The Gut Microbiome of Pediatric Crohnʼs Disease Patients Differs from Healthy Controls in Genes That Can Influence the Balance Between a Healthy and Dysregulated Immune Response
Bibliographic record
Abstract
BACKGROUND: Exclusive enteral nutrition (EEN) is a first-line therapy in pediatric Crohn's disease (CD) thought to induce remission through changes in the gut microbiome. With microbiome assessment largely focused on microbial taxonomy and diversity, it remains unclear to what extent EEN induces functional changes that thereby contribute to its therapeutic effect. METHODS: Fecal samples were collected from 15 pediatric CD patients prior to and after EEN treatment, as well as from 5 healthy controls. Metagenomic data were obtained via next-generation sequencing, and nonhuman reads were mapped to KEGG pathways, where possible. Pathway abundance was compared between CD patients and controls, and between CD patients that sustained remission (SR) and those that did not sustain remission (NSR). RESULTS: Of 132 KEGG pathways identified, 8 pathways differed significantly between baseline CD patients and controls. Examination of these eight pathways showed SR patients had greater similarity to controls than NSR patients in all cases. Pathways fell into one of three groups: 1) no prior connection to IBD, 2) previously reported connection to IBD, and 3) known roles in innate immunity and immunoregulation. CONCLUSIONS: The microbiota of CD patients and controls represent alternative ecological states that have broad differences in functional capabilities, including xenobiotic and environmental pollutant degradation, succinate metavolism, and bacterial HtpG, all of which can affect barrier integrity and immune regulation. Moreover, our finding that SR patients were more similar to healthy controls suggests that community microbial function, as inferred from fecal microbiomes, could serve as a valuable diagnostic tool.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".