Machine learning methods for the human microbiome

克隆氏病的治疗性饮食，如完全的肠内营养，可以提供非常有效的治疗。然而，它们并不是在所有情况下都适用于所有个人。这些饮食的影响将通过肠道微生物群来调节，肠道微生物群是一个多样化的微生物群落，参与肠道代谢的许多方面，但也通过与免疫系统的相互作用而引起炎症。微生物群因人而异，即使在健康的人中也是如此，但在患有IBD的人中表现出更大的变异性。因此，这种变异性极有可能影响治疗效果。横断面研究表明，在克罗恩氏症等肠易激疾病(IBD)中，某些微生物数量增加或减少。然而，这些都是纯粹的关联性观察。在治疗过程中对患者进行跟踪的纵向研究具有更大的统计学影响力。我们将利用克罗恩病饮食治疗中的一些大规模纵向数据集，这些数据集结合了揭示微生物组功能能力的元基因组数据和测量通过宿主或微生物代谢产生的小分子浓度的代谢组学。将代谢组学和元基因组学结合起来，将使当前的微生物组研究向理解群落功能和与宿主的相互作用方向发展具有巨大的潜力。以前的研究分析了个人的组学数据，并在过去十年中开发了许多强大的生物信息学工具，以实现代谢组和微生物组图谱。另一方面，多组学数据的集成和解释仍是一个有待解决的问题。后一种集成需要部署先进的机器学习和统计算法，这些算法利用多个不同但又相互关联的数据源，并在大型和高维数据集上实现这种规模。通过机器学习方法的开发和应用，我们将量化初始宿主微生物组对治疗结果的重要性，以及受控饮食下的代谢组在多大程度上由患者微生物组决定。这些机器学习方法将与肠道新陈代谢途径的统计建模结合起来，使用网络上的近似贝叶斯方法(如变分推理或预期传播)适应两者的组学数据来源。根据这些，我们将推断哪些代谢途径既是微生物介导的，又是治疗的重要途径。这些模型的开发将得到台式实验的帮助，这些实验通过将饮食扰动应用于Quadram研究所人工结肠系统中患者和对照粪便接种产生的群落来生成纵向组学数据集。

Therapeutic diets such as exclusive enteral nutrition for Crohn's disease can provide very effective treatments. However, they do not work for all individuals in all cases. The impact of these diets will be mediated through the gut microbiome, a diverse community of microbes involved in many aspects of gut metabolism but also inflammation through interaction with the immune system. The microbiome varies from one individual to another even in healthy individuals but exhibits substantially more variability in people suffering from IBD. It is highly likely therefore, that this variability impacts treatment efficacy.Cross-sectional studies have shown some organisms to be elevated or decreased in abundance in irritable bowel diseases (IBDs) such as Crohn's. However, these are purely associative observations. Longitudinal studies where patients are followed during treatment have far more statistical power. We will exploit a number of large-scale longitudinal data sets from dietary treatments of Crohn's disease that combine both metagenomics data which reveals the functional capacity of the microbiome together with metabolomics that measures small molecule concentrations produced either through the host or microbial metabolism. Coupling metabolomics with metagenomics has great potential to shift current microbiome research towards understanding community functions and interactions with the host. Previous studies analysed individual 'omics data, with many powerful bioinformatics tools developed over the past decade to enable metabolome and microbiome profiling. On the other hand, multi-omics data integration and interpretation is still a problem to be solved. The latter integration requires the deployment of advanced machine learning and statistical algorithms that leverage multiple heterogeneous, yet interconnected, data sources and that scale on large and high-dimensional datasets. Through the development and application of machine learning approaches we will quantify the importance of the initial host microbiome on treatment outcome but also the extent to which the metabolome under a controlled diet is determined by the patient microbiome. These machine learning methods will be combined with statistical modelling of the metabolic pathways in the gut, fitted to both 'omics data sources using approximate Bayesian methods on networks such as variational inference or expectation-propagation. From these we will infer which metabolic pathways are both microbially mediated and important for treatment. The development of these models will be aided by bench-top experiments generating longitudinal 'omics data sets from applying dietary perturbations to communities generated from patient and control fecal inocula in the artificial colon systems at the Quadram Institute.