Does microbiome-associated disease affect the inter-subject heterogeneity of human microbiome?

Abstract

Space is a critical and also challenging frontier in human microbiome research. It has been argued that lack of consideration of scales beyond individual and ignoring of microbe dispersal are two crucial roadblocks in preventing deep understanding of the heterogeneity of human microbiome. Assessing and interpreting the spatial distribution (dispersal) of microbes explicitly are particularly challenging, but implicit approaches such as Taylors power law (TPL) can still be effective and offer significant insights into the heterogeneity in abundance and distribution of human microbiomes. Here, we investigate the relationship between human microbiome-associated diseases (MADs) and the inter-subject microbiome heterogeneity, or heterogeneity-disease relationship (HDR), by harnessing the power of TPL extensions and by analyzing a big dataset of 25 MAD studies covering all five major microbiome habitats and majority of the high-profile MADs including obesity and diabetes. Our HDR analysis revealed that in approximately 10%-17% of the cases, disease effects were significant--the healthy and diseased cohorts exhibited statistically significant differences. In majority of the MAD cases, the microbiome was sufficiently resilient to endure the disturbances of MADs. Furthermore, comparative analysis with traditional DDR (diversity-disease relationship) results is presented. We postulate that HDR reveals evolutionary characteristics because it utilizes the TPL parameter that implicitly characterizes spatial behavior (dispersion), which is primarily shaped by microbe-host co-evolution and is more robust against disturbances including diseases, while diversity in DDR analysis is primarily an ecological-scale characteristic and is less robust against diseases. Nevertheless, both HDR and DDR cross-verified remarkable resilience of human microbiomes against MADs.\n\nImportanceIt has been argued that lack of consideration of scales beyond individual and ignoring of microbe dispersal are two crucial roadblocks in preventing deep understanding of the heterogeneity of human microbiome. Assessing and interpreting the spatial distribution (dispersal) of microbes explicitly are particularly challenging, but implicit approaches such as Taylors power law (TPL) can still be effective. Here, we investigate the relationship between human microbiome-associated diseases (MADs) and the inter-subject microbiome heterogeneity, or heterogeneity-disease relationship (HDR), by harnessing the power of TPL extensions and by analyzing a big dataset of 25 MAD studies covering all five major microbiome habitats and majority of the high-profile MADs including obesity and diabetes. We postulate that HDR reveals evolutionary characteristics because it utilizes the TPL parameter that implicitly characterizes spatial behavior (dispersion), which is primarily shaped by microbe-host co-evolution and is more robust against disturbances including diseases than the traditional diversity-disease relationship (DDR).