A meta-analysis study of the robustness and universality of gut microbiome-metabolome associations.
A meta-analysis study of the robustness and universality of gut microbiome-metabolome associations.
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DOI：
10.1186/s40168-021-01149-z
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发表时间：
2021-10-12
期刊：
影响因子：
15.5
通讯作者：
Borenstein E
中科院分区：
文献类型：
--
作者：
Muller E;Algavi YM;Borenstein E
近年来，人类肠道的微生物组-代谢组学研究越来越受欢迎，主要是由于肠道微生物、代谢物和宿主健康之间相互作用的证据越来越多。基于统计和机器学习的方法已被广泛应用于分析这种配对的微生物组-代谢组数据，以期识别受微生物组组成控制的代谢物。这些代谢物可能会受到基于微生物组的干预措施的调节，为促进肠道代谢健康提供了一条途径。然而，到目前为止，尚不清楚任何单一研究中微生物相关代谢物的发现是否会延续到其他研究或队列，以及微生物组-代谢物联系的可靠性和普遍性如何。在这项研究中，我们通过进行全面的荟萃分析来解决这一挑战，以确定可以根据多项研究中肠道微生物组的组成来预测的人类肠道代谢物。我们将这种代谢物称为“稳健良好预测的”。为此，我们处理了来自10项独立的人类肠道微生物组-代谢组研究的1733个样本的数据，最初侧重于健康受试者，并实施了一个机器学习管道，根据微生物组的组成预测每个数据集中的代谢物水平。通过比较数据集中每种代谢物的可预测性，我们发现了97种预测良好的代谢物。这些包括参与重要微生物途径的代谢物，如胆汁酸转化和多胺代谢。然而，重要的是，其他代谢物在数据集之间的可预测性方面表现出很大的差异，这表明微生物组和代谢物之间存在特定于队列或研究的关系。比较不同模型的分类贡献者，我们发现一些稳健的预测代谢物是由数据集上明显不同的分类群预测的，这表明一些微生物相关的代谢物可能由不同队列中的微生物组的不同成员控制。我们最后检查了在给定研究的对照组上训练的模型是否成功预测了同一研究的疾病组中的代谢物水平，确定了模型不可转移的几种代谢物，表明疾病相关的微生态失调中微生物代谢的转变。结合起来，我们的研究结果提供了对微生物组和代谢物之间联系的更好理解，并允许研究人员将已识别的微生物相关代谢物置于其他研究的背景下。视频摘要在线版本包含补充材料，可在10. 1186/s40168-021-01149-z获得。
Microbiome-metabolome studies of the human gut have been gaining popularity in recent years, mostly due to accumulating evidence of the interplay between gut microbes, metabolites, and host health. Statistical and machine learning-based methods have been widely applied to analyze such paired microbiome-metabolome data, in the hope of identifying metabolites that are governed by the composition of the microbiome. Such metabolites can be likely modulated by microbiome-based interventions, offering a route for promoting gut metabolic health. Yet, to date, it remains unclear whether findings of microbially associated metabolites in any single study carry over to other studies or cohorts, and how robust and universal are microbiome-metabolites links. In this study, we addressed this challenge by performing a comprehensive meta-analysis to identify human gut metabolites that can be predicted based on the composition of the gut microbiome across multiple studies. We term such metabolites “robustly well-predicted”. To this end, we processed data from 1733 samples from 10 independent human gut microbiome-metabolome studies, focusing initially on healthy subjects, and implemented a machine learning pipeline to predict metabolite levels in each dataset based on the composition of the microbiome. Comparing the predictability of each metabolite across datasets, we found 97 robustly well-predicted metabolites. These include metabolites involved in important microbial pathways such as bile acid transformations and polyamines metabolism. Importantly, however, other metabolites exhibited large variation in predictability across datasets, suggesting a cohort- or study-specific relationship between the microbiome and the metabolite. Comparing taxonomic contributors to different models, we found that some robustly well-predicted metabolites were predicted by markedly different sets of taxa across datasets, suggesting that some microbially associated metabolites may be governed by different members of the microbiome in different cohorts. We finally examined whether models trained on a control group of a given study successfully predicted the metabolite’s level in the disease group of the same study, identifying several metabolites where the model was not transferable, indicating a shift in microbial metabolism in disease-associated dysbiosis. Combined, our findings provide a better understanding of the link between the microbiome and metabolites and allow researchers to put identified microbially associated metabolites within the context of other studies. Video abstract The online version contains supplementary material available at 10.1186/s40168-021-01149-z.
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