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

最近对肠道微生物组特征的研究大大增加了我们对其组成的了解 以及健康和患病状态下宿主相关细菌群落的丰富性。然而，翻译 这些结果转化为临床信息疗法的速度一直很慢。除了抗生素策略，粪便 微生物区系移植是唯一经过临床验证的恢复微生物组健康的方法。一把钥匙 障碍一直是缺乏对肠道微生物区系如何定植的详细机制了解 胃肠道和使他们成功的特定因素。对当地微生物的详细了解 生物地理学是不可用的，导致微生物区系对健康的贡献只能从批量- 平均数据集。在微米尺度上描绘微生物区系的精确空间分布和异质性 它们与宿主特定细胞类型的特定关联可能有助于更好地理解它们在 胃肠健康和疾病。这项提议旨在开发一种新的方法，通过“空间” “微基因组学”绘制胃肠道微米级微生物生物地理学图，并应用 用于在小鼠模型中理解肠道微生物区系定植的系统。我们假设健康的肠道 微生物区系沿着胃肠道以微米级的特定空间模式组织起来，这些模式反映了 微生物间和宿主-微生物相互作用的强大和动态平衡的基础网络，这是 被特定的环境暴露和宿主因素破坏，导致生物失调和疾病状态。我们 将首先生成将微生物群细胞组封装在其自然共结合状态下的细胞粒子 用于通过下一代测序进行高通量分析。数据的反卷积导致共同定位 为种群在其原生栖息地的空间结构提供信息的网络。我们将描述如何 微生物组生物地理学在小鼠胃肠道不同部位的变化及其意义 对健康状态下饮食变化的反应。然后，我们将探索这些空间变化暴露在 各种抗生素，可能选择性地或广泛地破坏潜在的微生物区系相互作用网络。这些 然后，被破坏的社区将接受粪便微生物区系移植，以及这种影响 将探讨关于建立新的空间微生物区系结构的重新殖民化问题。同时，我们 将开发新陈代谢和基于网络的模型来分析微生物的详细机制 在这些健康和紊乱状态下胃肠道的空间结构。如果成功，这将是 该项目将首次展示肠道微生物区系在健康和混乱中的空间组织 使用无偏见和高通量方法并生成关键数据集和对微生物的洞察 以微米级的分辨率沿胃肠道分布。这些洞察力可能会转化为 成为微生物区系空间组织的新生物标志物，可用于未来的人类队列研究 微生物组研究。

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.