Gut bacteria-host metabolic interplay during conventionalisation of the mouse germfree colon

Abstract

The interplay between dietary nutrients, gut microbiota and mammalian host tissues of the gastrointestinal tract is recognised as highly relevant for host health. Combined transcriptome, metabonome and microbial profiling tools were employed to analyse the dynamic responses of germfree mouse colonic mucosa to colonisation by normal mouse microbiota (conventionalisation) at different time-points during 16 days. The colonising microbiota showed a shift from early (days 1 and 2) to later colonisers (days 8 and 16). The dynamic changes in the microbial community were rapidly reflected by the urine metabolic profiles (day 1) and at later stages (day 4 onward) by the colon mucosa transcriptome and metabolic profiles. Correlations of host transcriptomes, metabolite patterns and microbiota composition revealed associations between Bacilli and Proteobacteria, and differential expression of host genes involved in energy and anabolic metabolism. Differential gene expression correlated with scyllo- and myo-inositol, glutamine, glycine and alanine levels in colonic tissues during the time span of conventionalisation. Our combined time-resolved analyses may help to expand the understanding of host-microbe molecular interactions during the microbial establishment.

Figure 1

Dynamics of the colonising microbiota in the colon. (a) Hierarchical clustering analysis of MITChip fingerprints generated from the inoculum and colonic samples collected from days 1–16 post-conventionalisation (n=5-6 mice/time-point). The highest phylogenetic assignments of probe specificity are provided at the right side of the gel-view dendogram. (b) Dynamics of the relative contribution of different level 1 (class like) microbial groups to the total microbiota in the colon of mice at different time-points post-conventionalisation.

Figure 2

Dynamics of specific functional gene abundance in the microbiota. (a) Quantification of butyrate producers and sulphate reducers expressed as mean±s.d. log10 number of Butyryl-CoA-transferase (●) and dsr (♦) genes/g content, respectively. Statistical analysis was performed using a one-way analysis of variance test executed in SPSS Statistics 17.0 (SPSS Inc., Chicago, IL, USA). Significant differences between time-points are indicated by distinct characters above the measurement groups (P<0.05). (b) HPLC analysis of the large intestinal content for SCFAs including acetate, butyrate, propionate, lactate and succinate, (n=5-6 mice/time-point).

Figure 3

Altered colonic metabolites during conventionalisation. (a) Metabolic pathway map including the genes that participate in certain metabolic pathways conversions. Genes are indicated in box symbols; each box is divided into five sub-boxes with colour codes, representing the changes observed at days 1, 2, 4, 8 and 16 post-conventionalisation, respectively. Direct and indirect interactions are depicted by solid and dashed arrows respectively, (n=6 mice/time-point). Predicted altered metabolites are indicated with orange and purple arrows. (b) Heat map summarising the colonic metabolic variation during conventionalisation. A series of pair-wise OPLS-DA models were constructed for colon tissue. (†) refers to tentative assignment.

Figure 4

(a) Heat map summarising the urine metabolic variation during conventionalisation. A series of pair-wise OPLS-DA models were constructed for urine metabolite profiles with significant modulation during the days (d) 1–16 post-conventionalisation period, compared to germfree. (†) refers to tentative assignment. Purple arrows refer to metabolites as predicted from the projections of differentially expressed metabolic genes on KEGG maps. Orange asterisks refer to metabolites that have been previously reported to be exclusively produced by microbial metabolism. (b) Correlation heat map between urine metabolites and the bacterial taxonomical level from MITChip data (level 2-genera like) showing the association of early colonisers and urine metabolites.

Figure 5

Microbiota–metabolite–transcriptome correlation. Correlation heat maps showing: Model (1) correlation computed between colonic tissue transcriptome (shown in Supplementary Figure 3A), Bacilli and scyllo- and myo-inositol tissue metabolites (a) (positive correlation). Model (2) correlation computed between colonic tissue transcriptome (shown in Supplementary Figure 3B), glutamate, alanine and glycine tissue metabolites and epsilonproteobacteria, (b) and alphaproteobacteria (c) (positive correlation).

Figure 6

A schematic model summarising how different microbial taxa and their metabolites influence local (tissue metabolites and transcriptomes) and systemic (urine metabolites) metabolism in the time-resolved ecosystems biology approach applied in this study. Blue arrows indicate the O2-PLS based statistical correlations between microbiota, transcriptome and metabolites. Induced pathways and higher level metabolites are indicated in red and repressed pathways and lower level metabolites are indicated in green (in comparison to the germfree mice).