Fecal Microbial Transplant After Ileocolic Resection Reduces Ileitis but Restores Colitis in IL-10−/− Mice
Bibliographic record
Abstract
BACKGROUND: Ileocolic resection (ICR) is frequently performed for Crohn's disease; however, disease commonly recurs early in the neoterminal ileum. The aim of this study was to use the IL-10(-/-) mouse to determine the effects of ICR on gut microbiome and immune function and if postoperative fecal microbial transplant (FMT) would improve disease. METHODS: ICR was performed in 129S1/SvlmJ IL10(-/-) mice followed by FMT using stool from wild-type mice. Sham-transplant mice received their own stool. Stool samples were collected on day 0, day 13 (after ICR), and day 27 (after FMT) for whole metagenome shot-gun sequencing. Mucosal-associated bacteria were quantified with quantitative PCR and visualized by fluorescent in situ hybridization. Tissue cytokines were measured with multiplex arrays and mononuclear phagocyte populations by flow cytometry. RESULTS: Surgery induced microbial functional and taxonomic shifts, decreased diversity, and depleted Bacteroidia and Clostridia. ICR mice had reduced colitis but worse ileitis with bacterial overgrowth, increased translocation, and reduction in tissue macrophages. FMT prevented ileitis but restored colitis and allowed for a bloom of γ-proteobacteria. In the colon, ICR and sham transplant were associated with recruitment of tolerogenic dendritic cells, whereas FMT shifted these immune cell subsets to control profiles along with increasing cytokine levels. CONCLUSIONS: This study suggests that surgical-induced immune dysfunction and microbial dysbiosis with impaired clearance may be the underlying cause of the early ulcerations found in the ileum of patients with Crohn's disease after ICR. FMT has an immunostimulatory effect on the postoperative intestine, which was beneficial in preventing ileitis, but detrimental in restoring colonic injury after surgery.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".