Lymberopoulos Eva, Gentili Giorgia Isabella, Alomari Muhannad, Sharma Nikhil
Department of Clinical and Movement Neurosciences, Institute of Neurology, University College London, London, United Kingdom.
CDT AI-Enabled Healthcare Systems, Institute of Health Informatics, University College London, London, United Kingdom.
Front Artif Intell. 2021 Aug 18;4:680564. doi: 10.3389/frai.2021.680564. eCollection 2021.
There is growing interest in the connection between the gut microbiome and human health and disease. Conventional approaches to analyse microbiome data typically entail dimensionality reduction and assume linearity of the observed relationships, however, the microbiome is a highly complex ecosystem marked by non-linear relationships. In this study, we use topological data analysis (TDA) to explore differences and similarities between the gut microbiome across several countries. We used curated adult microbiome data at the genus level from the GMrepo database. The dataset contains OTU and demographical data of over 4,400 samples from 19 studies, spanning 12 countries. We analysed the data with , an integrative framework for TDA specifically designed for stratification and enrichment analysis of population-based gut microbiome datasets. We find associations between specific microbial genera and groups of countries. Specifically, both the USA and UK were significantly co-enriched with the proinflammatory genera and , while France and New Zealand were co-enriched with other, butyrate-producing, taxa of the order Clostridiales. The TDA approach demonstrates the overlap and distinctions of microbiome composition between and within countries. This yields unique insights into complex associations in the dataset, a finding not possible with conventional approaches. It highlights the potential utility of TDA as a complementary tool in microbiome research, particularly for large population-scale datasets, and suggests further analysis on the effects of diet and other regionally varying factors.
肠道微生物群与人类健康和疾病之间的联系正受到越来越多的关注。分析微生物群数据的传统方法通常需要进行降维,并假设观察到的关系呈线性,然而,微生物群是一个以非线性关系为特征的高度复杂的生态系统。在本研究中,我们使用拓扑数据分析(TDA)来探索多个国家肠道微生物群之间的差异和相似性。我们使用了来自GMrepo数据库的经过整理的属水平成人微生物群数据。该数据集包含来自19项研究的4400多个样本的OTU和人口统计学数据,涵盖12个国家。我们使用,一个专门为基于人群的肠道微生物群数据集的分层和富集分析设计的TDA综合框架来分析数据。我们发现特定微生物属与国家组之间存在关联。具体而言,美国和英国都显著共同富集了促炎属和,而法国和新西兰则共同富集了其他梭菌目产生丁酸盐的分类群。TDA方法展示了不同国家之间以及国家内部微生物群组成的重叠和差异。这为数据集中的复杂关联提供了独特的见解,这是传统方法无法实现的发现。它突出了TDA作为微生物群研究中一种补充工具的潜在效用,特别是对于大规模人群数据集,并建议进一步分析饮食和其他区域变化因素的影响。