Metaproteomics reveals diet-induced changes in gut microbiome function according to Crohn’s disease location
Bibliographic record
Abstract
BACKGROUND: Crohn's disease (CD) is characterized by chronic intestinal inflammation. Diet is a key modifiable factor influencing the gut microbiome (GM) and a risk factor for CD. However, the impact of diet modulation on GM function in CD patients is understudied. Herein, we evaluated the effect of a high-fiber, low-fat diet (the Mi-IBD diet) on GM function in CD patients. All participants were instructed to follow the Mi-IBD diet for 8 weeks. One group of CD patients received one-time diet counseling only (Gr1); catered food was supplied for the other three groups, including CD patients (Gr2) and dyads of CD patients and healthy household controls (HHCs) residing within the same household (Gr3-HHC dyads). Stool samples were collected at baseline, week 8, and week 36, and analyzed by liquid chromatography-tandem mass spectrometry. RESULTS: At baseline, the metaproteomic profiles of CD patients and HHCs differed. The Mi-IBD diet significantly increased carbohydrate and iron transport and metabolism. The predicted microbial composition underlying the metaproteomic changes differed between patients with ileal only disease (ICD) or colonic involvement: ICD was characterized by decreased Faecalibacterium abundance. Even on the Mi-IBD diet, the CD patient metaproteome displayed significant underrepresentation of carbohydrate and purine/pyrimidine synthesis pathways compared to that of HHCs. Human immune-related proteins were upregulated in CD patients compared to HHCs. CONCLUSIONS: The Mi-IBD diet changed the microbial function of CD patients and enhanced carbohydrate metabolism. Our metaproteomic results highlight functional differences in the microbiome according to disease location. Notably, our dietary intervention yielded the most benefit for CD patients with colonic involvement compared to ileal-only disease. Video Abstract.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".