Abstract
The commensal microbiome is known to influence a variety of host phenotypes. Microbiome profiling followed by differential abundance analysis has been established as an effective approach to study the mechanisms of host-microbiome interactions. However, it is challenging to interpret the collective functions of the resultant microbe-sets due to the lack of well-organized functional characterization of commensal microbiome. We developed microbe-set enrichment analysis (MSEA) to enable the functional interpretation of microbe-sets by examining the statistical significance of their overlaps with annotated groups of microbes that share common attributes such as biological function or phylogenetic similarity. We then constructed microbe-set libraries by query PubMed to find microbe-mammalian gene associations and disease associations by parsing the Disbiome database. To demonstrate the utility of our novel MSEA methodology, we carried out three case studies using publicly available curated knowledge resource and microbiome profiling datasets focusing on human diseases. We found MSEA not only yields consistent findings with the original studies, but also recovers insights about disease mechanisms that are supported by the literature. Overall, MSEA is a useful knowledge-based computational approach to interpret the functions of microbes, which can be integrated with microbiome profiling pipelines to help reveal the underlying mechanism of host-microbiome interactions.
Highlights
The commensal microbiome is known to influence a variety of host phenotypes
We demonstrated the outstanding utility of the microbe-set enrichment analysis (MSEA) methodology by carried out three case studies using publicly available curated knowledge resource and microbiome profiling datasets focusing on human diseases
Since we aim to study the hostmicrobiome interactions to investigate how gut microbial organisms affect host phenotypes via the expression of host genes, we grouped microbes based on their literature-documented associations with mammalian genes
Summary
The commensal microbiome is known to influence a variety of host phenotypes. Microbiome profiling followed by differential abundance analysis has been established as an effective approach to study the mechanisms of host-microbiome interactions. MSEA is a useful knowledge-based computational approach to interpret the functions of microbes, which can be integrated with microbiome profiling pipelines to help reveal the underlying mechanism of hostmicrobiome interactions. As a component of the Human Functional Genomics Project (HFGP), Schirmer et al.[4] found correlation between gut microbial features and production of various types of cytokines in a cohort of 500 healthy adults from the Netherlands They experimentally validated that two microbial metabolites, tryptophol and palmioleic acid, are able to modulate the production of IFNγ and TNFα, respectively, in peripheral blood mononuclear cells. As the knowledge about differential abundance of human microbiome species between healthy and diseases accumulates with the surge of microbiome profiling studies, our understanding of the mechanisms of how microbiome influence human phenotypes are still limited because of the complexity of host-microbe interactions. Themed collections of such microbe-sets can be organized into microbe-set library as a representation of knowledge
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