The unleashed microbiome of cancer patients as a discovery platform for rational microbiome engineering

项目摘要 人类肠道微生物群与一系列疾病有关，并可能积极地 或对治疗的成功产生负面影响。然而，两者之间的因果关系 由于缺乏可行性，人类肠道微生物群和宿主健康很少被弄清楚 在人体上进行受控实验。高分辨率、高频率的时间数据 配对的微生物组和生理测量，以及丰富的潜在元数据 混杂因素，允许应用因果推理框架。这样的数据可以 挖掘影响宿主健康的潜在微生物组驱动因素，特别是如果既有微生物组 和寄主生理在时间进程中受到干扰。我们最近发表了 一个巨大的纵向微生物组，来自遭受严重干扰的癌症患者 他们的免疫系统。同时，这些患者经历了戏剧性的变化 肠道生态系统。我们在此建议解锁这些数据，并构建一个发现平台 微生物组因果关系，走向合理的微生物组工程。我们将开发一种新的 机器学习技术，支持通过有效的方式快速探索我们的数据 可视化和基于Web的界面。我们将开发一种新的方法来识别肠道 常见病原体的微生物竞争物种，它们被系统地忽略 现有的方法，但代表着微生物组最有希望的目标 工程学。最后，我们将阐明人与人之间的双向相互作用 靶向药物和肠道微生物群。为此，我们将利用我们的大数据 一组成对的微生物群和宿主免疫细胞的轨迹。这将验证最近进入的 体外结果表明，人类靶向药物可能会影响肠道生态 利用原位数据，它将识别潜在的肠道微生物群调节 药物动力学。

Project Summary The human gut microbiome is associated with a range of diseases, and may positively or negatively affect the success of therapies. However, the causal directions between the human gut microbiome and host health are seldomly clear due to a lack of feasible controlled experiments in humans. High resolution, high frequency temporal data of paired microbiome and physiological measurement, and rich metadata of potential confounders, allow the application of causal inference frameworks. Such data can be mined for potential microbiome drivers of host health, especially if both the microbiome and host physiology are perturbed during the time courses. We have recently published a vast longitudinal microbiome set from cancer patients undergoing severe perturbation of their immune system. Concurrently, these patients experience dramatic shifts in their gut ecosystem. We here propose to unlock this data and build a discovery platform for microbiome causality, towards rational microbiome engineering. We will develop a new machine-learning technique that enables rapid exploration of our data through effective visualization and a web-based interface. We will develop a new method to identify gut microbial competitor species of common pathogens, which are systematically missed by existing approaches but are representing the most promising targets for microbiome engineering. Finally, we will elucidate the bidirectional interplay between human- targeted medications and the gut microbiome. For this, we will leverage our large data set of paired microbiome and host immune cell trajectories. This will validate recent in vitro results indicating that human-targeted medications may influence gut ecology using in situ data, and it will identify potential gut microbiome modulation of pharmacokinetics.