Dynamics of the Gut Mycobiome in Patients With Ulcerative Colitis

Abstract

Background & aims: Intestinal fungi have been implicated in the pathogenesis of ulcerative colitis (UC). However, it remains unclear if fungal composition is altered during active versus quiescent disease.

Methods: We analyzed clinical and metagenomic data from the Study of a Prospective Adult Research Cohort with Inflammatory Bowel Disease (SPARC IBD), available via the IBD Plexus Program of the Crohn's & Colitis Foundation. We evaluated the fungal composition of fecal samples from 421 patients with UC during clinical activity and remission. Within a longitudinal subcohort (n = 52), we assessed for dynamic taxonomic changes across alterations in clinical activity over time. We examined if fungal amplicon sequence variants and fungal-bacterial relationships were altered during activity versus remission. Finally, we classified activity in UC using a supervised machine learning random forest model trained on fungal abundance data.

Results: During clinical activity, the relative abundance of genus Candida was increased 3.5-fold (P-adj < 1 × 10^-4) compared with during remission. Patients with longitudinal reductions in clinical activity demonstrated parallel reductions in Candida relative abundance (P < .05). Candida relative abundance correlated with Parabacteroides diastonis, Faecalibacterium prausnitzii, and Bacteroides dorei relative abundance (P < .05) during remission; however, these correlations were disrupted during activity. Fungal abundance data successfully classified patients with active or quiescent UC (area under the curve ∼0.80), with Candida relative abundance critical to the success of the model.

Conclusions: Clinical activity in UC is associated with an increased relative abundance of Candida, cross-sectionally and dynamically over time. The role of fecal Candida as a target for therapeutics in UC should be evaluated.

Keywords: Fecal Microbial Transplantation; Fungome; Gut Microbiome.

Fig 1 –

Prevalence and abundance of fungal taxa among patients with UC. (A) Fractional prevalence and total abundance of fungal phyla. X-axis shows the proportion of total samples with detected phylum, y-axis shows the phylum microbial counts. (B) Relative abundance of fungal phyla. X-axis shows relative abundance, y-axis shows fungal phylum name. (C) Fractional prevalence and total abundance of fungal genera. X-axis shows the proportion of samples with detected genus, y-axis shows the total genus microbial counts. (D) Relative abundance of fungal genus. X-axis shows relative abundance, y-axis shows fungal genus name.

Fig 2 –

Differential abundance of fungal genera in clinical activity vs remission among patients with UC. (A) Differential log 2-fold changes in fungal genera among patients in clinical activity vs clinical remission. (B) Relative abundance of highly abundant fungal genera in patients with remission [R] vs activity [A]. (C) Differentially abundant fungal genera, after adjusting for age, sex, and immunosuppressive exposure.

Fig 3 –

Longitudinal relative abundance of Candida across disease activity. Relative abundance of Candida at timepoint 1 and 2 in patients with UC (A) in entire longitudinal cohort (n=52) (B) with no change in clinical status (n=39) (C) with shift in clinical status (n=13) (D) with stable clinical remission (n=33) (E) with clinical activity evolving to clinical remission (n=9) (F) with persistent clinical activity (n=6).

Fig 4 –

Heterogeneity in Candida amplicon sequence variants (ASVs) across patients with UC. (A) Fractional prevalence (x-axis) and total counts (y-axis) of ASVs attributed to Candida genus. (B) Relative abundance of each Candida ASV C) Relative abundance of each Candida ASV during clinical remission vs activity.

Fig 5 -

Classification of clinical activity in ulcerative colitis utilizing a supervised machine learning model trained on fungal taxa abundance data. Y-axis represents the true positive rate while x-axis represents false positive rate, with (A) overall micro-averaging (dark blue squares) and macro-averaging (light blue squares) (B) per class, with prediction of quiescence (pink line) and prediction of activity (black line). (C) Heat map demonstrates the feature importance of each predictor taxa contributing to the model, with light squares demonstrating high sequence counts and dark squares demonstrating low sequence counts.