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. 2017 Jun;66(6):1039-1048.
doi: 10.1136/gutjnl-2015-310746. Epub 2016 Feb 3.

Fungal microbiota dysbiosis in IBD

Harry Sokol  1   2   3   4 , Valentin Leducq  1   4 , Hugues Aschard  5 , Hang-Phuong Pham  6 , Sarah Jegou  1   4 , Cecilia Landman  3   4 , David Cohen  1   2 , Giuseppina Liguori  7 , Anne Bourrier  3   4 , Isabelle Nion-Larmurier  3   4 , Jacques Cosnes  3   4 , Philippe Seksik  3   4 , Philippe Langella  2   4 , David Skurnik  8   9 , Mathias L Richard  2   4 , Laurent Beaugerie  3   4
Affiliations

Fungal microbiota dysbiosis in IBD

Harry Sokol et al. Gut. 2017 Jun.

Abstract

Objective: The bacterial intestinal microbiota plays major roles in human physiology and IBDs. Although some data suggest a role of the fungal microbiota in IBD pathogenesis, the available data are scarce. The aim of our study was to characterise the faecal fungal microbiota in patients with IBD.

Design: Bacterial and fungal composition of the faecal microbiota of 235 patients with IBD and 38 healthy subjects (HS) was determined using 16S and ITS2 sequencing, respectively. The obtained sequences were analysed using the Qiime pipeline to assess composition and diversity. Bacterial and fungal taxa associated with clinical parameters were identified using multivariate association with linear models. Correlation between bacterial and fungal microbiota was investigated using Spearman's test and distance correlation.

Results: We observed that fungal microbiota is skewed in IBD, with an increased Basidiomycota/Ascomycota ratio, a decreased proportion of Saccharomyces cerevisiae and an increased proportion of Candida albicans compared with HS. We also identified disease-specific alterations in diversity, indicating that a Crohn's disease-specific gut environment may favour fungi at the expense of bacteria. The concomitant analysis of bacterial and fungal microbiota showed a dense and homogenous correlation network in HS but a dramatically unbalanced network in IBD, suggesting the existence of disease-specific inter-kingdom alterations.

Conclusions: Besides bacterial dysbiosis, our study identifies a distinct fungal microbiota dysbiosis in IBD characterised by alterations in biodiversity and composition. Moreover, we unravel here disease-specific inter-kingdom network alterations in IBD, suggesting that, beyond bacteria, fungi might also play a role in IBD pathogenesis.

Keywords: INFLAMMATORY BOWEL DISEASE.

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Conflict of interest statement

Competing interests: None declared.

Figures

Figure 1
Figure 1
Altered bacterial microbiota biodiversity and composition in IBD. (A and B) Beta diversity. Principal coordinate analysis of Bray–Curtis distance with each sample coloured according to the disease phenotype. PC1, PC2 and PC3 represent the top three principal coordinates that captured most of the diversity. The fraction of diversity captured by the coordinate is given as a percentage. Groups were compared using Permanova method. (C and D) Observed species number describing the alpha diversity of the bacterial microbiota in the various groups studied (Kruskal–Wallis test with Dunn's multiple comparison test). (E and F) Global composition of bacterial microbiota at the phyla and family levels. Healthy subjects (HS) and patient subgroups are labelled on the x-axis and expressed as the relative operational taxonomic unit (OTUs) abundance for each group. In all panels: *p<0.05; **p<0.01; ***p<0.001. CD, Crohn's disease.
Figure 2
Figure 2
Altered fungal microbiota biodiversity and composition in IBD. (A and B) Beta diversity. Principal coordinate analysis of Bray–Curtis distance with each sample coloured according to the disease phenotype. PC1, PC2 and PC3 represent the top three principal coordinates that captured most of the diversity. The fraction of diversity captured by the coordinate is given as a percentage. Groups were compared using Permanova method. (C) Observed species number describing the alpha diversity of the fungal microbiota in the various groups studied. (D) ITS2/16S observed species ratio (Kruskal–Wallis test with Dunn's multiple comparison test. (E and F) Global composition of fungal microbiota at the phyla and genus levels. Healthy subjects (HS) and patient subgroups are labelled on the x-axis and expressed as relative OTUs abundance for each group. In all panels: *p<0.05; **p<0.01; ***p<0.001. CD, Crohn's disease.
Figure 3
Figure 3
Bacterial and fungal taxa associated with IBD. (A and B) Differences in abundance are shown for the bacterial and fungal taxa detected using a multivariate statistical approach (see ‘Material and Methods’). The fold change for each taxon was calculated by dividing the mean abundance in the cases by that of the controls. The number of subjects that have any presence (>0) of the indicated taxon is indicated in brackets, and taxon with a mean abundance of >0.5% in at least one of the groups is indicated with ‘#’. (C) Absolute Saccharomyces cerevisiae abundance in the faecal microbiota quantified using qRT-PCR (mean±SEM). (D) Relative proportion of Candida albicans calculated by subtracting the log number of C. albicans from the log number of all fungi. (E) Absolute C. albicans abundance in the faecal microbiota quantified using qRT-PCR (mean±SEM). (F) Basidiomycota/Ascomycota relative abundance ratio in the various groups studied (Kruskal–Wallis test with Dunn's multiple comparison test, *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001). CD, Crohn's disease; HS, healthy subjects.
Figure 4
Figure 4
Saccharomyces cerevisiae and Candida albicans induces distinct dendritic cell response. Interleukin (IL)10 (A) and IL6 (B) secretion by mouse bone marrow-derived dendritic cells following stimulation with S. cerevisiae and C. albicans (mean±SEM). The numbers of mice per experiment are n=5–15 (Mann–Whitney U test). KO, knockout; WT, wildtype.
Figure 5
Figure 5
Specific bacteria–fungi correlation pattern in IBD. Distance correlation plots of the relative abundance of fungi and bacteria genera. Healthy subjects (HS) (left), UC (middle) and Crohn's disease (CD) (right). Statistical significance was determined for all pairwise comparisons; only significant correlations (p value <0.05 after false discovery rate correction) are displayed. Positive values (blue squares) indicate positive correlations, and negative values (red squares) indicate inverse correlations. The shading of the square indicates the magnitude of the association; darker shades are more strongly associated than lighter shades. The sign of the correlation was determined using Spearman's method.
Figure 6
Figure 6
Imbalance trans-kingdom network in IBD. Correlation network in healthy subjects (A), Crohn's disease (B) and UC (C) generated using Cytoscape. Each circle (node) represents a microbial genus, its colour represents the bacterial or fungal phylum it belongs to and its size represents the number of direct edges that it has. The edge colour indicates the magnitude of the distance correlation; green indicates positive correlation and red indicates negative correlation (determined using spearman test). Statistical significance was determined for all pairwise comparisons; only significant correlations (p value <0.05 after false discovery rate correction) are displayed.
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