Microbe-set enrichment analysis facilitates functional interpretation of microbiome profiling data

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.

Intratumor Microbiome Analysis Identifies Positive Association Between Megasphaera and Survival of Chinese Patients With Pancreatic Ductal Adenocarcinomas

“…For example, the sphingolipid metabolism pathway was enriched in LTS samples, which might be attributed to Sphingomonas and Enterococcus ; protein processing in the endoplasmic reticulum, which belongs to the genetic information processing pathway, might be attributed to Sphingomonas and Megasphaera ( Figure 3 ). Microbiome set enrichment analysis ( 33 ) was also performed to explore the relationship between differential genera and human diseases. Our results showed that most of the differential genera were involved in tumor immunity pathways, such as IL-17 and TNF signaling ( 34 , 35 ) ( Supplementary Figure 4 ).…”

Total RNA sequencing reveals gene expression and microbial alterations shared by oral pre-malignant lesions and cancer

“…To explore potential associations between differentially abundant microbial genera and the host transcriptome, we performed microbe-set enrichment analysis (MSEA) 24 , whereby the differentially abundant microbes we identified were tested for enrichment against a library of ~1,300 gene-labeled microbe-sets, and the significant microbe-gene associations were visualized in Figure 4 and tabulated in Supplementary Table 5. The union set of genes labeling significantly enriched microbe-sets was in turn tested for enrichment against the Hallmark compendium using hypeR (Supplementary Table 6) The significant genesets overlapping with those found in the host analysis in Figure 2A are also displayed in Figure 4 (center).…”

BugSigDB captures patterns of differential abundance across a broad range of host-associated microbial signatures

“…The properties shared by these clades are often not obvious, but could include common environmental exposures, metabolic or ecological requirements, or physiological characteristics. Although nascent attempts to apply concepts of GSEA to results of microbiome differential abundance analysis exist [15][16][17][18] , major obstacles have prevented their broad utility and adoption. The most significant obstacle has been the lack of comprehensive databases of signatures designed for enrichment analysis, such as those available for GSEA including GO 19 , KEGG 20 , MSigDB 21,22 , and GeneSigDB 23 .…”