Dataset Information

Diversity-disease relationships and shared species analyses for human microbiome-associated diseases.

ABSTRACT: Diversity indices have been routinely computed in the study of human microbiome-associated diseases (MADs). However, it is still unclear whether there is a consistent diversity-disease relationship (DDR) for the human MADs, and whether there are consistent differences in the taxonomic composition of microbiomes sampled from healthy versus diseased individuals. Here we reanalyzed raw data and used a meta-analysis to compare the microbiome diversity and composition of healthy versus diseased individuals in 41 comparisons extracted from 27 previously published studies of human MADs. In the DDR analysis, the average effect size across studies did not differ from zero for a comparison of healthy versus diseased individuals. In 30 of 41 comparisons (73%) there was no significant difference in microbiome diversity of healthy versus diseased individuals, or of different disease classes. For the species composition analysis (shared species analysis), the effect sizes were significantly different from zero. In 33 of 41 comparisons (80%), there were fewer OTUs (operational taxonomic units) shared between healthy and diseased individuals than expected by chance, but with 49% (20 of 41 comparisons) statistically significant. These results imply that the taxonomic composition of disease-associated microbiomes is often distinct from that of healthy individuals. Because species composition changes with disease state, some microbiome OTUs may serve as potential diagnostic indicators of disease. However, the overall species diversity of human microbiomes is not a reliable indicator of disease.

SUBMITTER: Ma ZS

PROVIDER: S-EPMC6775969 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Diversity-disease relationships and shared species analyses for human microbiome-associated diseases.

Ma Zhanshan Sam ZS Li Lianwei L Gotelli Nicholas J NJ

The ISME journal 20190320 8

Diversity indices have been routinely computed in the study of human microbiome-associated diseases (MADs). However, it is still unclear whether there is a consistent diversity-disease relationship (DDR) for the human MADs, and whether there are consistent differences in the taxonomic composition of microbiomes sampled from healthy versus diseased individuals. Here we reanalyzed raw data and used a meta-analysis to compare the microbiome diversity and composition of healthy versus diseased indiv ...[more]

PMID: 30894688

Similar Datasets

Project description:Analysis of human body microbial diversity is fundamental to understanding community structure, biology and ecology. The National Institutes of Health Human Microbiome Project (HMP) has provided an unprecedented opportunity to examine microbial diversity within and across body habitats and individuals through pyrosequencing-based profiling of 16 S rRNA gene sequences (16 S) from habits of the oral, skin, distal gut, and vaginal body regions from over 200 healthy individuals enabling the application of statistical techniques. In this study, two approaches were applied to elucidate the nature and extent of human microbiome diversity. First, bootstrap and parametric curve fitting techniques were evaluated to estimate the maximum number of unique taxa, S(max), and taxa discovery rate for habitats across individuals. Next, our results demonstrated that the variation of diversity within low abundant taxa across habitats and individuals was not sufficiently quantified with standard ecological diversity indices. This impact from low abundant taxa motivated us to introduce a novel rank-based diversity measure, the Tail statistic, ("?"), based on the standard deviation of the rank abundance curve if made symmetric by reflection around the most abundant taxon. Due to ?'s greater sensitivity to low abundant taxa, its application to diversity estimation of taxonomic units using taxonomic dependent and independent methods revealed a greater range of values recovered between individuals versus body habitats, and different patterns of diversity within habitats. The greatest range of ? values within and across individuals was found in stool, which also exhibited the most undiscovered taxa. Oral and skin habitats revealed variable diversity patterns, while vaginal habitats were consistently the least diverse. Collectively, these results demonstrate the importance, and motivate the introduction, of several visualization and analysis methods tuned specifically for next-generation sequence data, further revealing that low abundant taxa serve as an important reservoir of genetic diversity in the human microbiome.

Dataset Information

Diversity-disease relationships and shared species analyses for human microbiome-associated diseases.

Publications

Diversity-disease relationships and shared species analyses for human microbiome-associated diseases.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets