DOP46 Mucosal host-microbe interactions associate with clinical phenotypes in Inflammatory Bowel Disease
Bibliographic record
Abstract
Abstract 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".