IBD Parisian Gut Microbiome Cohorte
The human gut microbiome plays a central role in the onset and progression of inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC). However, both conditions are highly heterogeneous across clinical presentation, host biology, and microbial composition, complicating efforts to define consistent microbiome–disease associations. Here, leveraging a cross-sectional cohort of 845 shotgun metagenomic samples with extensive clinical metadata, we perform a fine-grained dissection of microbiome–phenotype relationships in IBD. We found shared and disease- or activity-specific microbial signatures, including a new non-monotonic triphasic signal for some key bacterial taxa. Conventional therapy was associated with worsened IBD dysbiosis, while biologics correlated with partial recovery of disease-depleted commensals. In addition, we built a machine learning model that predicts disease relapse at 1 year. Finally, we assessed IBD-associated strain-level variations, which appeared mainly driven by gene-content diversity. In particular, we identify disease-associated strain-level patterns in Faecalibacterium prausnitzii, well known for its importance in health.
The human gut microbiome plays a central role in the onset and progression of inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC). However, both conditions are highly heterogeneous across clinical presentation, host biology, and microbial composition, complicating efforts to define consistent microbiome–disease associations. Here, leveraging a cross-sectional cohort of 845 shotgun metagenomic samples with extensive clinical metadata, we perform a fine-grained dissection of microbiome–phenotype relationships in IBD. We found shared and disease- or activity-specific microbial signatures, including a new non-monotonic triphasic signal for some key bacterial taxa. Conventional therapy was associated with worsened IBD dysbiosis, while biologics correlated with partial recovery of disease-depleted commensals. In addition, we built a machine learning model that predicts disease relapse at 1 year. Finally, we assessed IBD-associated strain-level variations, which appeared mainly driven by gene-content diversity. In particular, we identify disease-associated strain-level patterns in Faecalibacterium prausnitzii, well known for its importance in health.
Centre de Recherche scientifique Saint-Antoine