Quantitative microbiome profiling disentangles inflammation- and bile duct obstruction-associated microbiota alterations across PSC/IBD diagnoses

Recent work has highlighted the importance of confounder control in microbiome association studies1,2. For instance, multiple pathologies previously linked to gut ecosystem dysbiosis display concomitant changes in stool consistency3–6, a major covariate of microbiome variation2,7. In those cases, observed microbiota alterations could largely reflect variation in faecal water content. Moreover, stool moisture variation has been linked to fluctuations in faecal microbial load, inducing artefacts in relative abundance profile analyses8,9. Hence, the identification of associations between the gut microbiota and specific disease manifestations in pathologies with complex aetiologies requires a deconfounded, quantitative assessment of microbiome variation. Here, we revisit a disease association microbiome data set comprising 106 patients with primary sclerosing cholangitis (PSC) and/or inflammatory bowel disease10. Assessing quantitative taxon abundances9, we study microbiome alterations beyond symptomatic stool moisture variation. We observe an increased prevalence of a low cell count Bacteroides 2 enterotype across the pathologies studied, with microbial loads correlating inversely with intestinal and systemic inflammation markers. Quantitative analyses allow us to differentiate between taxa associated with either intestinal inflammation severity (Fusobacterium) or cholangitis/biliary obstruction (Enterococcus) among previously suggested PSC marker genera. We identify and validate a near-exclusion pattern between the inflammation-associated Fusobacterium and Veillonella genera, with Fusobacterium detection being restricted to Crohn’s disease and patients with PSC–Crohn’s disease. Overall, through absolute quantification and confounder control, we single out clear-cut microbiome markers associated with pathophysiological manifestations and disease diagnosis. Here, the authors apply quantitative microbiome profiling to a metagenomics data set comprising patients with primary sclerosing cholangitis and/or inflammatory bowel disease and identify microbial taxa associated with inflammation or specific disease indicators, which were validated in an independent inflammatory bowel disease cohort.