A Metabologenomic Approach Reveals Changes in the Intestinal Environment of Mice Fed on American Diet

Abstract

Intestinal microbiota and their metabolites are strongly associated with host physiology. Developments in DNA sequencing and mass spectrometry technologies have allowed us to obtain additional data that enhance our understanding of the interactions among microbiota, metabolites, and the host. However, the strategies used to analyze these datasets are not yet well developed. Here, we describe an original analytical strategy, metabologenomics, consisting of an integrated analysis of mass spectrometry-based metabolome data and high-throughput-sequencing-based microbiome data. Using this approach, we compared data obtained from C57BL/6J mice fed an American diet (AD), which contained higher amounts of fat and fiber, to those from mice fed control rodent diet. The feces of the AD mice contained higher amounts of butyrate and propionate, and higher relative abundances of Oscillospira and Ruminococcus. The amount of butyrate positively correlated with the abundance of these bacterial genera. Furthermore, integrated analysis of the metabolome data and the predicted metagenomic data from Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) indicated that the abundance of genes associated with butyrate metabolism positively correlated with butyrate amounts. Thus, our metabologenomic approach is expected to provide new insights and understanding of intestinal metabolic dynamics in complex microbial ecosystems.

Protective effects of bifidobacteria against enteropathogens

“…In order to obtain novel knowledge by combining the metabolome and microbiome analysis of the intestinal environment, a multi-omics approach could be a valuable tool for understanding the entire intestinal ecosystem, including the relationships among microbiota, metabolites and host. Additionally, it is considered that a multi-omics approach integrating metagenomics and metabolomics, called metabologenomics, can provide new information about the mechanisms underlying the key roles the microbiota plays in the varying conditions of the intestinal environment (Ishii et al, 2018).…”

Statistical and Machine-Learning Analyses in Nutritional Genomics Studies

“…Similarly, a PCA analysis between different groups of proteins showed that the down-regulation of proteins related to energy metabolism was associated with an increase in the abundance of Bacteroidetes and Firmicutes after an eight week intervention with a cafeteria diet in rats (49% fat of total energy) [ 74 ]. Also, an animal study with an American diet (50% carbohydrate, 15% protein, and 35% fat, for nine weeks) compared to a normal diet (25–30% fat, for nine weeks), demonstrated that the amount butyrate in the stool (the difference between metabolites in two diets measured by PCA) was positively correlated (by correlation test and network analysis) with the abundance of butyrate-producing bacteria, Oscillospira and Ruminococcus (differences between microbiota between two diets by PCoA) [ 77 ]. Furthermore, a greater impact of gut microbiome compared to dietary intake (medium-fat and carbohydrate meal) was found on postprandial lipemia using a PCA analysis [ 70 ].…”

Metaproteomic and Metabolomic Approaches for Characterizing the Gut Microbiome

“…Ishii et al. developed an analytical strategy, termed metabologenomics, which consisted of MS‐based fecal metabolomics, metagenomics, and multi‐omics data integration (e.g., correlation, network analysis, and Procrustes analysis). Upon identification of differentially abundant metabolites, microbial taxa, and the predicted functions in American diet‐fed mice compared to controls, they demonstrated significant correlations between these alterations at different levels and showed that the consumption of high‐fiber diet can benefit the host through modulating the gut bacteria and their metabolites, even with a high‐fat diet.…”

“…performed Procrustes analysis for the principal coordinates analysis (PCoA) of gut metagenome data and PCA of bio‐clinical data for obese individuals before and after dietary intervention, which showed that the structural shifting of the gut microbiota was significantly associated with the improvement of host health with dietary intervention . Procrustes analysis has also been used to compare the fecal metabolome and microbiome which revealed significant inter‐omic correlations Multiple Co‐inertia Analysis.…”

“…The identified differentially abundant bacterial genera, predicted genes, and metabolites can be analyzed using correlation methods. Significant correlations are visualized, clustered in a network graph which can show clusters of bacterial species and metabolites that are positively or negatively correlated with each other, providing a simplified view of the potential microbe–microbe or microbe–metabolite interaction networks in the complex intestinal ecosystem . Maier et al.…”