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Abstract:
Historically, bacteria were either seen as pathogenic if they caused disease or benign if they lived commensally with the host. In many cases, however, single organisms have not been identified that are consistently associated with disease, but rather it is thought that bacterial community structure and bacterial interactions differ between healthy and diseased individuals. Investigating the microbiomes of healthy and diseased individuals using systems biology methodology could lead to insight into the processes that underlie dysbiosis. To that end, methodology has been developed to identify co-occurrence networks from microbiome data, but the focus has remained on healthy microbiome datasets, and we still lack an understanding of the common properties of dysbiosis. To address these gaps, we built microbiome co-occurrence networks using 16S rRNA data from ~2,500 individuals from the United Kingdom Adult Twins Registry stratified by health status for 34 immune diseases and 133 quantitative phenotypes. First, we identified disease-associated taxa using generalized and linear mixed models that take into account relatedness between individuals. Next, we built microbiome co-occurrence networks separately for individuals i) with and without disease or ii) from opposite tails of quantitative phenotype distributions. These networks were used to identify community differences across healthy and diseased states, including comparing general network statistics (modularity and diversity), characterizing the properties of disease-associated nodes (degree, betweenness, and closeness centrality), and identifying modules of co-occurring taxa. Using these data, we are conducting one of the first large scale comparisons of microbiome dynamics across healthy and diseased individuals.
