Consistent alterations in faecal microbiomes of patients with primary sclerosing cholangitis independent of associated colitis

Abstract

Background: Single-centre studies reported alterations of faecal microbiota in patients with primary sclerosing cholangitis (PSC). As regional factors may affect microbial communities, it is unclear if a microbial signature of PSC exists across different geographical regions.

Aim: To identify a robust microbial signature of PSC independent of geography and environmental influences.

Methods: We included 388 individuals (median age, 47 years; range, 15-78) from Germany and Norway in the study, 137 patients with PSC (n = 75 with colitis), 118 with ulcerative colitis (UC) and 133 healthy controls. Faecal microbiomes were analysed by 16S rRNA gene sequencing (V1-V2). Differences in relative abundances of single taxa were subjected to a meta-analysis.

Results: In both cohorts, microbiota composition (beta-diversity) differed between PSC patients and controls (P < 0.001). Random forests classification discriminated PSC patients from controls in both geographical cohorts with an average area under the curve of 0.88. Compared to healthy controls, many new cohort-spanning alterations were identified in PSC, such as an increase of Proteobacteria and the bile-tolerant genus Parabacteroides, which were detected independent from geographical region. Associated colitis only had minor effects on microbiota composition, suggesting that PSC itself drives the faecal microbiota changes observed.

Conclusion: Compared to healthy controls, numerous microbiota alterations are reproducible in PSC patients across geographical regions, clearly pointing towards a microbiota composition that is shaped by the disease itself and not by environmental factors. These reproducibly altered microbial populations might provide future insights into the pathophysiology of PSC.

Figure 1

Violin plots of Shannon‐Index and the unconstrained ordination plots of the Bray‐Curtis dissimilarities for the German (GER) and the Norwegian (NOR) cohort. Ordination was performed on genus‐level abundances subsequently plotted for each cohort separately. Centroids of ellipses are marked by crosses in the respective colours. *P < 0.05; **P < 0.01

Figure 2

Significant and between‐cohort consistent results of differentially abundant taxa in PSC patients and controls. Only taxa with P < 0.05 in each cohort, Q _META < 0.05 and concordant directionality are shown. Taxa from Kummen et al or Sabino et al that could be replicated in both cohorts are marked with a pound (#) symbol. Base‐colours depict the respective cohort (blue: Germany; red: Norwegian) and the combined meta‐analysis result (yellow). Beta‐values larger than zero represent a higher abundance in PSC patients, taxa with beta‐values less than zero are less abundant in PSC patients. Details on the model coefficients and the resulting P‐values in the cohorts and the meta‐analysis can be found in S1‐S3. *P < 0.05; **P < 0.01; ***P < 0.001

Figure 3

Robust results of the logistic regression within cohorts and the meta‐analysis testing for differential prevalence of taxonomic groups in PSC patients and healthy controls. Only taxa with P < 0.05 in each cohort, Q _META < 0.05 and concordant effect direction are shown. Colour saturation expresses the effect size (Beta) of the association. Beta‐values larger than zero (red boxes) represent a higher prevalence in PSC patients, taxa with values less than zero (blue) are less prevalent in PSC patients. Details on the model coefficients and the resulting P‐values in the cohorts and the meta‐analysis can be found in Table S4. p: phylum, c: class, o: order, f: family, g: genus

Figure 4

Receiver operating characteristic curve of random forest classification PSC vs controls across cohorts. Displayed are (A) the 0.632 bootstrap results from the pooled German and Norwegian cohort, (B) the classifier trained on the German cohort and validated on the Norwegian cohort and (C) vice versa. Features included in the model were the taxa with robust differential distribution between PSC and controls. (D) Feature importance of the respective taxa in the pooled classifier was ranked by Gini index