat. We will characterize how the microbiome biogeography changes along different parts of the murine gastrointestinal tract and their responses to dietary changes in healthy states. Then, we will probe these spatial changes upon exposure to various antibiotics that may selectively or broadly disrupt the underlying microbiota interaction network. These disrupted communities will then be subjected to fecal microbiota transplantation, and the effects of such recolonization on the establishment of new spatial microbiota architectures will be explored. Concurrently, we will develop metabolic and network-based models to analyze the detailed mechanisms that underlie microbial spatial architectures in the gastrointestinal tract under these healthy and disrupted states. If successful, this project will demonstrate for the first time the spatial organization of the gut microbiota in health and disrupted states using an unbiased and high-throughput method and generate key datasets and insights into the microbial distribution along the gastrointestinal tract at a micron-scale resolution. These insights can potentially translate into novel biomarkers for microbiota spatial organization that can be applied to study human cohorts in future microbiome studies.