Yang Sun, Lianwei Li, Aiyun Lai, Wanmeng Xiao, Kunhua Wang, Lan Wang, Junkun Niu, Juan Luo, Hongju Chen, Lin Dai, Yinglei Miao
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引用次数: 2
Abstract
The dysbiosis of the gut microbiome associated with ulcerative colitis (UC) has been extensively studied in recent years. However, the question of whether UC influences the spatial heterogeneity of the human gut mucosal microbiome has not been addressed. Spatial heterogeneity (specifically, the inter-individual heterogeneity in microbial species abundances) is one of the most important characterizations at both population and community scales, and can be assessed and interpreted by Taylor's power law (TPL) and its community-scale extensions (TPLEs). Due to the high mobility of microbes, it is difficult to investigate their spatial heterogeneity explicitly; however, TPLE offers an effective approach to implicitly analyze the microbial communities. Here, we investigated the influence of UC on the spatial heterogeneity of the gut microbiome with intestinal mucosal microbiome samples collected from 28 UC patients and healthy controls. Specifically, we applied Type-I TPLE for measuring community spatial heterogeneity and Type-III TPLE for measuring mixed-species population heterogeneity to evaluate the heterogeneity changes of the mucosal microbiome induced by UC at both the community and species scales. We further used permutation test to determine the possible differences between UC patients and healthy controls in heterogeneity scaling parameters. Results showed that UC did not significantly influence gut mucosal microbiome heterogeneity at either the community or mixed-species levels. These findings demonstrated significant resilience of the human gut microbiome and confirmed a prediction of TPLE: that the inter-subject heterogeneity scaling parameter of the gut microbiome is an intrinsic property to humans, invariant with UC disease.
期刊介绍:
Evolutionary Bioinformatics is an open access, peer reviewed international journal focusing on evolutionary bioinformatics. The journal aims to support understanding of organismal form and function through use of molecular, genetic, genomic and proteomic data by giving due consideration to its evolutionary context.