Topological Data Analysis Highlights Novel Geographical Signatures of the Human Gut Microbiome

Abstract

Background: 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. Methods: 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 tmap, an integrative framework for TDA specifically designed for stratification and enrichment analysis of population-based gut microbiome datasets. Results: 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 Lachnoclostridium and Ruminiclostridium, while France and New Zealand were co-enriched with other, butyrate-producing, taxa of the order Clostridiales. Conclusion: 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.

Keywords: global variation; gut microbiome; human microbiome; population health; topological data analysis (TDA).

FIGURE 1

(A) Persistence Diagram. (B) Adjusting the resolution and overlap parameters of the TDA network to achieve optimal cover ratio. Bv shows the final network, including the three clusters.

FIGURE 2

(A) Network stratification of the countries in the dataset, showing the enriched nodes. Note that Tanzania is not included here, as it did not have any significantly enriched nodes. However, this does not necessarily imply the absence of an effect. (B) Network stratification of the taxa with the most enriched nodes.

FIGURE 3

(A) Heatmap of age: distribution of absolute value across the network on the left, enrichment on the right. (B) Network stratification of sex across the network. (C) Network stratification of BMI across the network.

FIGURE 4

Enrichment heatmaps of three Bacteroidetes genera, absolute abundance across the network on the left, enrichment on the right. (A) Bacteroides. (B) Prevotella. (C) Paludibacter.

FIGURE 5

Enrichment heatmaps of one Bacteroidetes genus and two Firmicute genera, absolute abundance across the network on the left, enrichment on the right. (A) Alistipes, of Bacteroidetes phylum. (B) Lachnoclostridium. (C) Ruminiclostridium.

FIGURE 6

(A) Ranking of host metadata by their SAFE enriched score. (B) Co-enrichment significance values of host metadata and the most enriched taxa, presented as a heatmap. The significance threshold was set at the 0.5^th percentile threshold of all values.