Fecal Mycobiota Combined With Host Immune Factors Distinguish Clostridioides difficile Infection From Asymptomatic Carriage

Abstract

Background & aims: Although the role of gut microbiota in Clostridioides difficile infection (CDI) has been well established, little is known about the role of mycobiota in CDI. Here, we performed mycobiome data analysis in a well-characterized human cohort to evaluate the potential of using gut mycobiota features for CDI diagnosis.

Methods: Stool samples were collected from 118 hospital patients, divided into 3 groups: CDI (n = 58), asymptomatic carriers (Carrier, n = 28), and Control (n = 32). The nuclear ribosomal DNA internal transcribed spacer 2 was sequenced using the Illumina HiSeq platform to assess the fungal composition. Downstream statistical analyses (including Alpha diversity analysis, ordination analysis, differential abundance analysis, fungal correlation network analysis, and classification analysis) were then performed.

Results: Significant differences were observed in alpha and beta diversity between patients with CDI and Carrier (P < .05). Differential abundance analysis identified 2 genera (Cladosporium and Aspergillus) enriched in Carrier. The ratio of Ascomycota to Basidiomycota was dramatically higher in patients with CDI than in Carrier and Control (P < .05). Correlations between host immune factors and mycobiota features were weaker in patients with CDI than in Carrier. Using 4 fungal operational taxonomic units combined with 6 host immune markers in the random forest classifier can achieve very high performance (area under the curve ∼92.38%) in distinguishing patients with CDI from Carrier.

Conclusions: Our study provides specific markers of stool fungi combined with host immune factors to distinguish patients with CDI from Carrier. It highlights the importance of gut mycobiome in CDI, which may have been underestimated. Further studies on the diagnostic applications and therapeutic potentials of these findings are warranted.

Keywords: C difficile; Diagnostics; Gut Mycobiome; Immune Response.

Figure 1.. Alpha and beta diversities, and ordination analysis of the gut mycobiota with three distinct phenotypes: Control, Carrier and CDI.

A,B: The alpha diversity analysis was based on: Chao1 index (A) and Shannon index (B). C,D: Principle Coordinate Analysis (PCoA) of the fungal compositions at the operational taxonomic unit (OTU) level based on the Bray-Curtis dissimilarity (C) and unweighted UniFrac distance (D). The ellipses represent the 95% of the samples belonging to each group. Dissimilarity was analyzed using Adonis statistical tests with 999 permutations based on Bray-Curtis dissimilarity: CDI vs Carrier (R² = 0.0299, P = .032), CDI vs Control (R² = 0.0121, P = .337) and Carrier vs Control (R² = 0.0147, P = .491). Similar analysis based on unweighted UniFrac distance yielded: CDI vs Carrier (R² = 0.0363, P = .001), CDI vs Control (R² = 0.0361, P = .001) and Carrier vs Control (R² = 0.0118, P = .957). E,F: The beta diversity analysis was based on Bray-Curtis dissimilarity (E) and unweighted UniFrac distance (F). ns: P > .05, *P < .05, **P < .01, ***P < .001.

Figure 2.. Genus-level taxonomic profiles of the gut mycobiota from three distinct phenotypes: Control, Carrier and CDI.

Only genera with ≥ 1% abundance in at least one sample were depicted. Otherwise, they were included in the category “others”.

Figure 3.. The relative abundance and ratio of Ascomycota to Basidiomycota of gut mycobiota from three distinct phenotypes: Control, Carrier and CDI.

(A) Ascomycota, (B) Basidiomycota, (C) Ascomycota to Basidiomycota ratio. Data are presented as median and 95% CI with P values based on Wilcoxon rank sum test. ns: P > .05, *P < .05, **P < .01, ***P < .001.

Figure 4.. Differentially abundant fungal taxa among the three phenotypical groups: Control, Carrier and CDI.

Differentially abundant genera (A-B) and OTUs (C-F) were found using ANCOM. Note that for a taxon that is absent in most subjects, the interquartile range (difference between first quartile and third quartile) will be extremely small. ns: P > .05, *P < .05.

Figure 5.. Fungal correlation networks of the three phenotypical groups: Control (A), Carrier (B) and CDI (C).

Nodes represent genera and are colored based on their phylum. Edges represent fungal correlations: green/red means positive/negative correlations, respectively. Edge thickness indicates the absolute value of correlation coefficient, and only the high confidence interactions (P < .05) with high absolute correlation coefficients (> 0.1) were presented.

Figure 6.. Classification analyses based on random forest models.

A,C: CDI vs. Carrier. B,D: CDI vs. Control. For each classification analysis, we tried different types of features: best OTU, best immune factor, all OTUs, all immune factors, all OTUs and immune factors. The receiver operating characteristic (ROC) curves were shown in A and B. The top features ranked based on their mean decrease accuracy were shown in C and D. The lengths of the bars in the histogram represent the mean decrease accuracy, which indicates the importance of features (OTUs and immune factors) for classification. OTU657: Aspergillus_proliferans, OTU35: unclassified fungi, OTU252: unclassified_g_Cladosporium, OTU486: unclassified_o_Pleosporales, OTU584: unclassified_g_Aspergillus.

Figure 7.. Spearman correlations between fungal abundances in stool samples and the circulating levels of host immune markers in serum samples from the three phenotypic groups: Control (A), Carrier (B) and CDI (C).

For each heat map, rows correspond to fungal taxa at the genus level, columns correspond to immune factors. Red and blue represents the positive and negative correlations, respectively. The intensity of the colors denotes the degree of correlation between the genera abundances and the circulating levels of host serum immune factors. *P < .05, **P < .01, ***P < .001.