Microbiome Heterogeneity Characterizing Intestinal Tissue and Inflammatory Bowel Disease Phenotype
Bibliographic record
Abstract
Inflammatory bowel disease has been associated with differential abundance of numerous organisms when compared to healthy controls (HCs); however, few studies have investigated variability in the microbiome across intestinal locations and how this variability might be related to disease location and phenotype. In this study, we have analyzed the microbiome of a large cohort of individuals recruited at Mount Sinai Hospital in Toronto, Canada. Biopsies were taken from subjects with Crohn's disease, ulcerative colitis, and HC, and also individuals having undergone ileal pouch-anal anastomosis for treatment of ulcerative colitis or familial adenomatous polyposis. Microbial 16S rRNA was sequenced using the Illumina MiSeq platform. We observed a great deal of variability in the microbiome characterizing different sampling locations. Samples from pouch and afferent limb were comparable in microbial composition. When comparing sigmoid and terminal ileum samples, more differences were observed. The greatest number of differentially abundant microbes was observed when comparing either pouch or afferent limb samples to sigmoid or terminal ileum. Despite these differences, we were able to observe modest microbial variability between inflammatory bowel disease phenotypes and HCs, even when controlling for sampling location and additional experimental factors. Most detected associations were observed between HCs and Crohn's disease, with decreases in specific genera in the families Ruminococcaceae and Lachnospiraceae characterizing tissue samples from individuals with Crohn's disease. This study highlights important considerations when analyzing the composition of the microbiome and also provides useful insight into differences in the microbiome characterizing these seemingly related phenotypes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".