Heterogeneity–disease relationship in the human microbiome-associated diseases

• Published in: FEMS microbiology ecology 2020-07, Vol.96 (7), p.1
• Main Author: Ma, Zhanshan (Sam)
• Format: Article
• Language: English
• Subjects: Diabetes mellitus; Disease; Dispersal; Dispersion; Distribution; Ecological effects; Ecology; Gout; Health aspects; Heterogeneity; Microbial populations; Microbiological research; Microbiology; Microbiomes; Microbiota (Symbiotic organisms); Microorganisms; Spatial distribution; Spatial heterogeneity; Statistical analysis
• ISSN: 0168-6496; 1574-6941
• DOI: 10.1093/femsec/fiaa093

ABSTRACT Space is a critical and also challenging frontier in human microbiome research. It has been found that lack of consideration of scales beyond individual and ignoring of microbe dispersal are two crucial roadblocks in preventing deep understanding of the spatial heterogeneity of human microbiome. Assessing and interpreting the heterogeneity and dispersal in microbiomes explicitly are particularly challenging, but implicit approaches such as Taylor's power law (TPL) can be rather effective. Based on TPL, which achieved a rare status of ecological laws, we introduce a general methodology for characterizing the spatial heterogeneity of microbiome (i.e. characterization of microbial spatial distribution) and further apply it for investigating the heterogeneity–disease relationship (HDR) via analyzing a big dataset of 26 MAD (microbiome-associated disease) studies covering nearly all high-profile MADs including obesity, diabetes and gout. It was found that in majority of the MAD cases, the microbiome was sufficiently resilient to endure the disease disturbances. Specifically, in ∼10–16% cases, disease effects were significant—the healthy and diseased cohorts exhibited statistically significant differences in the TPL heterogeneity parameters. We further compared HDR with classic diversity–disease relationship (DDR) and explained their mechanistic differences. Both HDR and DDR cross-verified remarkable resilience of the human microbiomes against MADs. Since assessing and interpreting the heterogeneity and dispersal in microbiomes explicitly are particularly challenging, we introduce an implicit approach, i.e. Taylor's power law, to investigate the heterogeneity–disease relationship in the human microbiome-associated diseases.