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. 2017 Oct 11;2(5):e00351-17.
doi: 10.1128/mSphere.00351-17. eCollection 2017 Sep-Oct.

High-Fat Diet Changes Fungal Microbiomes and Interkingdom Relationships in the Murine Gut

Timothy Heisel  1 , Emmanuel Montassier  2 , Abigail Johnson  3 , Gabriel Al-Ghalith  4 , Yi-Wei Lin  5 , Li-Na Wei  5 , Dan Knights  3   6 , Cheryl A Gale  1
Affiliations

High-Fat Diet Changes Fungal Microbiomes and Interkingdom Relationships in the Murine Gut

Timothy Heisel et al. mSphere. .

Abstract

Dietary fat intake and shifts in gut bacterial community composition are associated with the development of obesity. To date, characterization of microbiota in lean versus obese subjects has been dominated by studies of gut bacteria. Fungi, recently shown to affect gut inflammation, have received little study for their role in obesity. We sought to determine the effects of high-fat diet on fungal and bacterial community structures in a mouse model using the internal transcribed spacer region 2 (ITS2) of fungal ribosomal DNA (rDNA) and the 16S rRNA genes of bacteria. Mice fed a high-fat diet had significantly different abundances of 19 bacterial and 6 fungal taxa than did mice fed standard chow, with high-fat diet causing similar magnitudes of change in overall fungal and bacterial microbiome structures. We observed strong and complex diet-specific coabundance relationships between intra- and interkingdom microbial pairs and dramatic reductions in the number of coabundance correlations in mice fed a high-fat diet compared to those fed standard chow. Furthermore, predicted microbiome functional modules related to metabolism were significantly less abundant in high-fat-diet-fed than in standard-chow-fed mice. These results suggest a role for fungi and interkingdom interactions in the association between gut microbiomes and obesity. IMPORTANCE Recent research shows that gut microbes are involved in the development of obesity, a growing health problem in developed countries that is linked to increased risk for cardiovascular disease. However, studies showing links between microbes and metabolism have been limited to the analysis of bacteria and have ignored the potential contribution of fungi in metabolic health. This study provides evidence that ingestion of a high-fat diet is associated with changes to the fungal (and bacterial) microbiome in a mouse model. In addition, we find that interkingdom structural and functional relationships exist between fungi and bacteria within the gut and that these are perturbed by high-fat diet.

Keywords: fungal-bacterial interactions; fungi; high-fat diet; microbiome; obesity.

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Figures

FIG 1
FIG 1
Beta-diversity comparisons of fungal communities (a) and bacterial communities (b) of feces from mice fed high-fat and standard diets. PCoA of Bray-Curtis distances is shown for fungi, and weighted (right) and unweighted (left) UniFrac distances are shown for bacteria. The proportion of variance explained by each principal coordinate is denoted in the corresponding axis label.
FIG 2
FIG 2
Procrustes analysis comparing the spatial fit of unweighted UniFrac principal-coordinate matrices of bacterial communities (yellow spheres) and Bray-Curtis principal-coordinate matrices of fungal communities (green spheres) from mice fed high-fat and standard diets. Concordance was observed between bacterial and fungal profile changes in response to diet (M2 = 0.513, P < 0.01).
FIG 3
FIG 3
Abundance plots of sequencing results. (a) Relative abundance plots of bacterial taxa in mouse feces. Taxa were identified to 97% similarity using the Greengenes reference database, as described in Materials and Methods. (b) Relative abundance plots of fungal taxa in mouse feces. Taxa were identified to the species level using the UNITE reference database, as described in Materials and Methods. For the results of statistical comparisons of taxon abundances between diets, see Fig. S2 and S4 in the supplemental material.
FIG 4
FIG 4
Relative abundance plots of fungal taxa for mouse chows. Each chow was sequenced three individual times (DNA was isolated from different pieces of chow for each sequencing run), and results for each chow were pooled after sequencing. Fungi were identified to the species level, as described in Materials and Methods.
FIG 5
FIG 5
Interkingdom interactions between fungi and bacteria. (a) Heat map depicting fungal-bacterial coabundance relationships in mice fed standard chow with the color gradient (scale inset) indicating the strength and direction of the correlation. Red, positive correlation; blue, negative correlation. (b) Network maps of fungal-bacterial interactions in mice fed either standard or high-fat chow. Blue line, positive correlation; gray line, negative correlation. Nodes are positioned using an edge-weighted spring-embedded layout. Color coding is as follows: fungal nodes, red node, Ascomycota phylum; green node, Basidiomycota phylum; bacterial nodes, purple node, Firmicutes phylum; green node, Bacteroidetes phylum; pink node, Deferribacteres phylum; dark blue node, Cyanobacteria phylum; light blue node, Actinobacteria phylum.
FIG 6
FIG 6
Functional microbiome differences between high-fat and standard chow diets. Relative abundance of each KEGG module was predicted with BugBase. KEGG modules displayed are metabolic modules that differ between high-fat and standard chow diets. KEGG modules are shown grouped by KEGG category. Asterisks indicate significant differences (Mann-Whitney U test; ***, FDR-corrected P value of <0.001; mean ± standard error).
FIG 7
FIG 7
Correlations between bacterial KEGG modules and fungal taxa that increase with high-fat (a) and standard chow (b) diet. KEGG modules were limited to those that were significantly different between diet groups for testing. Taxa were limited to those that significantly increased with each dietary treatment as described in Materials and Methods. Positive correlations are depicted in red, and negative correlations are in blue. Asterisks indicate significant correlations (Spearman correlation; *, FDR-corrected P value of <0.05).
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