P073 Changes in gut microbial signatures differentiate behavioural Crohn’s disease phenotypes; an in silico approach
Bibliographic record
Abstract
Crohn’s disease (CD) is an inflammatory bowel disease (IBD) of idiopathic nature with a heavy burden on the quality of patient life. In 2005 the Montreal World Congress of Gastroenterology introduced the behavioural B1 non‐stricturing non‐penetrating, B2 stricturing and B3 penetrating phenotypes. Today these phenotypes have been properly examined and validated clinically via their respective observations of inflammation, endoscopic fibrostenosis and fistulas. The differential behaviour of CD, is still under investigation with several research teams focusing on different genetic, serological and histological cofounding factors. This work studies the role of the human gut microbiome and its correlation with these specific phenotypes. In recent years microbiota dysbiosis has been linked to IBD. Many researchers have studied the quantifiable and qualitative microbiota population and metabolic changes in CD but have not focused on the behavioural phenotypes. A metagenomic in silico pipeline, consisting of diversity, taxonomic, biomarker, and microbial gene analyses was implemented using the QIIME, Calypso, LEfSe, PICRUSt, and STAMP tools. The pipeline detects changes in microbial 16s rRNA sequencing within the three behavioural phenotypes of CD (B1 non‐stricturing non‐penetrating, B2 stricturing and B3 penetrating) vs. healthy patient samples. The data for our investigations were obtained via publicly available datasets from the QIITA microbial study management platform. The initial identification of microbial populations, taxonomic and diversity analyses via QIIME and Calypso revealed the known loss of α-Diversity within all CD samples with the added effect of statistically significant reductions in the B2 and B3 sample groups vs. B1. There was no statistical significance between the B2 and B3 samples. β-Diversity analysis pointed to microbial compositions similar in the B2 and B3 samples, different from both B1 and healthy controls. Analysis of the abundance of genera in our groups highlighted populations that exhibit reduced or increased abundances in CD, but additionally statistically significant differences in the B2 and B3 compared with B1 samples. Finally, the same pattern was detected in our microbial gene analysis where certain microbial gene abundances were reduced or increased in CD vs. healthy controls, but the B2 and B3 samples had statistically significant changes vs. the B1 group. Our results reveal unique microbial insights into these three phenotypes. Our findings point to a distinct signature of the microbiome’s behaviour in the CD phenotypes and provide the basis for further investigation of CD from a different perspective.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".