DOP26 Metagenomic and metabolomic profiles in IBD: understanding microbial and metabolic shifts from a large deeply phenotyped cohort
Bibliographic record
Abstract
Abstract Background Alterations in gut microbiota composition and functions are involved in the pathogenesis of Inflammatory Bowel Disease (IBD) and the role of specific bacterial taxa has been particularly pointed out. The role of microbiota-derived metabolites, including those produced from tryptophan, are major actors in host-microbiota interactions in health and in IBD. Large studies analyzing both gut microbiota and metabolomics data are scarce. Methods In the current study, we analyzed a total of 764 individuals from Saint Antoine Hospital cohort, including 447 patients with Crohn's disease (CD), 262 patients with Ulcerative Colitis (UC) and 55 healthy subjects. We performed shotgun metagenomic sequencing on fecal samples and integrated the results with deep clinical phenotyping and targeted metabolomics data encompassing 294 different molecules. Results We observed strong changes in the taxonomic composition and functional capabilities of the microbiota in CD and UC patients compared to healthy subjects. Besides disease itself, the most important drivers of microbiota composition were the disease location (Montreal classification), recent antibiotic treatment, disease activity (flare vs remission) and history of ileocecal resection. Interestingly, IBD diagnosis explained much more the variations of microbiota functions than taxonomy. The decrease in microbiota diversity was stronger in CD than in UC. In parallel to a decreased amount of Faecalibacterium in IBD, we also observed a decrease in the diversity of Faecalibacterium strains, with a stronger decrease in CD. Our multifactorial analysis revealed specific microbial taxa and functions affected by disease-related factors. We particularly identified many correlations between tryptophan metabolites and microbial abundance. Targeted gene analysis of tryptophan-related enzymes in metagenomes further supported these findings. A network analysis considering bacterial taxa and metabolites revealed profound alterations in IBD with some specificities between CD and UC. Conclusion We pointed out new microbiome and metabolome alterations associated with IBD, with some phenotype specificities. Overall, our findings provide crucial information and a substantial resource for understanding the interactions between the host and microbiome in the context of 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".