M²IA: a Web Server for Microbiome and Metabolome Integrative Analysis

Abstract

Background: In the last decade, integrative studies of microbiome and metabolome have experienced exponential growth in understanding their impact on human health and diseases. However, analyzing the resulting multi-omics data and their correlations remains a significant challenge in current studies due to the lack of a comprehensive computational tool to facilitate data integration and interpretation. In this study, we have developed a microbiome and metabolome integrative analysis pipeline (M 2 IA) to meet the urgent needs for tools that effectively integrate microbiome and metabolome data to derive biological insights.Results: M 2 IA streamlines the integrative data analysis between metabolome and microbiome, including both univariate analysis and multivariate modeling methods.KEGG-based functional analysis of microbiome and metabolome are provided to explore their biological interactions within specific metabolic pathways. The functionality of M 2 IA was demonstrated using TwinsUK cohort data with 1116 fecal metabolites and 16s rRNA microbiome from 786 individuals. As a result, we identified two important pathways, i.e., the benzoate degradation and phosphotransferase system, were closely associated with obesity from both metabolome and microbiome.Conclusions: M 2 IA is a public available web server (http://m2ia.met-bioinformatics.cn) with the capability to investigate the complex interplay between microbiome and metabolome, and help users to develop mechanistic hypothesis for nutritional and personalized therapies of diseases.

Lactobacillus acidophilus and L. plantarum improve health status, modulate gut microbiota and innate immune response of marron (Cherax cainii)

“…The beta diversity analysis was performed as nonmetric multidimensional scaling plot (NMDS) using permutational analysis of variance (PERMANOVA) based on Bray-Curtis dissimilarity matrix. Differential abundance at genus level, global similarity, pairwise microbiome-metadata correlations, multivariate regression were performed using LEfSe (Linear Discriminant Analysis Effect Size) and MMCA microbiome pipeline 96,97 . Differentially expressed metabolic pathways in two different groups based on 16S rRNA data were predicted using Piphillin (http://secondgenome.com/ Piphillin) in support of KEGG database (May, 2017 release), BioCyc 21.0 and LEfSe 96,98 .…”