DOP46 Mucosal host-microbe interactions associate with clinical phenotypes in Inflammatory Bowel Disease

Bourgonje, A.(1)*;Hu, S.(1);Gacesa, R.(1);Jansen, B.H.(1);Björk, J.R.(1);Bangma, A.(1);Hidding, I.J.(1);van Dullemen, H.M.(1);Visschedijk, M.C.(1);Faber, K.N.(1);Dijkstra, G.(1);Harmsen, H.J.M.(2);Festen, E.A.M.(1);Vich Vila, A.(1);Spekhorst, L.M.(1);Weersma, R.K.(1);

(1)University of Groningen- University Medical Center Groningen, Gastroenterology and Hepatology, Groningen, The Netherlands;(2)University of Groningen- University Medical Center Groningen, Medical Microbiology, Groningen, The Netherlands;

Background

Host intestinal immune gene signatures and microbial dysregulations expose potential mechanisms in the pathogenesis of inflammatory bowel diseases (IBD). Profiling of mucosa-attached microbiota allows the understanding of locally present microbial communities and their immediate impact on the host. This study aimed to comprehensively examine interactions between host mucosal gene expression and mucosal microbiota in patients with IBD.

Methods

Intestinal mucosal RNA-sequencing data was combined with mucosal 16S rRNA gene sequencing data from 696 intestinal biopsies derived from 337 patients with IBD (181 with Crohn’s disease [CD] and 156 with ulcerative colitis [UC]) and 16 non-IBD controls (Fig. 1). Mucosal gene expression and bacterial abundances were systematically analyzed in relation to the presence of inflammation, Montreal disease classification, medication use (e.g. TNF-α-antagonists) and dysbiotic status. Pathway-based clustering and network analysis (Sparse-CCA and centrLCC analysis) and individual pairwise gene–taxa associations were investigated to identify host–microbiota interactions in different clinical contexts. Subsequently, the contribution of microbiota to variation in intestinal cell type–enrichment was analyzed. To confirm the key findings, we used publicly available mucosal 16S and RNA-seq datasets for external validation.

Results

In total, 1,141 inflammation-specific genes and 131 microbial taxa were identified, which were further classified by sparse-CCA into six hubs of molecular pathways associated with specific bacterial groups (FDR<0.05) (Fig. 2), findings we could partially validate in an independent cohort. An increased abundance of Bifidobacterium was associated with higher expression of genes involved in fatty acid metabolism, while Bacteroides was associated with increased metallothionein signaling. Fibrostenotic CD was characterized by a transcriptional network dominated by immunoregulatory genes associated with Lachnoclostridium bacteria in non-stenotic tissue (Fig. 3). In patients using TNF-α-antagonists, a transcriptional network dominated by fatty acid metabolism genes associated with Ruminococcaceae. Mucosal microbiota composition was associated with enrichment of distinct intestinal cell types, particularly intestinal epithelial cells, macrophages, and NK-cells (Fig. 4).

Conclusion

This study is the largest of its kind demonstrating the diversity and versatility of host-microbe interactions in IBD. Furthermore, it highlights the strong effects of patient traits on these interactions, providing important pathophysiological insights. Overall, we identify multiple host–microbe interactions that may guide microbiota-directed personalized medicine in IBD.