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. 2019 May 29:9:178.
doi: 10.3389/fcimb.2019.00178. eCollection 2019.

Multi-omics Analysis of Gut Microbiota and Metabolites in Rats With Irritable Bowel Syndrome

Si Liu  1 , Chaozeng Si  2 , Yang Yu  1 , Guiping Zhao  1 , Lei Chen  1 , Yu Zhao  1 , Zheng Zhang  1 , Hengcun Li  1 , Yang Chen  3 , Li Min  1 , Shutian Zhang  1 , Shengtao Zhu  1
Affiliations

Multi-omics Analysis of Gut Microbiota and Metabolites in Rats With Irritable Bowel Syndrome

Si Liu et al. Front Cell Infect Microbiol. .

Abstract

Irritable bowel syndrome (IBS) is a common gastrointestinal dysfunctional disease. The pathophysiology of IBS is, however, largely unknown. This study aimed to determine whether evaluation of fecal metabolite and microbiota profiles may offer an opportunity to identify a novel pathophysiological target for IBS, and to reveal possible gut microbe-metabolite associations. By using gas chromatography coupled to time-of-flight mass spectrometry (GC-TOFMS) and 16S rRNA gene sequencing, we measured fecal metabolites and microbiota of the control and water avoidance stress (WAS)-induced IBS rats. We found a significantly differential metabolite profile between the IBS and control groups; a cluster of metabolites was also found to be significantly associated with the amount of defecations. Moreover, the WAS group exhibited a decreased alpha diversity of the microbial population as compared to the control group. However, the characteristics of gut microbiota could not differentiate the IBS group from the control group. Correlation of the metabolite level with the number of microbial genera showed no significant association between the control and IBS groups. This study provides a global perspective on metabolomics and microbiota profiling in WAS-induced IBS model and a theoretical basis for research on the pathophysiology of IBS.

Keywords: 16S rRNA gene sequencing; correlation analysis; fecal metabolomics profiling; irritable bowel syndrome (IBS); water avoidance stress (WAS).

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Figures

Figure 1
Figure 1
The gut metabolites of IBS rats were significantly different from those of the control group. (A) Plot of OPLS-DA scores of the control (green) and IBS (blue) groups. (B) Variable importance in projection (VIP) score plot for metabolite features identified by OPLS-DA. Metabolites with VIP score >1.0 were considered to be significantly different. (C) The abundance of representative metabolites in the IBS group compared to that in the control group.
Figure 2
Figure 2
Cluster analysis of differential metabolites in IBS rats. (A) The heatmap shows the abundance of metabolites in different clusters from each sample. (B) The heatmap shows the correlations between metabolites and different phenotypes of rats (weight and stools).
Figure 3
Figure 3
The gut microbial analysis of IBS rats. (A) Non-metric multidimensional scaling (NMDS) plot of the control (orange) and the IBS (green) group. (B,C). Alpha diversity of ACE (B) and Chao1(C) indices of the control and IBS groups.
Figure 4
Figure 4
Cluster analysis of gut microbes in IBS rats. (A) The heatmap shows the abundance of microbes in different clusters from each sample. (B) The heatmap shows the correlations between microbes and different phenotypes of IBS rats (weight and stools).
Figure 5
Figure 5
Association analysis of differential gut microbes and metabolites. Correlation matrices between microbial genus abundances and fecal metabolites. The right gradient color band indicates the P-value of correlation. The results of the association of differentially abundant microbes and metabolites showed that the metabolites were significantly correlated with each other and the microbiota was also significantly correlated with each other, but the correlation between the microbes and metabolites was weak.
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