Machine learning methods for the human microbiome

治疗性饮食，如克罗恩病的肠内营养，可以提供非常有效的治疗。然而，它们并不是在所有情况下对所有人都有效。这些饮食的影响将通过肠道微生物组来介导，肠道微生物组是一个参与肠道代谢许多方面的微生物群落，但也通过与免疫系统的相互作用来介导炎症。即使在健康个体中，微生物组也因个体而异，但在IBD患者中表现出更大的变异性。因此，这种变异性很可能会影响治疗效果。横断面研究表明，在肠易激疾病（IBD）如克罗恩病中，某些微生物的丰度会升高或降低。然而，这些都是纯粹的联想观察。在治疗期间跟踪患者的纵向研究具有更大的统计功效。我们将利用大量来自克罗恩病饮食治疗的大规模纵向数据集，这些数据集将揭示微生物组功能能力的宏基因组学数据与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数据联合收割机结合起来。代谢组学与宏基因组学的结合具有很大的潜力，可以将目前的微生物组研究转向了解群落功能和与宿主的相互作用。以前的研究分析了个体的组学数据，在过去十年中开发了许多强大的生物信息学工具，以实现代谢组学和微生物组学分析。另一方面，多组学数据的整合和解释仍然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，这些算法利用多个异构但相互关联的数据源，并在大型和高维数据集上扩展。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，以及受控饮食下代谢组由患者微生物组决定的程度。这些机器学习方法将与肠道代谢途径的统计建模相结合，使用网络上的近似贝叶斯方法（如变分推理或期望传播）拟合两种组学数据源。从这些，我们将推断哪些代谢途径是微生物介导的和重要的治疗。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的患者和对照粪便接种物产生的群落来产生纵向组学数据集。

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.